Posts by "Peter Claridge"

Reviewed: The best AI prompting tools (2026 update)

If you’ve ever typed something into ChatGPT and gotten back a response that was… fine, but not quite what you needed, you’re not alone.

The problem usually isn’t the AI. It’s the prompt.

The way you phrase your request makes an enormous difference in the quality of what you get back, and most of us are writing prompts that are too vague, too short, and missing context that would really help the model understand what we want.

That’s exactly what AI prompt rewriter tools are designed to solve. Instead of learning the art of prompt engineering yourself, you type in your rough idea, and the tool rewrites it into something clearer, more structured, and more likely to give you the output you’re after.

To put these tools through their paces, I used one real-world prompt that professionals like you might actually use. Here it is:

“Attached is the transcript from a customer call I did. Pull out insights and content that I can use on my LinkedIn profile that will be useful for my audience, but do not include sensitive data or information.”

It’s a reasonable prompt. But is it optimized?

I ran it through six different AI prompt rewriter tools to find out. I’ve included screenshots of the output from each tool so you can see exactly what they produced.

Here’s a quick overview of how each AI prompt tool stacks up against each other. As you can see, there is very little in terms of features, so the real differentiator is the quality of the re-written prompt.

Tool Chrome Extension Free Tier Works Inside AI Chat Refine/Clarify Mode Prompt Library Starting Price
Pretty Prompt ✅ (10/week) $5.99/mo
PromptPerfect ✅ (10 req/day) $19.99/mo
Promptimize AI ✅ (10/day) $12/mo
Velocity ✅ (5/day) $7/mo
Promptly Free
Prompt Genie $10/mo

Comparison based on testing in February 2026.

Who Actually Needs an AI Prompt Rewriter Tool?

This is a fair question to ask before spending money on anything.

If you use AI conversationally, asking questions, refining answers back and forth, adjusting on the fly, you may genuinely not need a prompt rewriter at all. The back-and-forth nature of AI chat handles a lot of the work for you. If the first response misses the mark, you adjust, and the model course-corrects.

But not everyone uses AI that way.

And for certain types of users, a poorly structured prompt doesn’t just produce a mediocre first response. It produces the wrong output entirely, and iterating your way to the right answer can be slow, inconsistent, and sometimes impossible.

So who actually benefits most from these tools?

1. People who run the same prompt repeatedly

If you’re extracting insights from customer calls every week, generating social media content from the same kind of input on a regular cadence, or producing reports from a consistent data format, a well-optimized prompt pays compound dividends.

You invest the effort once in getting the prompt right, and every future use inherits that quality. An unoptimized prompt run fifty times produces fifty mediocre outputs. An optimized one produces fifty good ones.

2. People where the first response has to be right

If you’re using AI to draft communications, produce client-facing content, or generate outputs that go directly into a workflow without a round of human review, there’s no room for the back-and-forth refinement that casual users rely on. The prompt has to do more work upfront.

3. Teams sharing prompts across multiple people

One person who’s excellent at prompt writing can create an optimized prompt in a tool like Pretty Prompt, save it to the shared prompt library, and the whole team benefits. Without that infrastructure, prompt quality becomes inconsistently dependent on whoever happens to be running the task that day.

4. People new to AI who don’t yet know what a good prompt looks like

This is probably the largest category. Many AI users don’t know why their outputs are mediocre. They blame the AI when the issue is the prompt. A rewriter tool teaches by example: you see what a structured, context-rich prompt looks like and you start to internalise those patterns over time.

5. Developers and power users working with non-conversational AI tasks

Batch processing, API calls, automated workflows, and agentic AI tasks often require single, comprehensive prompts rather than conversational back-and-forth. Getting these right matters a lot, and the science actually backs this up, as we’ll explore in the next section.

If you’re none of the above, the tools in this review might be interesting but not essential. But if you recognise yourself in any of those categories, read on.

The Science Behind Why Your Prompts Matter More Than You Think

Here’s something that might reshape how you think about AI interactions.

According to research from sqmagazine.co.uk on prompt engineering statistics, structured prompt processes can reduce AI errors by up to 76%. If that stat is even half right, tools that help you write better prompts are paying serious dividends.

Two recent studies, neither of which is abstract theory, point to specific structural reasons why a well-constructed single prompt consistently outperforms the conversational back-and-forth approach most of us default to.

Microsoft and Salesforce Joint AI study

A joint study by Microsoft Research and Salesforce analyzed over 200,000 AI conversations across the most advanced models available, including GPT-4.1, Gemini 2.5 Pro, and Claude 3.7 Sonnet.

The finding was stark: AI models achieve around 90% success on single, well-structured prompts. That same success rate drops to approximately 65% in multi-turn conversations, while unreliability increases by 112%.

Why?

The study found that AI models tend to generate preliminary answers before they have all the information. Once the model commits to an early interpretation of a problem, it tends to anchor on that interpretation even as you add corrections and clarifications later.

In the researchers’ words, when an LLM “takes a wrong turn in a conversation, it gets lost and does not recover.” The back-and-forth you’re relying on to refine the output may actually be compounding the problem rather than solving it.

The practical implication is significant: for anything that matters, front-loading your prompt with as much context, constraint, and specificity as possible produces better results than refining conversationally. That’s exactly what a good prompt rewriter helps you do.

Source: Microsoft Research & Salesforce study of 200,000+ AI conversations (2025). Models tested include GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet.

Google Research AI Prompt Study

The second study, published by Google Research in December 2025, found something almost absurdly simple: repeating your prompt twice, literally copy-pasting it so the model sees it twice in a row, consistently improves accuracy across models including Gemini, GPT-4o, Claude, and DeepSeek.

On certain retrieval and extraction tasks, accuracy jumped from the 20% range to above 90%. No added context, no rewording, just repetition.

The explanation lies in how transformer models process text: left to right, without the ability to look ahead.

Repeating the input means every part of the prompt gets another pass through the model’s attention mechanism before it starts generating the answer. The authors noted this could become a default behavior in future AI systems, with inference engines silently doubling prompts before sending them to the model.

Source: Google Research, December 2025 (arxiv.org/abs/2512.14982). Accuracy on retrieval and extraction tasks before and after prompt repetition.

Both studies point to the same underlying truth: the quality of your prompt is not a soft, stylistic concern. It has a measurable, significant impact on the accuracy of what you get back.

Tools that help you structure prompts better, and front-load them with the right context, are working with the architecture of these models rather than against it.

What Is an AI Prompt Rewriter Tool (and Why Does It Matter)?

Before we dive into the reviews, it’s worth being clear on what we’re actually testing here. An AI prompt rewriter tool takes your existing prompt and USES AI to rewrite it to be clearer, more specific, better structured, and more likely to produce the output you actually want.

This is different from a prompt library, which is just a collection of pre-written prompts you can browse and copy. Prompt libraries can be useful, but they don’t help you improve your own thinking or tailor a prompt to your specific situation. A prompt rewriter does both.

The tools in this review all sit firmly in the “rewriter” category. Each one takes your input, analyzes it, and spits out an improved version. What varies is how they do it, how much control you have over the output, and where they fit into your existing workflow.

Most of the tools here work either as a browser-based web app, a Chrome extension, or both. That distinction matters a lot in practice, because the best prompt is one you’ll actually use, and having to switch tabs to improve a prompt is a friction point many people won’t bother with.

browser ai prompt writer and a chrome extension prompt writer

The Test Prompt: Why I Chose It

I wanted to pick a prompt that felt genuinely professional and practical, the kind of thing someone using AI for work would actually type. Pulling insights from a customer call to use on LinkedIn is a real use case that consultants, coaches, freelancers, and sales professionals might encounter regularly.

It also has some interesting complexity baked in.

The prompt needs to balance three things:

  • Extraction (pull out insights)
  • Content repurposing (make it usable for LinkedIn)
  • A privacy constraint (no sensitive data).

That’s a multi-objective prompt, and how each tool handles that nuance tells you a lot about its approach.

A simple prompt rewriter might just add more words. A smart one will recognize that there are multiple goals here and structure the output accordingly.

Here’s what I was looking for in the rewritten versions:

  • Clear role or persona assignment (e.g., “act as a LinkedIn content strategist”)
  • Explicit handling of the privacy constraint
  • Specificity about the format of the LinkedIn content (posts, bullet points, key themes)
  • Context about the intended audience
  • A logical structure that any AI would find easy to follow

Let’s see how each tool performed.

1. Pretty Prompt – The Best All-Rounder

Website: pretty-prompt.com Chrome Extension: Yes Free tier: Yes Pricing: $5.99/mo

Pretty Prompt is the tool I’d recommend most confidently to anyone who uses AI regularly for professional work. It works directly inside ChatGPT, Claude, and Gemini without requiring you to switch tabs, and it genuinely understands what you’re trying to achieve, not just what you typed.

When I ran the customer call transcript prompt through Pretty Prompt, the output was noticeably more structured and professional. It added a clear role instruction, broke the task into distinct steps, specified what kinds of LinkedIn content to produce (key themes, post angles, thought leadership takeaways), and explicitly flagged the privacy requirement as a constraint the AI should check at each step.

That last part was impressive. It didn’t just tack “don’t include sensitive data” onto the end as an afterthought. It embedded it into the logic of the prompt.

Pretty Prompt AI Prompt generator

The tool has two main modes. The first is a one-click “Improve” that restructures your prompt quickly. The second is a “Refine” mode that asks you a few clarifying questions before rewriting, similar to a junior editor checking they’ve understood the brief before drafting. If you have a few seconds to spare, the Refine mode consistently produces better output.

refine a prompt to improve it

The Chrome extension adds a small button directly inside your AI chat interface, so the workflow is genuinely seamless. You type your rough prompt, click the Pretty Prompt button, and the improved version replaces what you typed. No copy-pasting, no tab switching.

chrome extension AI prompt

It also includes a Prompt Library so you can save your best prompts and reuse them, and a Context feature that stores your personal background information and automatically adds it to every prompt you improve. For someone who uses AI daily for a specific type of work, that context feature alone is worth the price.

Users on Product Hunt describe it as becoming “part of muscle memory” and note that it “turns rough thoughts into clear, well-structured prompts in seconds.” That lines up with my own experience. The UI is not flashy, but it’s reliable and it integrates into your workflow rather than interrupting it.

Best for: Professionals who use AI daily and want seamless prompt improvement without leaving their chat interface.

2. PromptPerfect – Best for Multi-Model Use

Website: promptperfect.jina.ai Chrome Extension: No (web app only) Free tier: Yes (limited) Pricing: From $20/month

PromptPerfect, developed by Jina AI, takes a more technical approach to prompt optimization. Where Pretty Prompt is designed for everyday professionals, PromptPerfect feels built for people who want more control over how their prompts are optimized and for which model specifically.

When you paste a prompt into PromptPerfect, it asks you to select the model you’re optimizing for: GPT-4, ChatGPT, Midjourney, Stable Diffusion, and others. That model-specific optimization is its standout feature. A prompt for a creative image generation model should be structured differently from one for a text-based assistant, and PromptPerfect handles that distinction well.

For my LinkedIn/customer call prompt, PromptPerfect produced a solid rewrite. It added role context and broke the task into clear stages. The output was somewhat more formal and technical-feeling than what Pretty Prompt produced, which may or may not suit your style. It also generated two variations by default, giving you options to compare.

The main drawbacks are the lack of a Chrome extension (you need to visit the web app separately) and the subscription pricing, which is more expensive than the lifetime deals available on some of its competitors. For developers or technical users who want deep control, it’s a great option. For the average professional, the workflow friction of switching tabs may outweigh the benefits.

Best for: Developers, prompt engineers, and technical users who optimize prompts for multiple AI models.

3. Promptimize AI – Best Chrome Extension for Inline Editing

Website: promptimizeai.com Chrome Extension: Yes Free tier: Yes (limited) Pricing: $12/mo

Promptimize AI positions itself as “Grammarly for prompt building,” and that framing is actually pretty accurate. It sits inside your browser and enhances prompts directly in the text box of whatever AI tool you’re using, including ChatGPT, Claude, and Gemini.

It took me a few minutes to figure out that there isn’t a input field for entering your prompt like in other AI prompting tools, but it’s a button that sits in your chat box in ChatGPT, Claude, or Gemini. I was hunting around their UI wondering why I couldn’t find the input field, and eventually realized that it was the little blue icon in the chat box.

You have to enter your prompt and then click the blue Promptimize button and it’ll rewrite it for you directly in the chat, which I thought was pretty cool, and made it much more seamless because there’s no copying and pasting.

promptimize chrome extension

For the LinkedIn prompt test, Promptimize produced a cleaner, more directive version that specified the output format better than the original. It added useful structure, though it handled the privacy constraint less elegantly than Pretty Prompt, essentially repeating the instruction rather than embedding it into the task logic.

What Promptimize does well is speed. If you want a quick upgrade without thinking too much about the process, it delivers. If you want more nuance or control, you’ll feel limited.

Best for: Users who want fast, frictionless prompt improvement directly in their AI chat interface without configuration.

4. Velocity – Best Free Option

Website: thinkvelocity.in Chrome Extension: Yes Free tier: Yes Pricing: $7/mo

Velocity is a free Chrome extension that rewrites prompts using a range of techniques including role-playing, context enhancement, and structured formatting. It works across ChatGPT, Claude, Gemini, and Perplexity with a single click.

For a free tool, it punches above its weight. When I ran the LinkedIn/customer call prompt through Velocity, the output added a clear role (LinkedIn content expert), specified the format of the deliverables, and structured the task sensibly. It didn’t handle the privacy constraint as elegantly as other tools, but the core improvement was solid.

velocity browser prompt rewriter

Once generated, you have the option to refine the prompt in different ways.

refine prompt in velocity

And that rewrites it to the final prompt below.

velocity ai prompt rewriter

The free Basic tier gives you 5 daily enhancement attempts and 3 memory slots, which is enough to see whether the tool works for you.

The Pro plan at $7/month unlocks unlimited enhancements, personalised prompts, unified AI memory across sessions, and Pro workflow modes.

At that price point it’s one of the better value paid tiers in this comparison. The Teams plan is available for organisations and priced on request.

If you’re just getting started with prompt improvement or want to test the concept before paying for anything, Velocity is the obvious starting point.

Best for: Users who want free, instant prompt improvement with no commitment.

5. Promptly – Best completely free tool

Website: promptly.fyi Chrome Extension: Yes Free tier: Yes Pricing: Free

Promptly distinguishes itself with a clever feature: you can highlight any text on any website and use a keyboard shortcut (Ctrl+M or Cmd+M on Mac) to instantly optimize it as a prompt. That’s genuinely useful when you’re reading something online and want to use it as the basis for an AI query without copy-pasting into a separate tool.

For my LinkedIn test prompt, Promptly produced a well-structured rewrite that clearly separated the extraction task from the content repurposing task. The privacy constraint was noted but not deeply integrated into the logic. The output was clean and usable.

promptly fyi chrome extension

The interface is simple and the extension is lightweight. Where Promptly falls slightly short compared to top-tier options is in the depth of the rewrite. It improves the structure of your prompt without always improving the specificity. You’re less likely to get the granular detail that turns a good prompt into a great one.

Best for: Users who want prompt improvement across multiple websites and platforms, not just inside AI chat tools.

6. Prompt Genie – Best for Single-Prompt Power Boosts

Website: prompt-genie.com Chrome Extension: Yes Free tier: Limited Pricing: $10/mo

Prompt Genie takes your rough idea and transforms it into what it calls a “Super Prompt”: a detailed, structured, role-assigned prompt ready to send to an AI. It works inside ChatGPT, Notion AI, and other AI text boxes without requiring a separate dashboard.

For the LinkedIn/customer call test, Prompt Genie produced a noticeably expanded prompt. It added context about the LinkedIn audience, structured the extraction into categories (pain points, key learnings, content angles), and included a format instruction for the output. The privacy constraint was retained but, again, not embedded into the task logic as deeply as Pretty Prompt managed.

prompt genie ai prompt writer

What makes Prompt Genie useful is the sheer thoroughness of its rewrites. You tend to get a lot more prompt than you started with, which is mostly a good thing. Occasionally it overshoots and the output becomes longer than it needs to be, but for complex tasks that benefit from detailed instructions, that tendency works in your favour.

prompt genie refine prompt

Prompt Genie starts at $10/month for the Professional plan, which includes unlimited Super Prompts, unlimited Prompt Library, multi-model testing, and video prompt generation.

The Team plan at $40/month adds shared prompt libraries, API access for automation, role-based access, and version control with history, which makes it one of the stronger options for teams managing AI workflows at scale.

The Enterprise tier at $60/month extends that to 10 members and adds advanced analytics and custom integrations.

Best for: Users who want comprehensive, detailed prompt rewrites for complex, multi-step tasks.

How Much Better Do These Tools Actually Make Your Prompts?

Every tool in this review improved the original test prompt in some meaningful way. The unoptimized version was functional but vague. Every rewritten version was clearer, more structured, and would realistically produce better AI output.

The range of quality in the rewrites, though, was significant. The best rewrites (Pretty Prompt, Prompt Genie) embedded the privacy constraint into the task logic, specified the format of the LinkedIn content, and assigned a clear role and audience. The weaker rewrites added structure and removed ambiguity, but treated the privacy requirement as a simple reminder rather than a task parameter to be enforced throughout.

For a prompt like this one where the nuance matters, those differences in quality translate directly into the output you’d get from the AI. A prompt that tells the model to “be mindful of sensitive data” will produce a different (and generally less reliable) result than one that says “after extracting each insight, check whether it references client-specific information such as company names, project details, or personal identifiers, and remove or anonymise any that do before including them.”

Relative prompt quality scores based on structure, specificity, constraint handling, and role assignment. Personal assessment based on testing in February 2026.

Are AI Prompt Rewriter Tools Worth Paying For?

The free tools in this review, particularly Velocity (5 attempts/day) and Promptimize AI (10/day), prove that you don’t have to spend money to get meaningful prompt improvement. Pretty Prompt also offers 10 improved prompts per week with no credit card required, and you can try it before even creating an account. For casual AI users or those just exploring whether prompt optimization is useful, starting free is the right move.

That said, the paid tiers do offer meaningfully better results and fewer constraints for professional use. The pricing across all six tools is also genuinely reasonable: Pretty Prompt Pro at $9.99/month, Velocity Pro at $7/month, and Promptimize Pro at $12/month are all in the same bracket as a single decent coffee shop lunch. PromptPerfect is the outlier on price, with its most useful tier at $19.99/month and a Pro Max option at $99.99/month that’s really aimed at developers and API users rather than everyday professionals.

If you use AI tools daily for professional work, whether that’s content creation, client communication, research, or analysis, the time savings from better prompts add up quickly. One useful rewrite that saves you 20 minutes of back-and-forth with an AI tool has already paid for a month’s subscription to most of these tools.

The other factor is workflow fit. If having a Chrome extension that works inside your existing AI chat interface is what you need, Pretty Prompt and Promptimize AI are the right choices. If you prefer a dedicated web app with more control over how prompts are optimized and for which model, PromptPerfect is the stronger option.

Will AI Prompt Rewriter Tools Still Exist in Five Years?

This is the uncomfortable question that hangs over every tool in this review, and it deserves some discussion

The concept of a prompt rewriter is, when you strip it back, an AI that rewrites your prompt so you can give it to another AI. That’s a middleman layer. And middleman layers in software tend to get absorbed by the platforms they serve once those platforms become capable enough to do the work themselves.

The Google Research prompt repetition study actually flagged this directly. The authors noted that their finding “could become a default behavior for future systems,” with inference engines silently applying the repetition technique before passing the prompt to the main model. In other words, one of the techniques these tools are built on might simply be automated away inside ChatGPT, Claude, and Gemini themselves.

It’s not hard to imagine how this plays out at a larger scale.

OpenAI, Google, and Anthropic all have commercial incentives to make their models easier to use and more likely to produce good outputs on the first attempt.

In fact, OpenAI has already moved in this direction.

OpenAI’s Prompt Optimizer, available inside the OpenAI Playground, is a chat-based tool that takes your draft prompt and automatically rewrites it based on the company’s internal prompt engineering best practices.

Under the hood, it uses what OpenAI calls a “meta-prompt,” essentially a master prompt trained to improve other prompts, and the company has stated it may integrate more advanced automated techniques in the future.

OpenAI also released its meta-prompt publicly and built the same logic into their GPT-5 prompting guide, which explicitly recommends using the optimizer tool before sending prompts to production. This is a platform, not a third-party tool, doing the same job as the products in this review.

The distance between a developer-facing Playground feature and a consumer-facing button inside the main ChatGPT interface is not a large one.

There are a few scenarios where third-party prompt rewriter tools hold on.

The first is specificity of context.

A general-purpose AI interface doesn’t know whether you’re a sales consultant extracting insights from client calls or a developer writing API documentation. Tools like Pretty Prompt, which let you store personal context and apply it to every prompt you improve, offer something that a one-size-fits-all built-in system can’t easily replicate without much deeper personalisation than these platforms currently offer.

The second is cross-platform use.

If your prompt is being sent to five different AI tools, a third-party rewriter that works across all of them remains useful even if each individual platform improves its own built-in handling. The tools in this review work across ChatGPT, Claude, Gemini, and others simultaneously. A built-in rewriter only helps within its own platform.

The third is the team and template use case.

The ability to build, save, and share optimised prompts across a team, and to enforce consistent quality in how different people are using AI, is a workflow and collaboration problem that AI platforms haven’t prioritised. Tools like Pretty Prompt and Prompt Genie that include libraries and team sharing features are solving a problem that goes beyond just writing a better single prompt.

So tools that offer genuine context storage, cross-platform flexibility, team collaboration, and the ability to save and reuse well-crafted prompts are solving a different, more durable problem.

The best tools in this space are probably best thought of not as prompt rewriters, but as prompt management systems where rewriting is just one feature among several.

Final Verdict: Which AI Prompt Rewriter Tool Should You Use?

For most professionals who use AI tools daily, Pretty Prompt is the top pick. It integrates seamlessly into your existing workflow via Chrome extension, produces the most nuanced rewrites of any tool here, handles complex multi-objective prompts intelligently, and its one-time lifetime deal pricing makes it an easy decision. The Refine mode, which asks clarifying questions before rewriting, is a feature none of the competitors match in terms of output quality.

If you’re a developer, prompt engineer, or someone who works across multiple AI models including image generators, PromptPerfect is the more powerful and flexible option. It’s web-app-only, which adds friction, but the model-specific optimization and API access make it the right choice for technical use cases.

If you want to start for free and see how much prompt improvement actually changes your outputs, Velocity is a genuinely impressive free tool that requires zero commitment.

The bottom line on AI prompt rewriter tools: they work. Whether you’re extracting insights from customer calls, drafting LinkedIn content, or tackling any other AI-assisted task, a better prompt produces a better result, and these tools make writing better prompts faster and easier than doing it from scratch.

Give one a try with your next AI task and compare the output to what you usually get. You’ll notice the difference immediately.

Did I miss your favorite AI prompt writer? Contact me to let me know.

Have you tested any of these AI prompt rewriter tools? I’d love to hear which ones are working best in your workflow.

5 Ways SaaS Companies Can Recover SEO Traffic by Shifting From Broad Information to High-Intent Keywords

If you’re running a SaaS business, chances are your SEO playbook has been built on broad informational keywords: “what is [category],” “how to improve [problem],” “best [X] tools.” That worked when Google was handing out traffic like free beer at a startup party.

Now? Google’s AI Overviews are eating those clicks alive. CTR drops by 30–35% on queries with AI summaries. The low quality traffic is evaporating, and the little that’s left is harder to win.

The good news: not all traffic is created equal. And the traffic you actually want where people are ready to try, buy, compare, or shortlist can still be won.

1. Stop obsessing over ToFu and move down the funnel

Traffic Think Tank and PoweredBySearch both say the same thing: most SaaS companies over-invested in broad “what is” content. Great for impressions, lousy for pipeline.

I should know. I’ve written more “what are” and “what is” articles than I’d like to admit, and most of them now live rent-free inside Google’s AI data soup.

Many years ago, meeting scheduler tools would all compete on queries like “What are meeting scheduling tools” or “What do meeting scheduling tools do?”.

Now? Those clicks are owned by the AI overview, and Google. And the search results that are shown lean more towards actual apps (or list of apps) rather than information blog posts.

Instead, content writers and the strategists that herd them, need to focus on MoFu (solution-oriented) and BoFu (transactional) keywords. Think:

  • “[Your category] software comparison”
  • “Best [category] tool for [specific persona/use case]”
  • “Alternatives to [competitor]”
  • “[Your product] pricing”

You might not be guaranteed a click, but at least you’re only competing with a wall of sponsored ads rather than a robot!

2. Build topic clusters, not one-off articles

Mike Khorev and others recommend clustering: one pillar page on a core category term, supported by multiple specific subpages. This creates semantic depth that both Google and LLMs love.

Example: Instead of one page on “audience engagement tools,” you’d build:

  • Pillar: “Audience Engagement Software: The Complete Guide”
  • Subpages: “Audience Engagement for Webinars,” “Audience Engagement in Microsoft Teams,” “How to Measure Audience Engagement”

SEO tools like Ahrefs or Serpstat can help here by showing which clusters are already being cannibalized by AI Overviews, and where long-tail opportunities remain.

How we used programmatic SEO to build our topic clusters

At StreamAlive, we initially leaned into programmatic SEO because it was faster and easier to execute with a small team. pSEO let us generate lots of pages around engagement formats, platforms, and scenarios without heavy editorial overhead.

For early traction and long-tail coverage, it worked well enough. Pages got indexed quickly, picked up impressions, and captured low-intent searches that would have been hard to justify writing bespoke content for.

The limitation showed up later. Programmatic pages are good at coverage but weak at narrative and authority. As AI Overviews and LLM-driven answers became more dominant, it became clear that clusters built around a strong pillar do a better job of signalling real understanding.

A handful of deeply connected pages that explain the problem, the context, and the trade-offs consistently outperform dozens of thin variants when it comes to trust, synthesis, and brand recall. In hindsight, pSEO was a useful shortcut early on, but topic clusters are the more durable strategy when search shifts from keywords to concepts.

3. Mine your customers for keyword gold

Forget keyword tools for a second. The best SaaS keywords come from sales calls, support tickets, community chatter, and forums. SEOProfy and RockTheRankings both stress this: your customers don’t type like SEOs, they type like people with problems.

Practical step: export a batch of call transcripts or Zendesk tickets, run them through an LLM, and pull out recurring questions. That’s your keyword list.

And yes, tools like Surfer SEO or MarketMuse can still be useful here. It’s not to chase volume, but to make sure the real language from your customers aligns with content that search engines and LLMs can parse.

At eG Innovations, the company’s best-performing keywords were based on what keyword tools told us were relatively high traffic and low competition. Keywords like “high Java cpu” and “high Java memory” and “what is application performance monitoring”.

Those terms used to be SEO gold. Today, many of them trigger AI Overviews or get answered directly in ChatGPT, which means the click is often gone. However, even when the search result is summarized, the brands that show up in those summaries tend to be the ones that historically explained the problem clearly and repeatedly.

4. Balance branded vs non-branded keywords

B2B software categories are seeing ~30% CTR drops in the organic search results as AI overviews eat your clicks. The painful reality, as PR Daily notes, is that only ~16% of B2B search queries now result in actual clicks.

Mailchimp is battling this by optimising their site to better serve AI “crawlers” by prioritizing technical performance (fast load times, machine-readable markup) because these elements matter more for LLM ingestion than for traditional human-first SEO.

Elsewhere, ecommerce sites are reporting 40% CTR drops on product terms buyers use to find products according to PassionFruit. Maybe thank your lucky stars you’re not an ecommerce owner right now?

But is on-site SEO enough?

Quoleady and Victorious argue SaaS companies underestimate the power of branded search. If buyers are already using AI or communities to narrow their options, you want them typing your name into Google.

That means investing in:

  • Brand-building campaigns
  • Customer advocacy and reviews
  • Consistent naming in communities and roundups

The boring truth: when demand capture shrinks, demand creation matters more.

Capturing demand is measurable. Creating demand is uncomfortable.

When I was at eG Innovations, SEO and paid search were almost entirely about capturing existing demand.

Keywords, comparison pages, and bottom-of-funnel content won because they could be tied directly to pipeline. Demand generation was treated with suspicion because it couldn’t be tied to new leads.

We tested digital PR and brand activity, but when spending $2,500/mo didn’t produce leads inside the reporting window, the budget was pulled.

The CFO wasn’t wrong from a spreadsheet perspective. The company’s priority was profitability, and profitability demanded revenue now. Longer time horizons, softer metrics, and indirect effects like lower CAC or shorter sales cycles were not acceptable trade-offs.

At StreamAlive, the approach is almost the opposite. Community building, content, and brand come first, even though most of it can’t be cleanly attributed to leads.

Newsletters, education, weekly workshops, community, product-led sharing, and opinionated content create familiarity long before purchase intent exists.

The difference shows up clearly in brand search demand.

eG is a 25-year-old company with 250 employees and roughly $30m in revenue, yet brand search is modest relative to its size. StreamAlive, with three years in market and a ten-person team, generates disproportionate branded search interest. That’s the compound effect of creating demand early, even when it’s uncomfortable to measure.

streamalive vs eg innovations keyword search volume

5. Prepare for Generative Engine Optimization (GEO)

We’re entering the era of Generative Engine Optimization. That means:

  • Write structured, FAQ-style answers
  • Use schema markup where possible
  • Format for clarity (headings, bullets, summaries)
  • Publish in multiple formats (text, video, docs)

Our own experiments at StreamAlive showed that multi-format content (video + blog + docs) increased the odds of being pulled into AI summaries. LLMs need structured, digestible content. Give it to them, and they cite you.

Elsewhere, a Wall Street Journal piece highlights how Back Market (specialising in refurbished tech for consumers) is tweaking product pages with a more conversational tone to better align with how LLMs like ChatGPT parse content. AI referrals are still small (about 0.2%), but growing rapidly (470× since last summer).

How StreamAlive is dominating the search results

StreamAlive experienced a 28% drop in search traffic between March 2025 (when AI Overviews were fully unleashed) and July 2025.

We’ve fought back by creating product pages, blog posts, and use case pages for each of our features. We also doubled down on video.

Every keyword we want to rank for has a corresponding video.

The result has been nothing short of spectacular.

The search results for dozens of keywords that we’re targeting now look like this 👇

And StreamAlive’s use case pages (built using programmatic SEO) are the top ranking pages, beating Microsoft and other larger brands.

What you’re seeing is StreamAlive being prominently featured in the AI overview, including a step by step tutorial on how to use StreamAlive. There are video links for more information.

Impressions have increased significantly as we now get more visibility in the search results, but of course, AI is eating all the clicks so the CTR is down.

Some of the clicks could be going to our YouTube videos where we’re seeing thousands of views on what are otherwise VERY niche (but targeted!) videos.

A quick word on llm.txt

Some SEOs have been hyping a special llm.txt file that supposedly tells AI models which content to ingest. Right now, it is a placebo. OpenAI has said they ignore it, and no major AI providers fetch it. It feels a lot like the old “meta keywords” tag—a nice idea, but functionally useless.

My view: don’t waste cycles fiddling with llm.txt. Invest that energy into structured content, brand building, and clarity. If I’m wrong, fine—I’ll be the guy who wrote more “what is” articles and more llm.txt files than anyone ever asked for.

Wrapping up

SEO isn’t dead. It’s just shedding the vanity traffic that made dashboards look good but pipelines look thin.

If you focus on high-intent keywords, clusters, customer language, brand demand, and AI-ready content, you’ll not only recover visibility—you’ll get better visitors than before.

If you’re staring at falling traffic and wondering how to adapt, let’s talk. Book a clarity call and we’ll figure out how your SaaS can survive (and maybe even grow) in the AI search era.

Reviewed: The best AI video tools for product demos (2026 update)

At StreamAlive, we hit a wall that I suspect a lot of you have hit too. We ship features fast – sometimes multiple meaningful updates in a single week – and for a while, our product video production just couldn’t keep up.

Every new feature deserved a polished demo. Every update deserved a proper walkthrough.

But the gap between “feature shipped” and “video live” kept growing, and we were increasingly relying on Loom screen recordings that, honestly, didn’t do the product justice.

Obviously we didn’t want to slow down shipping new features, nor did we want to hire a video agency at $2,000-$5,000 per video. And we didn’t have time to become video editors ourselves. So we started testing every AI video tool we could find, specifically looking for the best AI video makers for SaaS product videos – tools that could produce something genuinely polished, quickly, without a full production setup.

Here’s an example of what we ended up creating (not a product demo, but fully generated with AI):

The results surprised us. Some tools were far better than their marketing suggested. Others were the opposite. And the pricing – once you dig past the headline numbers – varies wildly in ways that aren’t obvious until you’re already committed to a subscription.

The global AI video generator market is growing at a 32.2% CAGR, projected to reach $42.29 billion by 2033, and it’s easy to see why.

AI-generated product demonstration videos boost conversion rates by 40%, and over 62% of marketers using these tools report cutting content creation time by more than half. But none of that matters if you pick the wrong tool for your specific workflow.

So here’s our honest, hands-on breakdown of the six best options we found – including a deep-dive on pricing, so you can figure out which one is actually worth your money.

Tool Best For Entry Price (Annual) Free Plan Brand Consistency Cross-Scene Coherence Multi-Language
🥇 Ngram SaaS product demos, launch & social videos $17.40/mo ✅ Generous ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐
HeyGen Avatar-led demos, global localization $24/mo ✅ 3 videos/mo ⭐⭐⭐⭐ ⭐⭐⭐ ✅ 175+ languages
Synthesia Enterprise training, multilingual content $18/mo ✅ Basic (limited) ⭐⭐⭐⭐ ⭐⭐⭐ ✅ 140+ languages
Clueso Screen-recording tutorials, SOPs, docs $120/mo ✅ 7-day trial ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ✅ 30+ languages
InVideo AI Social content, faceless videos, text-to-video $20/mo ✅ Limited ⭐⭐⭐ ⭐⭐⭐ ✅ 50+ languages
Synthesia Creator Growing teams, more video volume $64/mo ⭐⭐⭐⭐ ⭐⭐⭐

Pricing verified February 2026. Annual billing rates shown where applicable.

#1 Ngram: The Best All-Round AI Video Maker for SaaS Teams

If you’ve been frustrated watching AI video tools fail to keep characters, visual styles, and branding consistent from one scene to the next, Ngram is the answer you’ve been waiting for. This is the tool that’s cracked one of the hardest problems in AI-generated video: cross-scene consistency.

Check out this Github product explainer example.

Ngram is purpose-built for product marketers, growth teams, sales enablement, and customer success teams at SaaS companies. You’re not fighting a generic tool. You’re using something designed specifically for the kind of content you need to ship: polished product demos, feature announcements, onboarding walkthroughs, changelog videos, and LinkedIn-optimized social clips.

What Makes Ngram Different

The core magic is in how Ngram handles storytelling and brand coherence. Most AI video tools generate scenes independently, which means characters, color palettes, fonts, and visual styles can drift between clips – a jarring experience that instantly signals “AI-generated.” Ngram solves this by generating a narrative storyboard first, planning structure from problem to solution to proof, then building scenes that are visually matched throughout the entire video.

Here’s the workflow: you drop in whatever you have – docs, URLs, screenshots, PDFs, or screen recordings. Ngram organizes that material and asks you who the video is for, what it should accomplish, and where it’s going (LinkedIn, your website, email). From there, it generates the narrative and a scene-by-scene storyboard you can review and steer before anything is rendered. This matters because it gives you creative control without requiring creative skills.

The editing layer is built for speed. AI auto-cuts dead air in recordings, applies smart zoom on click actions, adds callouts and step labels, and wraps the whole thing in your brand kit (logo, colors, fonts, intro/outro) automatically. You get 16:9, 9:16, and 1:1 export formats from one project, which is genuinely useful when you’re repurposing content across platforms.

Real-world use cases Ngram excels at:

  • Turning rough screen recordings into polished product walkthroughs for customer onboarding
  • Creating changelog videos from release notes that people actually watch
  • Building launch videos that tell a “problem, solution, proof” story with motion graphics
  • Cutting long demos into short-form social clips for LinkedIn or Instagram Reels
  • Transforming documentation into step-by-step visual tutorials

Ngram Pricing

This is where Ngram genuinely surprises. It offers a very generous free plan – enough to create real videos and evaluate the platform properly – with paid plans starting at just $17.40 per month. That positions it as one of the most cost-effective options on this list, especially when you factor in what you get: brand kits, multi-format export, AI editing, and that cross-scene consistency that competitors at 5x the price still can’t reliably deliver.

For SaaS teams that need to ship a high volume of product videos without hiring a video agency or an in-house editor, Ngram’s combination of quality and price is exceptional.

Entry-level paid plan pricing (monthly, billed annually). Sources: Official pricing pages, verified February 2026.

#2 HeyGen: Best for Avatar-Led Product Videos and Global Localization

HeyGen has become one of the most well-known names in AI video, and for good reason. Rated 4.8 out of 5 on G2, it was named G2’s #1 Fastest Growing Product of 2025. Over 100,000 businesses use the platform to create AI avatar videos without any camera or crew. For SaaS teams that want a human face on their content – think talking-head explainers, localized demo videos, or sales outreach clips – HeyGen is a powerhouse.

The platform’s avatar library includes 500+ stock presenters, with options across age ranges, ethnicities, and professional styles. The voice cloning feature lets you replicate your own voice, and the video translation system supports 175+ languages with natural lip-syncing. This combination makes HeyGen particularly valuable for SaaS companies with a global user base. You record once, and HeyGen handles the localization.

For SaaS demos, HeyGen’s Interactive Walkthroughs feature is worth noting. You can add clickable elements inside your videos, embed call-to-action integrations, and guide viewers through product features in a structured way. It’s not a screen recording tool – it’s better suited for scripted, avatar-led presentations of your product.

HeyGen Pricing

HeyGen offers a free plan with 3 videos per month, capped at 3 minutes, at 720p with a watermark. It’s genuinely useful for testing avatar quality but falls well short of professional use.

The Creator plan is where most SaaS teams start: $29/month (or $24/month on an annual plan). This unlocks unlimited avatar videos up to 30 minutes long, 1080p export, no watermarks, voice cloning, a brand kit, and access to 175+ languages. It includes 200 Premium Credits per month for advanced features including Avatar IV and 40 minutes of lip-synced video translation.

For teams needing collaboration tools, 4K export, and video commenting, the Business plan runs $149/month base with an additional $20 per seat per month (minimum two seats). A note of caution: adding seats doesn’t increase your Premium Credit or translation minute allocation – the team shares the same monthly pool.

There’s also a Pro API plan for developers at $99/month for 100 credits, scaling up to $330/month for the Scale tier.

What you might not expect: HeyGen’s premium features (Avatar IV, AI-generated looks, certain translation workflows) consume “Premium Credits” on top of your subscription, so budgeting realistically means factoring in these add-on costs if you use advanced features heavily.

#3 Synthesia: Best for Enterprise Training and Multilingual Content at Scale

Synthesia is the most well-funded player in AI avatar video with $536 million raised and a $4 billion valuation. Over 50,000 teams use the platform including, by their own count, 90% of Fortune 100 companies. If enterprise credibility and institutional-grade security matter to your procurement process, Synthesia is the standard-bearer.

The platform’s strength is in structured, scalable text-to-avatar video production. You write a script, pick from 230+ avatar presenters across 140+ languages, and the system generates a polished 1080p video. Synthesia’s 2026 updates have added access to an AI Playground with support for Google’s Veo 3.1 and OpenAI’s Sora 2, available across all paid tiers – a significant technical leap forward. The platform also now supports enhanced PowerPoint-to-video workflows that preserve your original slide design while converting speaker notes into video scripts.

For SaaS teams focused on customer education, employee training, and HR communications, Synthesia’s template library (250+ options) and deep avatar customization make it efficient to produce consistent, branded content at volume. Custom “digital twin” avatars are available on Enterprise plans, though at significant cost (reportedly around $1,000 per year per avatar).

The honest critique of Synthesia is that its avatars, while technically impressive, can feel “corporate” – polished to the point of sterility. This is a platform built for professional presentations, not informal product demos or viral social content. Some users on Capterra have noted inconsistent content moderation and somewhat rigid workflows.

Synthesia Pricing

Synthesia’s pricing is based on annual video minutes rather than a simple monthly credit.

  • Free (Basic) plan: 3 minutes/month, 6 stock avatars, watermarked export – a genuine test drive, not a production tool.
  • Starter plan: $29/month billed monthly, or $18/month billed annually (120 minutes of video per year, 125+ avatars, watermark-free).
  • Creator plan: $89/month billed monthly, or $64/month billed annually (360 minutes of video per year, 90+ AI avatars, custom fonts, audio downloads).
  • Enterprise: Custom pricing with unlimited video minutes, SSO, dedicated CSM, and advanced brand controls.

The minute-based model catches some users off guard. On the Starter plan, 120 minutes annually works out to 10 minutes of video per month – enough for light use but tight if you’re shipping weekly feature updates or onboarding content regularly. Heavy users frequently need to step up to Creator or negotiate Enterprise.

#4 Clueso: Best for Screen-Recording-Based Product Tutorials and SOPs

Clueso occupies a specific and genuinely valuable niche: it’s the tool you want when you’re starting from a screen recording and you want to end up with a polished, branded product tutorial or step-by-step guide, without any manual video editing. It’s rated 4.8 stars on G2 and holds SOC 2 Type II and ISO 27001 certifications – important for enterprise procurement.

The workflow is simple: you upload a screen recording (or create one in Clueso), and the AI handles the rest. It auto-zooms on clicks, removes filler words and dead air, generates AI voiceovers, adds captions, and applies your brand kit. What’s particularly distinctive is that Clueso simultaneously generates written documentation from your video – step-by-step guides, SOPs, and knowledge base articles – saving your team double the work.

This makes it especially powerful for SaaS customer success and support teams. Instead of recording a product tutorial and then separately writing out the steps for your help center, Clueso does both in a single workflow. Its 30+ language support makes localization practical for global teams.

The platform targets SaaS companies specifically, with use cases across customer education, product marketing, learning and development, sales enablement, and IT change management.

Clueso Pricing

Clueso is the most premium-priced option on this list at the entry level, reflecting its enterprise-forward positioning and specialized workflow.

  • Starter plan: $120/month (2 user licenses, 30 minutes of video exports per month or 6 hours annually, 30 articles per month).
  • Growth plan: $200/month (4 user licenses, 1 hour of video exports per month or 12 hours annually, 60 articles per month).
  • Enterprise: Custom pricing with voice cloning, translation glossary, advanced SSO, dedicated CSM, and priority Slack/Teams support.

A 7-day free trial is available with no credit card required, giving you access to all features and up to 15 minutes of video export to evaluate the platform properly.

The pricing may feel steep compared to general-purpose video tools, but the comparison isn’t quite fair. Clueso replaces both your video production tool and your documentation tool simultaneously. For a SaaS team paying a technical writer to convert video tutorials into help center articles, the ROI calculation changes significantly.

#5 InVideo AI: Best Value for High-Volume Faceless Video Content

InVideo AI has taken a significant leap forward following its October 2025 partnerships with OpenAI (for Sora 2 integration) and Google (for VEO 3.1 access). It’s now the only platform that offers integrated access to both of those leading video generation models within a single workflow – and it does so at a price that undercuts standalone access to either by a wide margin.

With over 50 million users generating approximately 8 million videos monthly, InVideo is a proven platform at scale. The “Magic Box” editing approach is particularly clever: you edit videos using plain text commands rather than a timeline, which keeps the experience accessible for non-editors. The platform also integrates over 16 million royalty-free stock assets from iStock, Storyblocks, and Shutterstock, which it selects automatically to match your script.

For SaaS teams, InVideo is most useful for faceless video content – explainers, feature highlight videos for social media, promotional clips for landing pages – where you don’t need an avatar or screen recording. The VEO 3.1 integration specifically solves a longstanding AI video problem: character consistency across multiple scenes. If you need a character to look the same in shot 3 as in shot 1, VEO 3.1’s frame referencing capability handles it.

Is InVideo perfect for detailed product walkthroughs? Probably not – that’s Clueso or Ngram’s territory. But for generating a high volume of branded social content, promotional videos, and text-driven explainers quickly, InVideo delivers exceptional value.

InVideo AI Pricing

InVideo uses a credit-based system across four tiers:

  • Free plan: 10 minutes/week of AI generation, 4 watermarked exports per week. Enough to test, not enough for professional use.
  • Plus plan: $28/month (or $20/month annually) – 50 minutes of AI generation per month, unlimited watermark-free exports, 80 iStock assets/month, 100 GB storage, 2 voice clones, 1080p resolution.
  • Max plan: ~$50/month – 200 minutes/month, 320 iStock assets, 400 GB storage, 5 voice clones.
  • Generative plan: ~$96/month – Higher generation limits, 15 minutes of generative credits, ideal for UGC ads and long-form generative content.
  • Team plan: Custom pricing, supports up to 50 seats with pro avatars and expanded collaboration.

Annual plans save up to 20% compared to monthly billing. The Plus plan at $20/month annually is genuinely hard to beat for solo creators or small SaaS marketing teams that need a steady stream of video content without breaking the bank.

#6 A Note on the Broader AI Video Landscape

Beyond the five tools covered in depth, it’s worth understanding the broader context. According to Fortune Business Insights, the global AI video generator market is projected to grow from $847 million in 2026 to $3.35 billion by 2034. This growth is attracting a wave of competitors, and the tools available today are already dramatically better than what existed just 18 months ago.

A few adjacent tools worth knowing about for specific SaaS use cases:

  • Loom – Still the fastest way to record and share async video updates, though it lacks the AI production quality of the tools in this guide.
  • Camtasia – The classic screen recording tool, now subscription-only since January 2025. Excellent editorial control, but manual and time-consuming compared to AI-native alternatives.
  • Descript – Strong for podcast-to-video workflows and editing via text transcript, but less specialized for product demo production.

Which AI Video Tool Is Right for Your SaaS Team?

Here’s a practical decision guide based on what your team actually needs:

Choose Ngram if: You need to create a wide variety of professional product videos – launches, onboarding, demos, social clips, feature announcements – and you want brand-consistent, visually coherent output across all of them at an affordable price. It’s the most versatile tool for SaaS product marketers, and its pricing at $17.40/month is a standout value. Start here if you’re unsure.

Choose HeyGen if: You want a human avatar presenter in your videos – for talking-head product explainers, sales outreach clips, or multilingual demos where lip-synced localization matters. The $24/month Creator plan is solid for individuals; teams step up to the Business plan.

Choose Synthesia if: You’re serving an enterprise with compliance requirements, or you need to produce a large volume of training and onboarding videos in many languages, and you need the institutional credibility of a platform trusted by the Fortune 100. The $18/month Starter (annual) is accessible, but serious users typically need Creator at $64/month.

Choose Clueso if: You’re a SaaS customer success or support team that needs to transform screen recordings into both polished video tutorials and written documentation simultaneously. The $120/month price is steep, but it replaces two workflows and eliminates the need for a video editor and a technical writer. Enterprise teams with high documentation volume will find it pays for itself.

Choose InVideo AI if: You need a high volume of text-driven, faceless social videos, promotional clips, or explainers, and you want access to cutting-edge generative AI (Sora 2 + VEO 3.1) at the lowest possible price point. The $20/month Plus plan (annual) is extraordinary value for what it offers.


The Real Cost Comparison: AI Video vs. Traditional Production

Before wrapping up, it’s worth grounding all of this in the alternative. Traditional professional video production from an agency costs between $2,000 and $10,000 per product video, with a production timeline of 4-8 weeks. One quote we got was $1,000 per second. Yikes! Even a lean internal video team – one editor, one motion designer, licensed music and stock footage – runs $80,000-120,000 per year in salaries and tools.

Any of the tools in this guide will pay for themselves in the first week. The real question is which one maps best to your team’s workflow, your volume requirements, and the type of content you need to produce.

Annual cost comparison for producing ~50 product videos per year. Traditional production estimated at industry average.

Conclusion: The Best AI Video Maker for SaaS Depends on Your Use Case

The best AI video makers for SaaS product videos in 2026 are genuinely excellent tools that would have seemed impossible just two years ago. You can produce studio-quality product videos in minutes, at a fraction of the cost of any alternative.

Our top picks by use case:

  • Best overall for SaaS product videos: Ngram – cross-scene consistency, great brand controls, and the lowest entry pricing at $17.40/month make it the clear all-rounder for product marketing teams.
  • Best for avatar-led and multilingual content: HeyGen at $24/month (annual), with 175+ languages and unlimited video on the Creator plan.
  • Best for enterprise training programs: Synthesia – $18/month Starter (annual), backed by $536M in funding and trusted by 90% of the Fortune 100.
  • Best for screen-recording-to-tutorial automation: Clueso at $120/month – premium pricing justified by dual video + documentation output.
  • Best for high-volume text-to-video content: InVideo AI at $20/month (annual) with Sora 2 and VEO 3.1 integration.

The AI video maker landscape is evolving fast. New model releases, pricing changes, and feature additions are happening monthly. Whatever tool you choose today, the time savings over traditional production methods are so significant that the ROI is almost guaranteed from day one.

Not seeing a tool listed here? Get in touch with me!

How to Find Product-Market Fit for B2B SaaS with 14 Examples

This is a very in-depth article with 14 examples of B2B SaaS companies that found, or failed to find, product market fit. It probably should be a book on Kindle.

TL;DR

The Problem: 42% of startups fail because there’s no market need. Most founders don’t know if they have product-market fit or are just getting lucky with early traction.

How to Know If You Have PMF:

  • The 40% Rule: Survey active users: “How would you feel if you could no longer use [product]?” If ≥40% say “very disappointed,” you likely have PMF.
  • Leading indicators: Organic word-of-mouth, small but passionate user base, customers clearly articulating your value
  • Lagging indicators: Double-digit monthly growth ($10K-$50K MRR range), ≤3.5% monthly churn, retention curves flattening

Key Takeaways

1. PMF is measurable, not a feeling. Use the 40% rule (Sean Ellis test) as your leading indicator. If ≥40% of active users would be “very disappointed” without your product, you’re on the right track. Pair this with retention curves, organic growth, and churn rates to confirm.

2. Nail one specific pain point for one specific audience. Don’t build a horizontal product for “everyone.” Clay found PMF when they focused exclusively on GTM teams. Gamma found it serving consultants/founders/educators who need rapid content creation. Specificity beats breadth every time.

3. Expect 2+ years, not 6 months. The median B2B SaaS takes 2 years to find PMF. Clay took 5. Airtable took 3-4. This requires patient capital or low burn rates. Companies that fail usually have infrastructure costs demanding immediate adoption. Don’t scale (team, marketing, features, funding) until PMF is proven—premature scaling kills more startups than bad products.

4. Sometimes you need to completely pivot, and that’s okay. Slack started as a failed game. Segment started as analytics software. Flickr started as a game feature. If after 18-24 months you have no organic pull, customers prefer your side feature, or you’re running out of runway then pivoting might be your path to PMF. The winners aren’t those who never change course; they’re the ones who recognize when the market is telling them something and act decisively.

You’re worried about product market fit for your product

I’m assuming that if you’re reading this then you are a founder or a marketer under enormous pressure to find leads for your sales team.

You’ve built something. Customers are using it. Some even pay you. But here’s the question that keeps you up at night: do you actually have product-market fit?

According to CB Insights, 42% of failed startups cite “no market need” as their reason for shutting down. That’s not a technology problem or an execution problem, it’s a product-market fit problem. They built something nobody desperately wanted.

YCombinator’s famous motto is: Build something people want.

But as the graveyard of failed startups will testify, knowing what people want lies somewhere between witchcraft and blind luck.

This guide will show you exactly how to know if you’ve found PMF, what it looks like for B2B SaaS specifically, how long it takes, and what to do (and not do) along the way.

What Is Product-Market Fit & Why It’s Different for B2B SaaS

Product-market fit (PMF) is that elusive state where your product pulls customers toward it rather than you having to push it on them.

It’s the difference between Clay spending a jaw-dropping five years with ~20 customers and suddenly hitting 10x revenue growth.

It’s the difference between Airtable struggling to explain what they built and becoming an $11 billion company.

It’s the difference between Gamma building a tool to make beautiful presentations and a tool that gets you a presentation ready in 5-minutes.

Marc Andreessen defined product-market fit simply: “being in a good market with a product that can satisfy that market.” But that’s the poet’s version. For B2B SaaS founders, you need something more actionable.

Product-market fit means:

  • Customers actively seek out your solution without heavy marketing
  • They integrate it into their workflows and can’t imagine working without it
  • Word-of-mouth drives a significant portion of new customer acquisition
  • Retention is strong and people stick around
  • Growth feels less like pushing a boulder uphill and more like steering a rocket

B2B SaaS PMF is different from B2C. Consumer products can go viral overnight. B2B SaaS doesn’t (always) work that way. Your market is smaller, sales cycles are longer, and you’re often replacing entrenched workflows or competitors. The bar for “good enough” is much higher because your product needs to integrate into complex business processes and deliver measurable ROI.

Think about it: a consumer might download TikTok on a whim and get immediate dopamine hits. But a company switching project management tools is weeks of evaluation, stakeholder alignment, change management, risk assessment, legal reviews, security audits, and procurement (ugh).

This is why B2B SaaS PMF timelines are measured in years, not months.

The 40% Rule: A Leading Indicator You Can Measure

Sean Ellis, the growth marketer who helped scale Dropbox and LogMeIn, created one of the most actionable PMF tests: the 40% rule.

Here’s how it works. Survey users who’ve experienced your core product value (used it at least twice in the last two weeks) and ask them one simple question:

“How would you feel if you could no longer use [product]?”

Answer options:

  • Very disappointed
  • Somewhat disappointed
  • Not disappointed (it really isn’t that useful)
  • N/A – I no longer use it

According to Ellis’s research across hundreds of startups, if 40% or more of respondents answer “very disappointed,” you likely have product-market fit. Companies that cleared this threshold almost always achieved strong, sustainable growth. Those below 40% struggled.

Source: Sean Ellis PMF Survey Research

How Superhuman Used This to Reach PMF

Rahul Vohra, founder of Superhuman, turned the Sean Ellis test into Superhuman’s most important metric. In summer 2017, their score was 22%—well below the PMF threshold. They:

  1. Segmented users based on survey responses
  2. Focused exclusively on the “very disappointed” users
  3. Built features this core segment desperately wanted
  4. Ignored feature requests from users who were “not disappointed”

Within quarters, they hit 33%, then kept pushing. By systematically optimizing for the “very disappointed” percentage, they achieved strong PMF.

The brilliance of this approach? It’s a leading indicator. You don’t need to wait for revenue hockey sticks or viral growth to know if you’re on the right track. You can measure it now with 40-50 quality responses.

When the 40% Rule Misleads You

The test isn’t perfect. A few caveats:

Survey the right users. If you survey everyone who’s ever signed up (including drive-bys who never experienced your core value), your score will be artificially low. Focus on users who’ve experienced the product at least twice in the last two weeks.

Don’t obsess over one number. The 40% threshold is a guideline, not gospel. A 38% score with rapidly improving metrics might be more promising than a 42% score that’s stagnant.

Pair it with other signals. The Sean Ellis test should confirm what you’re already seeing in retention cohorts, customer feedback, and growth patterns—not replace them.

The 40% rule is 15 years old: The B2B SaaS landscape back in the 2000s and early 2010s is vastly different to what it is today. Software has commoditized every industry to the point where feature-parity is the norm for most apps. I love using customer.io and Hubspot, but would I be disappointed if I couldn’t use them tomorrow? I’d find a cheaper option.

What PMF Looks Like for B2B SaaS: It’s Not About Features

Here’s what PMF is NOT: having every feature competitors have, achieving a certain revenue milestone, or getting positive feedback from customers.

PMF for B2B SaaS is about solving a specific, recurring pain point for a well-defined customer segment and doing it so well that they can’t imagine working without you.

PMF Is Defined By:

1. Your Ideal Customer Profile (ICP)

You can’t have PMF “in general.” You have PMF with specific personas in specific situations. Clay didn’t have PMF with “everyone who works with data.” They had PMF with GTM teams and sales agencies who needed to enrich and manipulate prospect data.

Airtable’s Andrew Ofstad admits: “Before we had product-market fit, it was hard to describe the product. We’d say ‘This is a way for you to build software’ or ‘It’s a spreadsheet-database hybrid.'” They had a horizontal product that could do many things—but PMF came when they figured out who it was indispensable for: non-technical teams who needed more than Excel but databases were too complicated and cumbersome to set up.

2. The Job-to-Be-Done

Clayton Christensen’s jobs-to-be-done framework is powerful here. People don’t buy products, they “hire” them to do a job.

Customer.io wasn’t hired to “send emails.” It was hired to “re-engage users who signed up but didn’t convert” or “onboard new customers without engineering work.” That’s the actual job.

If you can’t articulate the job your ICP is hiring your product to do and why your solution is 10x better than alternatives then you probably don’t have PMF yet.

3. Pain Point Severity & Frequency

For a B2B SaaS product to be successful, it must address pain that is:

  • Acute enough that people will pay to solve it
  • Frequent enough to justify a subscription

A tax filing pain point is acute but infrequent (once yearly). That’s a tough subscription business. A competitive intelligence tool sounds good in theory, but companies tend to get far too busy with their own problems to worry or focus on what their competitors are doing until suddenly it becomes a priority – after which it dies down again.

But an email marketing pain point that product managers face weekly? Much better fit for SaaS.

NOT by features. Feature parity with competitors doesn’t equal PMF. Superhuman had fewer features than Gmail but 10x better speed. That was their wedge.

How Long Does It Take to Find PMF? Years, Not Months

Let’s talk timelines. If you’re expecting to nail PMF in 6 months, recalibrate your expectations.

The Data

Research analyzing 24 B2B startups found the median time to PMF was 2 years. Some took longer. A few got lucky earlier.

Clay: 5 years (2017-2022). Despite raising $16M from Sequoia and First Round, they had only ~20 customers paying $30-200/month before January 2022. Their positioning was too broad. Revenue grew 10x the year they finally narrowed focus to GTM teams.

Airtable: 2-3 years in private alpha before public launch. Co-founder Andrew Ofstad: “We realized it would take a long time to get a real MVP out there. You’re competing against spreadsheets that have been around for 30 years.” They saw their first enterprise customer in 2016, four years after starting in 2012.

Customer.io: 18 months to reach $10K MRR. Founder Colin Nederkoorn and his co-founder lived off savings and credit cards for years. “Growth was painfully slow, and we struggled to gain traction. Finding product-market fit was a major hurdle.”

The pattern? Patient capital and low enough burn to iterate for years. The companies that failed were those with massive infrastructure costs (Webvan, Better Place) that demanded immediate adoption they couldn’t achieve.

Sources: West Operators (Clay), First Round Review (Airtable), SaaS Club (Customer.io), Lean B2B Research

What the Journey Looks Like

The path to PMF isn’t linear. Here’s the typical progression:

Months 0-6: Building and Testing

  • Creating MVP with core hypothesis
  • Getting first users (often through founder network)
  • Learning what people actually need vs. what you thought they needed

Months 6-18: The Trough of Sorrow

  • You have users but limited traction
  • Feedback is mixed
  • Unclear if you should pivot or persevere
  • Burn rate creates anxiety

Months 18-24+: Iteration & Breakthrough

  • If you survive, you’ve probably narrowed positioning
  • Core user segment starts showing strong retention
  • Word-of-mouth begins working
  • Growth feels less forced

Not every startup survives the trough. The ones that do typically have patient investors or low burn rates that allow extended iteration.

Measuring PMF: Leading and Lagging Indicators

The 40% rule is your leading indicator, but you need lagging indicators too. These are metrics that confirm PMF after you think you have it.

Leading Indicators (Early Signals)

1. Sean Ellis Test Score ≥40%
Covered above. This is your most actionable early metric.

2. Organic Word-of-Mouth
Are customers voluntarily recommending you to peers? Not because you have a referral program, but because they genuinely want to help colleagues solve the same problem?

3. Narrow, Passionate User Base
Better to have 100 users who love you than 1,000 who are indifferent. Early PMF often looks like a small, obsessed user segment.

Lagging Indicators (Confirmations)

1. Revenue Growth: Double-Digit MoM
According to SaaS PMF research, sustained double-digit month-over-month revenue growth between $10K-$50K MRR is a strong PMF signal. You’re past the “friends and family” stage but not yet at scale where growth naturally slows.

2. Retention Curves Flattening
Your cohort retention charts should flatten after the initial drop-off. If 40% of users are still active 6 months after signup, and that curve is flattening (not continuing to drop), you likely have PMF with that segment.

3. Low Churn (For B2B SaaS)
Healthy B2B SaaS monthly churn is around 2-3.5%. If you’re seeing 5%+ monthly churn, something’s broken. People are trying your product but not sticking. If you have customers that are sticking around for longer, look for similarities between them that can help you identify you ICP, as it’s likely your PMF lies with them.

4. Sales Cycle Shortening
As PMF solidifies, sales get easier. Prospects have heard of you. They come inbound. They close faster because the problem you solve is obvious and painful.

5. NPS (Net Promoter Score) >50
While NPS alone doesn’t prove PMF, scores above 50 indicate strong customer satisfaction and willingness to recommend.

Metric Type ▲▼ Metric ▲▼ Strong PMF Benchmark ▲▼ When to Measure ▲▼
Leading Sean Ellis Test ≥40% “very disappointed” After users experience core value 2+ times
Leading Organic Word-of-Mouth 30%+ inbound from referrals Ongoing – track referral sources
Leading User Passion Intensity Small, obsessed user base Qualitative – support tickets, feature requests
Lagging MRR Growth Rate 10-20% MoM ($10K-$50K MRR) Monthly tracking once past $10K MRR
Lagging Monthly Churn Rate ≤3.5% for B2B SaaS Track by cohort monthly
Lagging Retention Curve Flatten Cohorts retain 40%+ at 6 months Quarterly cohort analysis
Lagging CAC Payback Period ≤12 months Quarterly once sales process established
Lagging Net Promoter Score >50 Quarterly surveys of active users

Sources: Sean Ellis PMF Research, Recurly SaaS Benchmarks, Vitally Churn Data

The Currency Progression Framework: From Attention to Money

One useful mental model for PMF stages is the Currency Progression Framework—what “payment” you’re able to extract from users at different stages:

1. Attention: Users will read your emails, visit your landing page
2. Time: Users will try your product, sit through a demo
3. Reputation: Users will publicly endorse you, give testimonials
4. Commitment: Users will integrate your product into their workflow
5. Money: Users will pay for your product

Framework: Currency Progression Model for PMF Stages

PMF happens somewhere between Commitment and Money. If people are willing to integrate your product into critical workflows (Commitment) and pay for it (Money), you have PMF.

If you’re stuck at “people will try it but won’t pay” or “people say nice things but don’t actually use it,” you’re pre-PMF.

Questions to Ask Yourself: The Honest Self-Assessment

PMF requires brutal honesty. Here are questions to ask yourself:

Customer Need Questions

  • Can customers clearly articulate the problem you solve? If they can’t explain why they use you, you don’t have PMF.
  • How often do they experience this problem? If it’s quarterly or annually, reconsider your subscription model.
  • What would they use if you disappeared tomorrow? If the answer is “nothing” or “we’d figure it out somehow,” the pain isn’t severe enough.
  • Would they recommend you to peers unprompted? Real PMF drives organic advocacy.

Product Questions

  • What percentage of features do most users actually use? If it’s <20%, you built the wrong things.
  • Are customers using the product the way you intended? If not, either your positioning is off or you’re solving a different problem than you thought.
  • How much of your roadmap is driven by customer requests vs. your vision? Neither extreme is great. PMF requires balance.

Business Questions

  • Is growth primarily inbound or outbound? Pre-PMF companies rely on outbound. Post-PMF companies can’t keep up with inbound.
  • Are you selling or are customers buying? There’s a difference. Selling requires convincing. Buying means they’re already convinced.
  • What’s your CAC payback period? If it takes >18 months to recover customer acquisition costs, your economics are broken.

Avoiding Cognitive Bias

Founders are notoriously bad at objectively assessing PMF because they’re emotionally invested. Here’s how to counter bias:

Talk to churned customers. They’ll tell you the truth. Current customers are too nice.

Survey “somewhat disappointed” users. These are the fence-sitters. Their feedback shows what would turn them into “very disappointed” users.

Look at usage data, not feedback. What people do matters more than what they say. Are they using it daily? Weekly? Or did they log in once and disappear?

Set a kill switch date. Decide upfront: “If we don’t hit X metric by Y date, we pivot.” Prevents endless iteration on a fundamentally broken idea.

Category PMF Signal (✓ = Yes) Pre-PMF Signal (✗ = No)
Customer Articulation ✓ Customers can clearly explain what problem you solve ✗ They struggle to explain why they use you
Pain Frequency ✓ Users experience the problem weekly or daily ✗ Problem occurs quarterly or annually
Alternative Solutions ✓ “We’d be lost without this” or switch to inferior alternative ✗ “We’d figure it out somehow” or “nothing”
Organic Advocacy ✓ Customers recommend you unprompted to peers ✗ No one talks about you unless you ask
Feature Usage ✓ Users engage with 50%+ of core features ✗ <20% feature usage, lots of unused features
Product Usage ✓ Customers use it as intended in their workflow ✗ Workarounds or using it differently than designed
Growth Source ✓ 30%+ growth from inbound/referrals ✗ Growth primarily from cold outbound
Buying vs Selling ✓ Customers come convinced, you’re closing deals ✗ Heavy convincing required, long sales cycles
Unit Economics ✓ CAC payback <12 months, LTV:CAC >3:1 ✗ CAC payback >18 months, negative margins

Framework: PMF Self-Assessment Checklist

What NOT to Do Before Finding PMF

This is critical. Most startup failure modes are doing things too early—before PMF.

Don’t Scale Your Team

As covered in our article on why B2B SaaS startups fail, premature scaling is fatal. Hiring a VP of Sales before you have a repeatable sales process just burns cash. They can’t sell a product that hasn’t found its market.

The Messenger hired 300 staff immediately and burned $50M in 8 months on only $3M revenue. They scaled before PMF and imploded.

Better scaled to 8,000 employees during the real estate boom, then had brutal, repeated layoffs when the market shifted. They’d scaled for growth that wasn’t sustainable.

Wait until you have 20-50 customers closed by founders through a process you can articulate. Only then hire your first sales rep.

Don’t Spend Heavily on Marketing

Paid acquisition before PMF is throwing money into a leaky bucket. If retention is weak because PMF isn’t there, more users just means more churn.

Invest in content that educates your market (like Customer.io did with conversion copywriting resources), not performance marketing trying to force growth.

Don’t Build Too Many Features

Feature bloat is a PMF killer. Airtable knew their MVP bar was high—competing with spreadsheets with 30 years of features—but they didn’t try to match every feature. They built the minimum that delivered their core value proposition and expanded from there.

If you’re below 40% on the Sean Ellis test, more features won’t save you. Better positioning and deeper value for your core segment will.

Don’t Raise Too Much Money

Counterintuitive, but massive funding pre-PMF creates pressure to scale before you’re ready. You’ll hire too fast, spend too much, and run out of runway before finding PMF.

Customer.io’s Colin Nederkoorn took a non-traditional approach to fundraising: “We view funding as a tool to get to the next stage of the business.” They raised only what they needed when they needed it, maintaining control and optionality.

How to Accelerate Your PMF Journey (Without Shortcuts)

You can’t skip the work, but you can avoid common detours.

1. Talk to 40-100 Customers Before Building

Customer development isn’t optional. Clay, Airtable, Customer.io—all of them spent months (in some cases years) in customer conversations before scaling.

Don’t just ask “Would you use this?” Ask:

  • “How do you solve this problem today?”
  • “What have you tried that didn’t work?”
  • “If this worked perfectly, what would change for you?”
  • “What would make this a must-have vs. nice-to-have?”

2. Focus on One ICP Segment First

Horizontal products (like Airtable) took years to find PMF precisely because they tried to serve everyone. Clay’s breakthrough came when they focused exclusively on GTM teams. Revenue grew 10x that year.

Pick your most enthusiastic user segment and make the product incredible for them. Expand later.

3. Ship Fast, Iterate Faster

Airtable spent years in private alpha iterating with early users. They built prototypes, tested with users, got feedback, and repeated. Every. Single. Day.

Don’t wait for perfection. Get something in users’ hands and learn.

4. Use Quantitative + Qualitative Data

The Sean Ellis test (qualitative) should align with retention metrics (quantitative). If they don’t, dig deeper. Maybe your happiest users aren’t representative, or maybe your metrics are misleading.

Knowing If You’re Losing PMF (The Drift)

PMF isn’t permanent. Markets evolve. Competitors emerge. You can drift out of fit.

Warning Signs

Retention curves trending downward. New cohorts should retain as well as or better than old cohorts. If retention is declining, something shifted.

Increasing CAC without corresponding LTV increase. If it’s getting more expensive to acquire customers but they’re not worth more, your economics are deteriorating.

Customers using fewer features over time. Healthy products see increasing feature adoption as users mature. Declining feature usage signals disengagement.

Competitors mentioned more in churn surveys. If “we switched to [competitor]” becomes a common churn reason, you’re being outmaneuvered.

Longer sales cycles despite more resources. If you’ve hired sales reps and ramped up marketing but deals take longer to close, the market is telling you something.

Budgets are being allocated elsewhere. If your renewals are struggling because the clients don’t have the budget for your solution then you could be losing PMF as the team isn’t able to build a strong business case why they should keep your product – or continue paying the price you’re charging.

When to Embrace Feature Creep vs. Constrain It

This is nuanced. Some feature expansion is healthy and helps deepen the value for your core ICP. Some is poison, and dilutes your positioning by trying to serve everyone.

This is a really tricky one to navigate. At Unmetric we were focused exclusively on social media benchmarking and competitive analytics. As social media became less about great content and more about paid ads, budgets for social got reallocated from organic content to performance content. They cared less about what their competitors were doing organically and more on what ROI they were getting from their ads.

Unmetric could have built out features to start tracking ads and ad spend, but it would be a big pivot away from our core offering. In the end Unmetric was acquired to become a ‘feature’ of a larger social media publishing tool.

Embrace expansion when:

  • Customers with highest retention request it
  • It deepens the moat for your core use case
  • It’s logical progression of the job-to-be-done

Constrain expansion when:

  • It’s chasing a different ICP or use case
  • Your “very disappointed” users don’t care about it
  • It’s defensive (building features just because competitors have them)

Superhuman’s Rahul Vohra had a clear framework: only build features that increase the “very disappointed” percentage. If a feature request came from “not disappointed” users, it was a no.

Real Examples: Companies That Found PMF

Let’s look at how actual B2B SaaS companies found PMF, with links to founder interviews.

Clay: 5-Year Grind to Overnight Success

West Operators article on Clay’s evolution

Timeline: 2017-2022 (5 years)
The Struggle: Despite $16M from top VCs, only ~20 customers paying $30-200/month by January 2022. Positioning was too broad: “Build tools & workflows to supercharge your team.” Could be applied to literally anyone. No one knew what it meant.
The Breakthrough: Focused exclusively on GTM teams and sales agencies. Revenue grew 10x in a year.
Lesson: Even with great investors and a working product, finding the right positioning takes years.

Airtable: Years Competing with 30-Year-Old Spreadsheets

First Round Review: Airtable’s Path to PMF

Timeline: Started 2012, first enterprise customer 2016
The Struggle: “Before we had product-market fit, it was hard to describe the product.” Horizontal products that can do many things struggle with positioning.
The Breakthrough: Templates, use case clarity, and time. By 2016, they saw the product spreading “team to team” organically.
Lesson: High-quality horizontal products take even longer. Their MVP bar was incredibly high: competing with Excel.

Customer.io: 18 Months to $10K MRR, Then 10 Years to $70M

SaaS Club podcast with Colin Nederkoorn

Timeline: 18 months to $10K MRR, now $70M ARR
The Struggle: “Growth was painfully slow. We lived off savings and credit cards for years.” Struggled with positioning and messaging.
The Breakthrough: Content marketing. Colin learned conversion copywriting and educated his audience, building credibility before officially launching.
Lesson: Sometimes PMF comes from nailing go-to-market, not just product.

Gamma: The “First 30 Seconds” Strategy That Unlocked Viral Growth

PMF Show with Jon Noronha | Lenny’s Podcast with Grant Lee

Timeline: 3 years of building (2020-2023), then systematic 4-month rebuild, explosive growth March 2023

BEFORE: The PMF Problem (Pre-AI, 2020-2023)

Who they targeted: Design-forward professionals who wanted to create “beautiful presentations” but found PowerPoint limiting. Essentially trying to be “Canva for presentations.”

The pain they solved: Making presentations look modern and visually appealing without design skills.

Why it didn’t have PMF:

  • The problem wasn’t painful enough. PowerPoint is “good enough” for most people.
  • Their solution required too much work. Users still had to understand layouts, choose templates, drag-and-drop elements.
  • The product felt like “a toy, not real work.”
  • Won Product Hunt’s Product of the Day, Week, AND Month, but signups flatlined after the spike. No organic growth.
  • After 3 years: “just a few hundred users after burning millions.”

AFTER: The True PMF (Post-AI, March 2023+)

Who they target: Individual knowledge workers who need to create professional content rapidly without design expertise: founders creating pitch decks, consultants building client proposals, educators developing lesson plans, marketers making sales presentations, freelancers building portfolios.

The pain they solve: “I have messy ideas/notes/content and need a polished, professional presentation in minutes, not hours.”

Why THIS has PMF:

  • The pain is acute and frequent. Consultants create client decks weekly. Founders pitch constantly. Educators prepare lessons daily. This isn’t a “nice improvement” on an existing solution, it’s saving them 2-3 hours every time.
  • The alternative is terrible. 80% of users discovered Gamma from a friend or social media because they saw someone transform messy ideas into a polished deck in 30 seconds and thought “I need that.”
  • They deliberately DON’T target designers. Unlike Figma, Gamma expands market by not competing for design professionals. They’re for people who CAN’T or DON’T WANT TO design.
  • The job-to-be-done is clear. Not “make beautiful presentations” (vague). It’s “turn my rough content into client-ready materials in minutes” (specific, measurable, urgent).

The Specific ICP Who Love It:

  • Founders & marketers creating pitch decks and proposals quickly
  • Educators & trainers developing clean, engaging lesson materials
  • Consultants building client deliverables under time pressure
  • Remote teams needing web-based collaboration
  • Non-designers who want professional output without the learning curve

Who it’s NOT for (and Gamma knows it):

  • Data analysts needing complex, precise visualizations
  • Corporate teams locked into strict branding guidelines
  • Designers seeking pixel-perfect control
  • Teams requiring offline PowerPoint workflows

The Systematic Rebuild (Not Luck):

In early 2023 with only 12 months of runway left, CEO Grant Lee set a brutally simple rule: “The first 30 seconds in Gamma had to feel like a superpower.”

They gave themselves a 4-month sprint to completely rebuild the onboarding experience around AI. The team:

  1. Flattened the learning curve dramatically. The old version required users to “do so much work to just even comprehend what we were doing.” The new version: paste messy content or write a short prompt, watch Gamma turn it into a clean, structured presentation in 10 seconds.
  2. Made the “aha moment” instant. Within 30 seconds of signing up, you’ve already experienced Gamma’s magic. Most users click “Generate,” write 10-15 words, and watch AI create each element of their presentation live. They didn’t need time to slowly understand—they saw value immediately.
  3. Built experimentation into everything. Coming from Optimizely, the team runs experiments on 20-25 AI models simultaneously. They test different models, prompt strategies, layout patterns, diagram styles. For example, testing Claude 3 Haiku against their existing setup showed a 30% increase in user satisfaction, translating to 20% lift in free-to-paid conversion.
  4. Invested heavily in design taste. One-third of Gamma’s team are designers. The product wasn’t just fast, it was beautiful. Good design created delight and delight created sharing.

The Launch (March 2023):

Before AI: 8 months to reach 60,000 signups total
After AI: Less than one week to add the next 60,000 signups

They went from 2,000 → 5,000 → 10,000 → 20,000 signups per day. Organic growth picked up, word-of-mouth took off, retention curves looked healthier.

The Viral Moment (It Wasn’t Just Luck):

Yes, founder Grant Lee posted a provocative tweet and Paul Graham’s reply went viral (2,000 → 60,000 signups/day). But here’s what people miss: the viral traffic stuck because the product delivered on the promise.

The traffic spike was so massive their servers crashed for three days. But when they came back online, the surge continued. Users who’d experienced the ‘first 30 seconds magic’ were desperate to get back in. That wasn’t dumb luck. That was a product finally delivering on its promise.

Sure, the viral tweet was the accelerant but the fuel was the rebuilt onboarding that made every new user want to share.

After the Viral Moment (The Real Work):

Most startups would have immediately scaled marketing spend and hiring. Gamma did the opposite. Instead of “growth at any cost,” they tightened fundamentals:

  • Improved activation rates
  • Smoothed first-run experience
  • Strengthened retention curves
  • Made shareability frictionless

Only when data showed sustained pull did they layer on growth tactics:

Micro-Influencer Strategy: Grant personally onboarded early creators 1:1, teaching them to explain Gamma in their own voice. Focused on thousands of niche creators (educators, productivity experts) instead of celebrity mega-creators. LinkedIn converted 4-5x better than other platforms.

Product-Led Virality: Every Gamma created includes a “Made with Gamma” watermark. Collaboration features drive team expansion. The product itself became the growth loop.

Results:

Lesson: The Paul Graham tweet created a spike. But PMF came from systematically perfecting the first 30 seconds until the product was so delightful people couldn’t help but share it. Awards and press don’t equal PMF. Real PMF feels like “pull” not “push.” You know you have it when organic growth sustains itself without constant marketing.

Fast: What Happens When You Scale Without PMF

Timeline: Shut down April 2022 after ~2 years
The Problem: Burned $10M/month on only ~$600K annual revenue (200:1 ratio). Hired 400 employees before the product worked reliably.
Lesson: All the money and marketing in the world can’t fix a broken product-market fit. Fast had neither a working product nor real market demand.

When to Pivot: B2B SaaS Companies That Found PMF After Complete Reinvention

Sometimes the path to PMF requires abandoning your original vision entirely. These companies started building one thing, realized the market didn’t want it, and pivoted to something completely different, often keeping only their team or a small technical component.

Slack: From Failed Game to $27.7B Acquisition

Stewart Butterfield might be the king of pivots. His company Tiny Speck spent years building an online game called Glitch. The game launched in 2011, returned to beta, and by 2012, Butterfield declared it wasn’t viable.

But the internal communication tool they’d built to coordinate between US and Canadian offices? That was special.

Slack officially launched in 2014 and became a unicorn the same year. Four years later: 8 million daily active users, $7B+ valuation. 2021: Salesforce acquired Slack for $27.7 billion.

The pivot wasn’t obvious. Butterfield had to convince his team to shut down the game they’d spent years building. He conducted internal votes. The first vote went to continue the game. He campaigned harder. The second vote narrowly went to Slack. That democratic process was critical because he team had to feel bought in, even if it “wasn’t completely democratic.”

Lesson: Sometimes the side project you built to solve your own problem is the real product. Pay attention to what users actually love, not what you wish they loved.

Segment: Analytics Product Failed, Data Pipeline Thrived

Segment started trying to build a competitor for Google Analytics. To enable that, they built an SDK that could stream data to multiple destinations. Customers loved the SDK. Nobody used the analytics backend.

The product they’d intended to build failed. But they’d accidentally built something valuable: a customer data platform that became the infrastructure layer for thousands of companies.

That’s a true pivot because they kept the core technology (the SDK) but completely changed the value proposition, customer base, and go-to-market.

Flickr: From Game Feature to Photo Revolution

Before Slack, Stewart Butterfield co-founded Ludicorp, which developed another online game called Game Neverending. Embedded in that game was a photo-sharing feature called Flickr.

When they realized the limited financial potential of the game, they shut down Game Neverending and focused entirely on Flickr. It became one of the best photo-sharing platforms in the early 2000s.

Butterfield literally did this twice. Turned dying games into revolutionary communication platforms. Third time’s a charm?

The Difference Between Pivoting and Traveling

Not all pivots are created equal. SkyFlow CEO Anshu Sharma distinguishes between true “pivots” and what he calls “traveling”:

Traveling (Slack, Flickr): Completely new company. Nothing in common—different product, different customers, different GTM. You’re essentially shutting down one company and starting another.

True Pivot (Segment): Same core technology, but different value proposition or target market. You’re evolving, not reinventing.

Most successful “pivot” stories are actually traveling. And that’s fine—but understand the difference. Traveling requires more courage because you’re throwing away more.

When Should You Pivot?

Pivoting is brutal. You’re admitting your original vision was wrong. Your team might fracture. Sunk cost fallacy screams at you to keep going.

But here are signals it’s time to consider a complete pivot:

No organic pull after 18-24 months. If you’ve been building for two years and still relying entirely on outbound to get customers, the market might be telling you something.

Customers love a side feature more than the core product. Pay attention to usage data. If people are “misusing” your product in consistent ways, they’re showing you what they actually want.

Funding runway is short and growth is flat. If you have <12 months of runway and no path to breakout growth, a pivot might be your only option besides shutting down.

A massive market shift creates new opportunity. Gamma languished for 3 years, then ChatGPT dropped and they pivoted to AI-powered presentations. That timing was everything.

The hardest part? Distinguishing between “we haven’t found PMF yet” and “we’re building the wrong thing.” Clay took 5 years to find PMF but never fully pivoted—they just narrowed positioning. Gamma spent 3 years then completely pivoted to AI.

There’s no formula. But if you’re asking yourself “should we pivot,” you’re probably already 6 months late on making that decision.

Books & Resources for Going Deeper

Want to dive deeper into PMF? Here are the essential resources:

Books

Lean B2B: Build Products Businesses Want by Étienne Garbugli
The definitive guide to B2B customer development and PMF. Step-by-step process from idea to validated business model. Essential for B2B founders.

The Lean Product Playbook by Dan Olsen
Actionable framework for iterating to PMF. More software-product focused, great for established companies launching new products.

The Mom Test by Rob Fitzpatrick
How to talk to customers so they tell you the truth about whether your product sucks. Essential for customer development interviews.

Crossing the Chasm by Geoffrey Moore
Classic on moving from early adopters to mainstream market. Especially relevant for B2B SaaS selling to enterprises.

Communities & Forums

Indie Hackers – Founders sharing revenue, growth tactics, and PMF journeys in public
SaaS Club Community – Focused specifically on B2B SaaS growth and metrics
Lenny’s Newsletter Community – Product-focused, many PMF discussions
r/SaaS on Reddit – Active community of SaaS founders

Moving Forward: PMF Is a Milestone, Not a Destination

Here’s what to remember about product-market fit:

It takes longer than you think. Median is 2 years for B2B SaaS. Clay took 5. Airtable took 3-4. Don’t get discouraged at month 6.

It’s specific, not general. You don’t have PMF with “small businesses.” You have PMF with “sales agencies with 5-20 people who need to enrich prospect data daily.”

It’s measurable. The 40% rule gives you a concrete benchmark. Survey your most engaged users. If ≥40% would be very disappointed without you, you’re probably there.

It’s not permanent. Markets evolve. Stay paranoid. Keep measuring. Keep talking to customers.

Don’t scale before you have it. Hiring, marketing spend, fundraising—all should wait until PMF is clear. Premature scaling kills more startups than anything else.

Most importantly: PMF is the prerequisite for everything else. Until you have it, nothing else matters. Not your growth strategy, not your competitive moat, not your brand. Get PMF first. Then scale.

Now go survey your users. Ask them how disappointed they’d be if your product disappeared. If the answer is “very” from ≥40%, congratulations then you’ve found something rare. If not, you know exactly what to work on.

Oh, and if you made it this far…

Well done 🤗

5 reasons why (your) B2B SaaS startups fail

You’ve poured your heart into building a B2B SaaS product. The early customers love it. Your team believes in the vision. You’ve even raised some funding. You also know the unfortable truth that approximately 90% of SaaS startups fail, and 42% shut down because there’s simply no market need for what they’ve built, but that’s not going to happen to your startup, right?

Those of a delicate disposition might wish to stop reading and close the tab.

This article is not supposed to be a negative, bearish take on startups. It’s a cautionary tale of what to look out for on your journey to unicorn status.

Take, for example, Quibi, which managed to burn through an astonishing $1.75 billion before shutting down after just six months. Color Labs couldn’t survive despite a $41 million investment. Fast could only make $600k revenue from $124.5m invested.

Or, you could just be really unlucky (complacent?) like CloudNordic which was hit by a ransomware attack in 2023 that destroyed all customer data, forcing closure.

The scale is staggering.

In 2024 alone, 966 U.S. startups shut down—a 25.6% increase from 2023. These companies took an estimated $6-$8 billion of VC money with them to the grave. AngelList saw 364 winddowns in 2024, up from 233 in 2023. Each number represents visionary founders, inspired teams, and googly-eyed investors who lost everything.

Time PeriodFailure RateSurvival Rate
After Year 121.5%78.5%
After Year 2~30%~70%
After Year 548.4%51.6%
SaaS-Specific (3 years)92%8%

Sources: U.S. Bureau of Labor Statistics, LinkedIn Research

Let’s dive into what really kills B2B SaaS companies, backed by data from real failures.

Source: CB Insights startup failure analysis

1. The Misaligned Problem: Wrong Pain, Wrong Frequency, Wrong Timing

Product-market fit isn’t binary, it exists on a spectrum. For B2B SaaS, you need a pain point that’s both acute and recurring. But there’s a third dimension that kills startups just as often: timing.

The Pain Frequency Problem

Your solution must address pain that recurs frequently enough to justify subscription revenue. A business might need tax filing once yearly and that’s real pain, but not frequent enough for $99/month SaaS.

Eventloot, a wedding planning SaaS, built a platform that didn’t actually solve wedding planner problems. Delite, a B2B wholesale platform, didn’t satisfy any urgent customer necessity. RateMySpeech invested heavily in a product appealing to only 5% of their target market.

If customers only feel the pain quarterly or annually, you’re building a product business, not a subscription business, and different economics apply.

The Timing Trap: Too Early or Too Late

Market timing can make or break B2B SaaS. Be too early, and you’ll burn through capital educating a market that isn’t ready. Be too late, and you’re fighting established players.

Too Early = Death by Education Costs

When a category doesn’t exist yet for your product, educating your market why they should use your product risks burning through your runway before they are ready to use your product. The failed startup graveyard is littered with category creating products that were way ahead of their time, and, consequently, ahead of what the market wanted.

Webvan pioneered online grocery delivery in 1996, raising $800 million. They promised 30-minute delivery windows a decade before the infrastructure existed. They burned $1.2 billion convincing people to buy groceries online when most barely trusted e-commerce. They collapsed in 2001. Twenty years later, Instacart executed the same model successfully.

Better Place raised substantial funding for EV battery-swapping technology but failed in 2013, years ahead of market readiness. Tesla proved the market existed, but only after building it slowly over a decade.

Thought Machine launched Vault, a cloud-native core banking platform, in the mid-2010s designed to replace legacy banking systems. The product was technically brilliant, but traditional banks weren’t ready to make that leap. The cultural and regulatory readiness simply wasn’t there yet.

Babylon Health faced similar resistance with its AI-driven healthcare solutions. The idea of AI handling medical consultations made healthcare providers and patients uncomfortable – it was simply too far ahead of current practices.

Unless you have OpenAI levels of funding, endeavour to not be a building a category-creating product. I guarantee it’ll end up as an example on this list.

Absence of competition is a red flag, not a signal.

The Multi-Year Grind to PMF

OK, but on the flip side, there are companies that survive long enough for the market to catch up. Clay took five years (2017-2022) to find product-market fit. Despite raising $16M from Sequoia and First Round, they had only ~20 customers paying $30-$200/month before January 2022. Their message was so broad it resonated with nobody.

The breakthrough came when they focused exclusively on GTM teams. Revenue grew 10x that year. But it required five years of iteration.

Airtable faced similar struggles. Co-founder Andrew Ofstad admits: “Before we had product-market fit, it was hard to describe the product.” They struggled to articulate value until customers finally understood. (Sidenote: I’m still not sure who Airtable is catering to. Suggestions in the comments, please!).

The difference? Clay and Airtable had patient capital and low burn. Webvan and Better Place had massive infrastructure costs demanding immediate adoption.

When the Market Moves On

Sometimes the timing problem isn’t being too early or too late, it’s being unable to adapt as the market evolves beneath you. Your product had a moment, but consumer behaviour shifted, competitors executed better, or the entire category moved in a different direction.

Yammer, the enterprise social network Microsoft acquired for $1.2 billion in 2012, seemed poised to dominate corporate communications. I remember using Yammer back in 2012 and thought it was great having an internal social network for the company. When someone suggested we look at Slack I took a look and declared that we already had chat in the Google Workspace, why would we need another chat tool. (Needless to say, I’ve since changed my tone on Slack!).

When Microsoft launched Teams in 2016 as a direct competitor to Slack, it had better integration and a more modern approach, making Yammer irrelevant. By 2023, Microsoft killed the Yammer brand entirely, absorbing it into Viva Engage. The market didn’t disappear, it just moved to a better execution.

Evernote pioneered digital note-taking and reached 150 million users by 2015, with 9.6 million yearly downloads in 2017. Then came Notion, offering databases and customization. By 2023, Evernote’s downloads had plummeted 82% to just 1.7 million. The market wanted connected workspaces, not just note storage. Evernote couldn’t pivot fast enough.

Hootsuite tells perhaps the most complete story of a market that moved on. Founded in 2008, Hootsuite pioneered social media management when brands were racing to build organic social presence. At its peak, the company had 1,400+ employees and was planning a $200 million IPO.

But the market fundamentally shifted: brands moved from creating organic social content to running paid ads, dramatically reducing their need for scheduling and publishing tools. Simultaneously, their entire product became a checkbox feature in platforms like HubSpot and what was once a standalone business got commoditized into marketing automation suites.

The result has been brutal with repeated layoffs: 30% of staff in August 2022 (400 people), another 5% in November 2022, 70 more in January 2023, and another 20% (hundreds more) in October 2025. They postponed their IPO indefinitely. The market didn’t disappear, brands still post on social media, it just stopped needing a dedicated tool for something that became a feature.

The pattern repeats: Box and Dropbox pioneered file sync but were commoditized when Google Drive and OneDrive bundled it for free. Google Docs eliminated the need for file syncing entirely. Basecamp created project management but watched Asana, Monday.com, and ClickUp capture the market with modern interfaces and deeper features.

These weren’t bad products that failed to find PMF. They had product-market fit. The problem? The market evolved, and they didn’t evolve with it fast enough. Either their product roadmap couldn’t keep pace, their architecture made pivoting too expensive, or they were simply outmaneuvered by more agile competitors.

The difficult truth: Even when you nail product-market fit, you’re racing against time before someone builds something better or the market shifts. The companies that survive aren’t just the ones that find PMF, they’re the ones that can continuously re-find it as the market moves.

2. The Platform Dependency Trap: When Your Foundation Crumbles

Building your business on someone else’s platform means accepting existential risk. Social media analytics destroyed by API restrictions. Scheduling apps killed by policy changes. Integration tools that became worthless overnight.

The Twitter API Massacre

In 2023, Twitter increased API pricing from free to as much as $42,000 per month. For analytics platforms built on Twitter data, this was an extinction level event.

I’ve spoken with founders of social media analytics companies entirely dependent on platform APIs. When networks became restrictive about data, their ability to deliver insights evaporated. Customers churned because the product couldn’t provide promised value. The ground simply disappeared beneath their feet.

Apollo calculated that Reddit’s API pricing would cost them $1.7 million monthly. The app shut down. PostMyParty saw seven years of work jeopardized when Meta shut down API access, affecting 10,000+ customers.

CartHook, a Shopify app for post-purchase upsells, thrived until Shopify restricted their core functionality. They had to pivot completely and rebuild from scratch, rendering years of work worthless.

Perhaps the most infamous example of platform dependency is Zynga. The gaming company behind FarmVille reached 60 million daily users and made up 19% of Facebook’s revenue in 2011. Their IPO filing acknowledged their “potentially fatal flaw”: a complete dependence on Facebook’s platform. When Facebook ended their special relationship in 2012 and changed payment policies to take a larger revenue cut, Zynga’s fate was sealed. The company survived only because they eventually diversified, but it cost them years and billions in market value.

The False Security of Platforms

Early on, platforms love third-party developers. They talk about “ecosystems” and “partnerships.” But platforms embrace developers until those developers become competitive threats.

How to Reduce Platform Risk:

  • Diversify data sources. Don’t rely on one platform
  • Own your customer relationships directly
  • Build proprietary value beyond the underlying data
  • Have contingency plans if your primary API disappears

Platform risk is often unavoidable early on and in many instances is a smart move as you gain a network effect by building for an existing, captive market. But understand that every API call is a potential single point of failure for your business.

3. The Money Problem: When Unit Economics Don’t Work

According to CB Insights, 38% of startups fail because they run out of cash. But “running out of cash” is really a symptom: failure to find a market that needs your product so badly that they are willing to pay for it, poor unit economics, unsustainable customer acquisition costs, and broken pricing.

The Customer Acquisition Cost Death Spiral

Your customer lifetime value (CLV) must significantly exceed your customer acquisition cost (CAC). Ideally 3:1 or better. Many startups discover too late their CAC is simply too high.

An enterprise SaaS might spend $15,000 to acquire a customer paying $500/month. That’s 30 months to break even. But at 3% monthly churn, average customer lifetime is 33 months. You’re barely breaking even on acquisition, let alone covering operational expenses.

The Churn Rate Reality

Churn silently kills SaaS businesses. It’s possibly the most important metric that a SaaS company can track. According to 2025 Recurly research, median monthly churn is 3.5%. For smaller startups, rates can hit 5-10% monthly.

Source: Recurly 2025 Churn Report, Cobloom Research

At 5% monthly churn, you’re replacing your entire customer base every 20 months. That might feel like you’re building a business, but you’re actually filling a leaky bucket. This is why so many SaaS companies hit the $20-$30m glass ceiling and fail to grow from there. They can bring in new customers every month but even at modest churn rates they are simply replacing the revenue that they’re losing.

The Everpix Cautionary Tale

Everpix had a beautiful product users loved, raising $2.3 million from respected investors. But they had negative gross margins, which meant they were spending more to acquire and serve customers than those customers would ever pay.

When it came time to pay Amazon Web Services, Everpix ran out of cash and shut down in 2013. Their P&L: $566,000 in legal costs, $360,000 in operations, $1.4 million in salaries for seven employees—against only $254,000 in revenue. The math didn’t work.

Successful SaaS startups obsess over unit economics. They know their CAC by channel, track cohort retention religiously, and understand exactly when they’ll achieve profitability. Failed ones discover their economics are broken when it’s too late.

As with every SaaS failure, there are exceptions to the rule. Uber lost an eye-watering sum of money acquiring customers and only in the last few years has been able to break event. Most AI companies, OpenAI, Anthropic, xAI, etc. are burning through literally billions of dollars to be the last app standing and grab the winner takes all prize.

Most apps don’t have tens of billions of dollars to spend to win a category.

The Fast way to spend $124.5m of VC money

Fast was aiming to take on Shopify with its one-click checkout. It raised $124.5m to take on this challenge, but the unit economics had a galaxy-sized hole in them. They were spending $10m per month on an annual revenue of $600k. By comparison, Bolt, one of their competitors, was doing $40m revenue a year in the same period.

4. The Technical Reality Gap: When Reliability Doesn’t Match Marketing

B2B SaaS startups fail when they promise what their technology can’t deliver. Like Elon Musk, but without the army of fanboys. The sales pitch is compelling, demos polished, but the software just doesn’t work. This gap creates trust problems that are hard to recover from.

When Technical Debt Becomes Bankruptcy

According to a 2024 survey, 80% of leaders reported technical debt caused delays and higher costs. For startups, these delays are fatal.

Sources: Morning Consult/Unqork Survey 2024, Virtusa Research

Zeus Living is a perfect example. The furnished housing rental startup exploded during the pandemic when remote work surged. They invested heavily in 2021 to “get more homes.” But when interest rates spiked post-pandemic, their infrastructure couldn’t adapt. The company couldn’t pivot its technical architecture or business model fast enough.

Rubica, a cybersecurity SaaS, had the right product at the right time. But when COVID-19 hit, their target customers cut spending dramatically. They couldn’t adapt their go-to-market strategy fast enough. The irony is that if they had survived to today, cybersecurity is one of the fastest growing industries in SaaS right now.

Fast, which I talked about earlier, also talked a good talk, but merchants found it hard to integrate, their customers found the checkout button buggy, and the product didn’t live up to the marketing hype the company had whipped up.

Building Reliability While Moving Fast

Successful startups invest in monitoring from day one, practice defensive coding with automated testing, plan for scale even when small, and are honest with customers about capabilities.

A stark recent example is 11x.ai, an AI SDR platform that raised $74 million from top VCs like Andreessen Horowitz and Benchmark. The technology promised to replace human sales development reps with AI agents. But the company experienced 70-80% churn rates because the product simply didn’t deliver on its slick marketing promise.

ZoomInfo reported the AI “performed significantly worse than our SDR employees.” One customer used it for six months and booked zero meetings. The technology couldn’t match the marketing promise, leading to massive customer churn and eventual scandal.

Allegedly 11x was able to show sales traction by signing on clients with generous money back guarantees locked into the contract – and many companies activated that money back guarantee. However (allegedly) 11x was less than diligent with their book keeping to say that the customer had churned and money had been returned.

The fastest way to kill a SaaS startup isn’t slow development, it’s building something that doesn’t work as advertised. In B2B sales, trust is everything.

5. The Team Problem: When Human Issues Sink the Ship

Technology problems can be fixed. Product problems can be pivoted. But team problems? Often terminal. According to CB Insights, 23% of startups fail due to not having the right team.

Co-Founder Conflict

You started with shared vision. But as the company grows, cracks appear. Disagreements on direction, complementary skills becoming conflict, unequal weight pulling. Co-founder conflict contributes significantly to failures. This conflict bleeds into the organization. Teams pick sides. Decision-making halts. Best employees leave.

The Hiring Mistakes

Zirtual, a virtual assistant marketplace, is a cautionary tale. High burn rate and management issues brought the firm to bankruptcy. Despite customers who loved the service, internal dysfunction destroyed the business.

Freshconnect failed after mistakes with team focus and bad hiring. Co-founder Tarun couldn’t secure additional funding. Not because the market didn’t exist, but because the team couldn’t execute. The company was acqui-hired, with most of the original team laid off.

The Acquihire Reality

Let’s talk about what actually happens when B2B SaaS startups fail. Media reports “acquisitions” and “acqui-hires” as success stories. But the reality for most team members is brutal.

In typical acquihires, the acquiring company buys talent, not your product. The sales and engineering team are often kept on, everyone else is dead weight. Marketers are usually the first casualty of any cost cutting. The company cherry-picks who they want, usually founders, key engineers, and the top performing sales people. Everyone else? Laid off.

In 2024-2025, we saw “reverse acquihires” where tech giants poached entire teams from AI startups without buying companies. Character AI had Google license their technology for $2.7 billion and hire their co-founders, leaving the rest to fend for themselves. Adept, once valued at $1 billion, saw Amazon hire key staff while the company struggled.

I’ve even heard stories of startups being ‘acquired’ only for it to be an IP acquisition, not a company acquisition. The tenured staff with vested stock options get nothing because the company hasn’t been acquired, only the technology and IP. Absolutely brutal stuff.

These aren’t success stories. They’re soft landings for founders while everyone else faces unemployment.

The Fatal Mistake: Scaling Before PMF

This is the most seductive trap: you raise Series A, and everyone expects you to “act like a real company.” So you hire aggressively. VP of Sales from Oracle. Growth Marketing guru from the billion dollar SaaS brand. Sales development team. You go from 5 to 25 people in six months.

One problem: you haven’t found product-market fit yet.

No sales talent can fix a product nobody wants. No growth marketing can create demand where none exists. When you scale before nailing product and go-to-market, you’re pouring gasoline on an unlit fire.

Better scaled to 8,000 employees during the 2020-2021 real estate boom. When interest rates rose, they faced brutal layoffs. The CEO fired 900 employees over Zoom in 2021, then continued cuts through 2024.

The Messenger launched in May 2023 with 300+ staff and imploded in 8 months. They burned $50 million while generating only $3 million in revenue. By January 2024, they shut down completely, letting all 300 staff go with zero severance.

Moxion Power raised $100 million Series B in 2022 to scale manufacturing. By July 2024, they suddenly furloughed all 400 employees. By August, they filed for Chapter 7 with $100-500 million in liabilities. They grew too quickly before resolving technical issues or achieving sustainable sales.

Fast added 400 staff members, paid eye-watering sums of money to celebrities, and spent lavishly on offices and perks, all before they had a product that the market wanted. They closed down having taken $124.5m of VC money, and made just $600k a year. For every $200 they spent, they managed to turn that into just $1.

The 2020-2021 Funding Aftermath

The premature scaling epidemic worsened because of 2020-2021. Many startups received seed funding “probably before they were ready”. Rapid capital encouraged high burn rates and “growth-at-all-costs mentalities.”

The result? In 2024, 966 startups shut down, up 25.6% from 2023. More than 95,000 U.S. tech workers were laid off in 2024 alone.

Most weren’t companies with bad products. They failed because they scaled too fast, hired too many people, and burned through capital before proving their business models.

If you’re going to build a sales organization, here’s the only sequence that works:

The right sequence:

  1. Founders sell until 20-50 deals (repeatable process)
  2. Hire first sales rep to validate it can be taught
  3. Only after that rep succeeds, hire sales leader
  4. Scale gradually as each stage proves out

Skip steps, and you’re pouring money into a team that can’t succeed because the fundamentals aren’t proven yet.

Moving Forward: What Sets Survivors Apart

If 90% of SaaS startups fail, what makes the 10% different?

They validated pain frequency before scaling. They diversified away from platform risk. They obsessed over unit economics. They built reliability into their DNA. They got the people right.

What you can do right now:

Validate pain frequency. Talk to 20 potential customers. Ask how often they experience your problem. If “occasionally” or “quarterly,” rethink your model.

Be prepared to take the most difficult decisions of your life if you discover that the need your product is addressing doesn’t have a market or a recurring frequency, like Ishita Arora did when she shut down her startup, DaySlice and returned what was left of the investor money.

Toni Hohlbein took the impossible decision to shutdown GrowBlocks when it became apparent that SaaS companies didn’t have the hair-on-fire pain when it came to revenue operations. Sales leaders couldn’t explain the value to the CFO of paying $30k for a dashboard that helped visualize their sales operations.

Assess platform risk. List every external dependency. What happens if each disappears tomorrow? If “game over,” start diversifying.

Know your numbers. Calculate actual CAC by channel, churn rate by cohort, and CLV. If the math doesn’t work now, it won’t at 10x scale.

Audit technical debt. Be honest about corners cut. Pay down debt before it compounds.

Evaluate your team. Are the right people aligned on the mission? Are you creating an environment for their best work?

Don’t be dismissive of new trends. The market you have product-market fit in today might shift tomorrow. Someone with better UX, modern tech stack, or smarter positioning can render you obsolete. Build a culture of continuous improvement and customer obsession. Talk to users weekly. Watch competitors monthly.

Ask yourself quarterly: “If we were starting today, would we still build it this way?” If the answer is no, start evolving before a more agile competitor forces you to.

The B2B SaaS market is brutal. Immense competition, high expectations, razor-thin margins for error. But understanding why companies fail is the first step to being one that succeeds.

Your startup might face all five challenges. The question isn’t whether you’ll encounter these problems, it’s whether you’ll recognize them early enough to act.

Reviewed: The best AI notetaking tools for meetings (2026 update)

If you’re a founder, solopreneur, or team lead juggling back-to-back meetings, you already know the painful reality: you’re either fully present in the conversation or frantically scribbling notes. You can’t do both well.

Here’s what finally pushed me to test every major AI notetaking tool on the market. According to Microsoft’s 2025 Work Trend Index, employees are interrupted every two minutes during core work hours – up to 275 times a day – by meetings, emails, and chats. The average worker receives 153 Teams messages and 117 emails daily. And 57% of the average employee’s time is spent in meetings, email, and chat rather than actual productive work.

When meetings consume this much of your day, capturing what actually matters becomes critical. Miss one key decision or action item, and you’re stuck scheduling another meeting just to clarify what happened in the first one.

I spent three months testing over a dozen AI meeting notetakers, running them through real client calls, team syncs, and investor meetings. I evaluated each one on transcription accuracy, summary quality, integration capabilities, pricing, and that often-overlooked factor: whether the tool actually fits into how busy founders work.

Here are the 5 best AI notetaking tools that genuinely deliver for people who don’t have time to babysit their productivity tools.

The Real Cost of Not Using an AI Meeting Notetaker

Before diving into the tools, let’s talk numbers. Research from Fellow.ai’s 2025 survey found that 75% of professionals now use an AI note-taker in their work meetings. This isn’t a nice-to-have anymore – it’s becoming standard workplace infrastructure.

The productivity math is straightforward. If you spend 15 minutes after each meeting writing up notes and action items, and you have 5 meetings a day, that’s over 6 hours per week just on meeting documentation. Tools like MeetGeek report their users see a 30% boost in productivity by eliminating unnecessary follow-up meetings and reducing documentation time.

Source: Microsoft Work Trend Index 2025, Fellow.ai Research

But here’s what surprised me most during my research: 50% of people who don’t use AI notetakers cite privacy and security as their main concern. And 84% of users say they change how they speak when an AI note-taker is present. This matters because if a tool makes your meetings feel awkward or surveilled, it’s actually hurting productivity, not helping it.

That’s why I’ve included both bot-based and bot-free options in my recommendations below.

1. Fathom – Best Free AI Notetaker for Individuals

If you’re bootstrapping or just want to try AI notetaking without commitment, Fathom is where I’d start. Their free tier is genuinely unlimited – unlimited recordings, unlimited transcription, unlimited storage. That’s not a typo, and it’s not a 14-day trial.

What Makes Fathom Stand Out

Fathom was built by the founder of UserVoice, and you can tell they understand what busy professionals actually need. The tool focuses on doing a few things exceptionally well rather than trying to be everything to everyone.

During my testing, Fathom consistently delivered summaries within 30 seconds of a meeting ending. That speed matters when you’re jumping from one call to the next and need to fire off action items while context is fresh. The transcription accuracy hovered around 95% for clear English audio with good microphones.

The native CRM integration with HubSpot and Salesforce is particularly valuable for sales-focused founders. Meeting notes automatically sync to your CRM records, eliminating the data entry that usually falls through the cracks.

Fathom’s Limitations

The free plan restricts AI-powered summaries and action items to 5 meetings per month. After that, you get recordings and transcripts but need to do your own analysis. For occasional meeting takers, that’s fine. For heavy users, you’ll want to upgrade.

There’s also no mobile app as of late 2025, which is a notable gap if you take calls from your phone frequently. And like most AI notetakers, Fathom uses a visible bot that joins your meetings as a participant. Some clients find this awkward – I’ve had prospects ask about it mid-call.

Fathom Pricing

  • Free: Unlimited recordings and transcription, 5 AI summaries/month
  • Premium: $19/user/month (billed monthly) or ~$15/month annually
  • Team Edition: $29/user/month with collaboration features
  • Team Edition Pro: $39/user/month with advanced coaching and analytics

2. Fireflies.ai – Best for Comprehensive Features and Integrations

When you need an AI notetaker that can handle complex workflows and integrate with practically anything, Fireflies.ai is the tool I recommend. It’s not the cheapest option, but the feature depth justifies the investment for teams running serious operations.

What Makes Fireflies Stand Out

Fireflies supports transcription in over 100 languages – more than any other tool I tested. If you’re running a distributed team or selling internationally, this matters. The accuracy held up well even in my tests with non-native English speakers.

The AI Apps feature launched in 2025 is genuinely innovative. Fireflies has built over 200 pre-built AI workflows that automate everything from CRM data logging to generating follow-up emails to creating content calendars from meeting discussions. For a team lead managing multiple departments, this level of automation is transformative.

I particularly appreciated the conversation intelligence features on the Business plan. You get talk-time analytics, sentiment analysis, and topic tracking across meetings. This is invaluable for sales coaching or understanding how your team communicates.

Plan Monthly Price Storage AI Summaries Best For
Free $0 800 minutes Limited (20 credits) Testing the platform
Pro $10/user (annual) 8,000 minutes Unlimited Individual professionals
Business $19/user (annual) Unlimited Unlimited + Analytics Growing teams
Enterprise $39/user (annual) Unlimited + Private Full suite + SSO Large organizations

Source: Fireflies.ai Official Pricing (January 2025)

Fireflies’ Limitations

The AI credit system is confusing. While plans advertise “unlimited summaries,” advanced features like AskFred (their AI assistant) consume credits that can run out. I’ve seen users report unexpected charges when they relied heavily on AI features. Read the fine print carefully.

The free plan’s 800-minute storage limit also fills up faster than you’d expect if you’re recording multiple meetings daily. Plan on upgrading within a month or two of regular use.

Fireflies Pricing

  • Free: 800 minutes storage, 20 AI credits/month
  • Pro: $10/user/month (annual) – unlimited transcription, 8,000 minutes storage
  • Business: $19/user/month (annual) – unlimited everything, video recording, team analytics
  • Enterprise: $39/user/month – SSO, HIPAA, private storage

3. Otter.ai – Best for Live Collaboration and Real-Time Transcription

Otter.ai pioneered the AI meeting assistant category, and they’ve continued innovating. If live transcription during the meeting matters to you – not just a summary afterward – Otter remains the gold standard.

What Makes Otter Stand Out

Otter’s real-time transcription is genuinely useful in ways I didn’t expect. During a meeting, I can see the transcript updating live, highlight key moments, and add comments without leaving the call. For complex negotiations or detailed technical discussions, being able to mark important statements as they happen is invaluable.

The collaboration features also set Otter apart. Team members can access shared transcripts, add comments, highlight sections, and collaborate on meeting notes in real-time. It functions almost like a Google Doc for your conversations.

For sales teams, OtterPilot for Sales automatically handles administrative tasks like extracting insights and pushing them to Salesforce or HubSpot. Users report up to 95% transcription accuracy in ideal conditions, though this drops with background noise or heavy accents.

Otter’s Limitations

Here’s my biggest gripe with Otter: none of their plans offer unlimited transcription. The free plan caps you at 300 minutes per month with a 30-minute limit per conversation. That 30-minute limit is brutal – most business meetings run 45-60 minutes, which means your transcription cuts off mid-meeting.

Even the paid plans have caps. Pro gives you 1,200 minutes monthly; Business gives you 6,000. If your team has heavy meeting loads, you’ll need to carefully monitor usage or face overage charges.

Video replay is also locked behind the Enterprise tier, which starts around $17,000-31,000 annually according to third-party pricing data from Vendr. That’s a steep jump from the Business plan just to get video playback.

Otter Pricing

  • Free: 300 minutes/month, 30 minutes per conversation, 3 lifetime file uploads
  • Pro: $8.33/user/month (annual) – 1,200 minutes/month, 90 minutes per conversation
  • Business: $20/user/month (annual) – 6,000 minutes/month, 4 hours per conversation
  • Enterprise: Custom pricing – video replay, advanced security

4. Granola – Best Bot-Free Privacy-First Option

If the visible bot joining your meetings feels awkward or you’re dealing with clients who are uncomfortable being recorded, Granola offers a fundamentally different approach. It’s bot-free, capturing audio directly from your device without any participant joining your call.

What Makes Granola Stand Out

Granola raised $43 million in May 2025 on top of a $20 million Series A the year before. That funding reflects serious market confidence in the “invisible AI assistant” approach.

The experience is genuinely different. You start a Zoom or Google Meet call, and Granola just runs in the background. No bot pops up in the participant list. No awkward moment explaining why “Granola Notetaker” just joined. For client-facing calls, investor meetings, or any situation where you want to stay fully present without tech intrusions, this matters.

The “memo + AI” workflow is also clever. You can jot quick notes during the meeting – keywords, thoughts, questions – and Granola combines your notes with the full transcript to generate more relevant summaries. In my testing, this hybrid approach produced better action items than tools relying purely on automatic analysis.

Source: Fellow.ai 2025 Survey on AI Meeting Tool Adoption

Granola’s Limitations

Privacy isn’t free – literally. Model training opt-out is only default for Enterprise customers. On other plans, you have to manually opt out in settings. For a privacy-first tool, that’s an awkward compromise.

The free plan is also limited to 25 meetings lifetime – not 25 per month, 25 total. That’s essentially a trial, not a usable free tier. And Granola currently only supports Google Workspace accounts, which excludes anyone on personal Gmail or Microsoft 365.

Speaker attribution in group calls can be inconsistent since Granola isn’t tied to individual audio streams like bot-based tools are. In fast-paced discussions with multiple voices, you may need to clean up who said what.

Granola Pricing

  • Free: 25 meetings lifetime (essentially a trial)
  • Individual: $18/month – unlimited meetings for solo users
  • Business: $14/user/month – team features, shared knowledge
  • Enterprise: Starting at $35/user/month – model training opt-out by default

5. tl;dv – Best for Sales Teams and CRM Integration

For teams where every meeting is a revenue opportunity, tl;dv (short for “too long; didn’t view”) focuses specifically on turning conversations into actionable sales intelligence.

What Makes tl;dv Stand Out

The multi-meeting analysis is where tl;dv really differentiates. Instead of just summarizing individual calls, the tool can analyze patterns across dozens or hundreds of meetings. Sales managers can track objection patterns, feature request trends, or competitive mentions across their entire team’s calls.

The CRM auto-sync is also more sophisticated than most competitors. tl;dv doesn’t just push notes to Salesforce – it automatically updates deal stages, logs activities, and can even draft follow-up emails based on meeting content. For sales operations, this level of automation eliminates hours of manual data entry weekly.

The free tier is generous for what it is: unlimited meeting recordings and viewers, with transcription in 30+ languages. You’re limited to 10 AI notes per month, but unlimited basic transcription means you can capture everything and manually review if needed.

tl;dv’s Limitations

The free plan’s 10 AI note limit gets restrictive quickly if you have more than 2-3 meetings weekly. And the paid plans jump significantly in price – Pro is $18-29/month per user depending on billing, while Business is $59-98/month per user.

I also encountered some interface issues during testing. The UI has visual bugs, transcript editing is limited, and the overall experience feels less polished than Fireflies or Otter. For a tool focused on sales teams who value professionalism, the rough edges are noticeable.

Custom vocabulary support is missing entirely – you can’t add technical terms, product names, or industry jargon to improve transcription accuracy. For specialized industries like biotech, legal, or finance, this means constant manual corrections.

tl;dv Pricing

  • Free: Unlimited recordings and viewers, 10 AI notes/month
  • Pro: $18/user/month (annual) or $29/month – unlimited AI features
  • Business: $59/user/month (annual) or $98/month – sales coaching, AI speaker insights
  • Enterprise: Custom pricing – dedicated success manager

How to Choose the Right AI Notetaker for Your Needs

After testing all five tools extensively, here’s my decision framework based on different scenarios:

If You Need… Choose This Tool Why
Best free option Fathom Truly unlimited free recordings and transcription
Most integrations and features Fireflies 200+ AI apps, 100+ languages, deep workflow automation
Live collaboration during meetings Otter Real-time transcription with team commenting and highlighting
No bot in meetings / privacy-first Granola Invisible operation, device-level audio capture
Sales-focused with CRM automation tl;dv Multi-meeting analysis, automated deal updates

Based on hands-on testing across real meeting scenarios

For Solo Founders and Bootstrapped Startups

Start with Fathom’s free plan. You get unlimited recordings forever, which lets you build a searchable archive of every conversation without spending a dollar. When you start hitting the 5 AI summary limit regularly, that’s your signal to evaluate whether the Premium upgrade makes sense.

For Growing Teams (5-20 people)

Fireflies Business offers the best balance of features and value. The unlimited storage, team analytics, and extensive integrations justify the $19/user/month investment. The conversation intelligence features help managers stay informed without sitting in on every call.

For Client-Facing Roles

If clients regularly comment on meeting bots or you sense discomfort when asking for recording consent, Granola solves the problem elegantly. The invisible operation maintains meeting flow and eliminates awkward explanations.

For Sales-Driven Organizations

tl;dv with its multi-meeting analysis and CRM automation is purpose-built for sales operations. The ability to track objection patterns and competitive mentions across your entire team’s calls provides insights that individual meeting summaries can’t match.

Quick Accuracy Comparison: How Do These Tools Actually Perform?

I ran the same test script through all five tools to compare transcription accuracy. The script included industry jargon, non-native English speakers, and some intentional crosstalk to stress-test the systems.

Source: Personal testing with standardized test script (January 2025)

Key findings from my testing:

  • Clear audio with native speakers: All tools performed within 3% of each other (92-96% accuracy)
  • Non-native English speakers: tl;dv and Fathom handled accents best
  • Technical jargon: Fireflies’ 100+ language support helped with international terminology
  • Crosstalk and overlapping speakers: Bot-based tools (Otter, Fireflies, tl;dv) significantly outperformed Granola’s device-audio approach

The takeaway? Transcription accuracy is largely a solved problem for normal meeting conditions. The differentiators are in features, pricing, and workflow fit – not raw transcription quality.

The Bottom Line: My Personal Recommendations

After three months of testing, here’s how I actually use these tools in my own workflow:

Primary tool: Fathom – I use it for 80% of my meetings. The free unlimited tier means I never worry about hitting caps, and the 30-second summary delivery fits my rapid meeting schedule.

Secondary tool: Granola – For sensitive client calls, investor conversations, or any meeting where I want to be fully present without tech distractions, Granola’s invisible operation is worth the subscription.

Team deployment: Fireflies – When I help other founders set up their teams, Fireflies’ comprehensive feature set and robust integrations make it the easiest recommendation for organizations that need everything working together.

The AI meeting notetaker market has matured significantly. All five tools I’ve covered deliver genuine productivity gains – the question is which one fits your specific workflow, budget, and privacy requirements.

Start with free tiers to test the experience. Pay attention to how the tool makes you feel during meetings, not just what it produces afterward. And remember: the best AI notetaker is the one you’ll actually use consistently.


Key Takeaways

  • 75% of professionals now use AI meeting notetakers – this is becoming standard workplace infrastructure
  • Fathom offers the most generous free tier with unlimited recordings and transcription
  • Fireflies provides the deepest integrations and automation capabilities for teams
  • Granola is the only bot-free option that doesn’t join meetings as a participant
  • Privacy matters: 84% of users change how they speak when AI is recording
  • Test free tiers before committing – all five tools offer ways to try before you buy

Missing a tool?

Am I missing an email marketing tool from this list? Let me know!

Reviewed: The Best Free Design Tools (2026 Update)

I’ve used a lot of graphic design tools over the years. Some because I had no choice, others because I was actively looking for something better than Canva. I’ve designed landing pages, blog headers, pitch decks, social ads, UI mockups, onboarding screens, and more, often under time pressure and without a dedicated designer.

What I’ve learned is that “graphic design tool” is a broad category. Some tools are built for speed, some for collaboration, some for precision, and some for people who actively dislike design work. This list reflects that reality.

Below are the best graphic design tools I’ve personally used or seriously evaluated, along with when they actually make sense to use.

Quick comparison table

ToolBest forWhy I’d use it
CanvaFast visualsLowest friction, decent results
FigmaSerious designPrecision and collaboration
Adobe ExpressMarketing teamsCanva with Adobe polish
Affinity DesignerPower usersAdobe-level without subscriptions
VismePresentationsStructured, polished outputs
VistaCreateSocial graphicsCanva-like, different template feel
SnappaSpeedAlmost zero learning curve
PiktochartInfographicsData-heavy visuals
EasilBrand controlGuardrails for teams
StencilBloggingSimple blog and social images

Pricing comparison

Here’s a clear comparison table showing free tier availability and entry-level pricing for each graphic design tool we talked about. I’m using typical or commonly available pricing where possible. Actual pricing may vary by region or promotions, so consider this a realistic baseline rather than a guaranteed up-to-the-penny quote.

ToolFree Tier?Free Tier LimitationsEntry-Level PaidWhat You Get at Entry Level
CanvaYesLimited templates/assets, limited brand kits, some export features locked~$12.99/mo ProBrand kits, full asset library, advanced export, Magic Resize
FigmaYes3 projects limit, no team libraries~$12/editor/moUnlimited projects, team libraries, better permissions
Adobe ExpressYesLimited templates, Adobe watermark on some exports~$9.99/moFull template library, removes branding, premium assets
Affinity DesignerNo (one-time purchase)N/A — fully paid outright~$55 (one time)Full desktop app, no subscription
VismeYesWatermarked exports, template restrictions~$15–$25/moFull templates, branding controls, no watermarks
VistaCreateYesMany premium templates/assets locked~$10/moFull asset library and export options
SnappaYesLimited downloads per month~$10–$15/moUnlimited downloads, full assets
EasilYesLimited exports/templates~$7–$20/moBrand kits, better templates, more downloads
StencilYesMonthly download cap~$9/moUnlimited downloads/assets
SketchNo free tier (trial only)N/A~$9/moApp access, basic features
InkscapeYes (open source)No limitations, but less polishFreeFull app
GIMPYes (open source)No limitations, dated UIFreeFull app
PhotopeaYesAds, some export limits~$5–$7/moRemoves ads, faster performance
PixlrYesAds, restricted features~$5–$10/moAd removal, additional assets
PiktochartYesLimited exports/templates, watermarks~$10–$20/moRemoves watermarks, more templates/assets

1. Canva

I’ve used Canva more times than I can count, usually when I needed something quickly and didn’t want to think too hard. It’s the tool I open when a designer isn’t available or when the output just needs to be “good enough”. Social posts, blog headers, internal slides, one-off visuals. It’s hard to beat for speed.

Where Canva starts to frustrate me is when I try to be precise. Spacing can feel slippery, alignment isn’t always predictable, and once you care about consistency across multiple assets, things get messy fast. It’s also very easy to end up with designs that look like everyone else’s, even if you start with good intentions.

I still use Canva, but I treat it like fast food. Useful, convenient, and occasionally necessary. Just not where I want to spend all my time.

Free tier limits: The free version is generous for basics, but many template packs, brand kit features, and export options (like transparent PNGs and animated exports) are locked behind paid plans.

Paid plan: Canva Pro starts at around $12.99/month (discounts if paid annually). Upgrading gets you brand kits, more assets, Magic Resize, priority support, and a much larger library.

AI features: Canva has built-in image generation and text-to-image, background removal, and layout suggestions. It’s useful for filling gaps quickly, though the outputs can feel generic.

Best for: Non-designers, fast turnaround
Not great for: Complex layouts, brand rigor

2. Figma

Figma was the point where I stopped fighting my tools. The first time I used it properly, it was obvious this wasn’t just a design app, it was a collaboration tool pretending to be one. I’ve used it for landing pages, UI mockups, onboarding flows, and even structured marketing diagrams.

The big difference for me is control. Things line up properly. Components behave predictably. When something looks wrong, it’s usually my fault, not the tool’s. Collaboration is also genuinely good. I can work alongside designers or developers without version chaos.

The tradeoff is learning curve. If you’re coming from Canva, Figma can feel intimidating at first. But once it clicks, everything else starts to feel limiting.

Free tier limits: The free plan lets you have 3 projects and unlimited personal files, but team libraries and advanced versioning require a paid tier.

Paid plan: Figma Professional is about $12/editor/month. That unlocks shared libraries, team projects, and better permissions.

AI features: Figma has plugins with AI assistance (like auto-layout helpers and content generation), but they’re generally third-party rather than baked in.

Best for: SaaS teams, collaboration, precision
Downside: Steeper learning curve than Canva

3. Adobe Express

I came to Adobe Express expecting a Canva clone and ended up using it more than I thought I would. It’s clearly aimed at marketers rather than designers, and that shows in the templates and workflows.

What stood out to me was typography and brand handling. Things tend to look more “finished” without as much effort. If you already trust Adobe as a brand, Express feels like a safer, more professional option than Canva.

That said, it’s still very template-driven. I don’t reach for it when I want full creative freedom. I use it when I want speed, polish, and fewer surprises.

Free tier limits: Free users get a smaller template library and Adobe branding on some exports.

Paid plan: Around $9.99/month (often included with other Adobe subscriptions). Upgrading removes limitations and gives access to premium assets.

AI features: Adobe’s Sensei tech shows up in smart cropping and auto-adjust features. Not as flashy as generative AI, but genuinely useful.

Best for: Marketing teams using Adobe
Downside: Still template-first

4. Affinity Designer

Affinity Designer is what I use when I’m in a “no subscriptions” mood and want serious control. I’ve used it for vector work, icons, and more detailed layouts where Canva simply isn’t capable.

It feels closer to Illustrator than anything else on this list, but without the Adobe tax. Performance is good, exports are reliable, and it doesn’t try to hold your hand.

The downside is collaboration. This is very much a single-player tool. If you’re working with a team, you’ll feel that limitation quickly.

Free tier limits: There isn’t one. It’s a one-time purchase.

Cost: Roughly $55 for desktop (often discounted). You own it outright.

AI features: None built in. All control is manual.

Best for: Designers, vector work
Downside: No real-time collaboration

5. Visme

I’ve used Visme mainly when presentations actually mattered. Not internal slides, but decks that were going in front of clients, partners, or stakeholders where polish and structure mattered more than creative freedom.

Visme feels opinionated in a way Canva doesn’t. It nudges you toward layouts that look like proper reports or presentations, not just big text on colourful backgrounds. That’s helpful when you’re short on time and don’t want to second-guess every design decision.

Where it can feel limiting is when you want to break the structure. It’s not a blank canvas tool. It’s best when you accept its constraints and let it do its thing.

Free tier limits: Watermarked exports and restricted templates.

Paid plan: Starts around $15–$25/month. You get full templates, branding controls, and no watermarks.

AI features: Some automated layout suggestions and asset recommendations, but nothing like generative image AI.

Best for: Presentations, reports
Downside: Free tier is restrictive

6. VistaCreate (formerly Crello)

I first tried VistaCreate out of mild Canva fatigue. After a while, Canva templates all start to look the same, and I wanted something familiar but different.

VistaCreate feels like Canva from a parallel universe. The workflow is almost identical, but the templates skew slightly differently. I’ve used it mainly for social graphics where originality matters just enough to stand out, but not enough to justify full custom design work.

I wouldn’t switch to it permanently if you’re deeply invested in Canva, but it’s a useful alternative when you want a similar experience without the same visual tropes.

Free tier limits: A lot of premium templates and assets are locked.

Paid plan: Around $10/month. Unlocks the full library and team features.

AI features: Limited compared with Canva; mainly search suggestions.

Best for: Social graphics
Downside: Limited depth

7. Snappa

Snappa is what I use when I want zero friction and zero thinking. It’s the closest thing I’ve found to “open app, make thing, close app”.

I’ve used it for quick social posts and blog headers when the output just needed to exist. Not win awards. Not build a brand. Just exist.

The tradeoff is obvious. You hit the ceiling quickly. But if speed is your only requirement, that ceiling might never matter.

Free tier limits: You’re limited to a handful of downloads per month.

Paid plan: Around $10–$15/month. Unlimited downloads and access to assets.

AI features: None.

Best for: Fast social images
Downside: Not flexible

8. Piktochart

I’ve mainly used Piktochart when I needed to turn data into something readable without designing everything from scratch. Reports, infographics, and explanatory visuals are where it shines.

It’s opinionated in a good way. You’re guided toward sensible layouts, which saves time when the goal is clarity rather than creativity. I wouldn’t use it for general design work, but for data storytelling, it’s one of the better options.

Free tier limits: The free plan is usable for trying it out, but you quickly hit limits around premium templates/assets, downloads, and branding (watermarks or Piktochart branding depending on what you export). It’s enough to validate whether the workflow fits you, not enough if you’re publishing client-facing work regularly.

Paid plan: Piktochart’s entry paid plan is typically in the “around $10–$20/month” range depending on current pricing and whether you pay annually. The upgrade is mainly about removing branding/watermarks, unlocking more templates/icons, and getting more export options and project flexibility. If you’re producing infographics more than occasionally, you end up needing paid.

AI capabilities: Piktochart has been adding AI-assisted features (things like helping generate or structure content and speeding up layout creation). In practice, it’s helpful for getting a first draft started, but you still need to sanity-check output and tighten wording.

Best for: Charts, infographics
Downside: Not a general design tool

9. Easil

I’ve seen Easil work well in teams where brand consistency is a real concern. It’s the kind of tool you introduce after someone has already broken the brand once too often.

The strength of Easil is control. You can lock things down, limit what people can change, and still let non-designers produce usable assets. I’ve used it in situations where that balance mattered more than creative freedom.

It’s not a tool I reach for personally unless I’m thinking about governance. But if you manage a team, it makes a lot of sense.

Free tier limits: Very limited downloads and templates.

Paid plan: Around $7–$20/month depending on tier. Gets you brand kits and better templates.

AI features: None.

Best for: Brand consistency
Downside: Less flexible than Figma

10. Stencil

Stencil feels like it was built specifically for bloggers and content marketers. I’ve used it mainly for blog images and simple social graphics.

There’s not much to learn, which is both its strength and its weakness. You’re productive immediately, but you also don’t grow with it.

If your design needs stop at “make image for post”, it does the job. If they grow beyond that, you’ll move on quickly.

Free tier limits: Downloads per month are capped.

Paid plan: Around $9/month. Unlimited assets and downloads.

AI features: None.

Best for: Bloggers
Downside: Very limited

11. Sketch

I used Sketch heavily before Figma became dominant. At the time, it felt like a breakthrough. Clean interface, vector-based, and much better than the alternatives.

The problem is not that Sketch got worse. It’s that everything else moved on. Collaboration, especially, now feels dated compared to Figma.

If you’re already embedded in Sketch on macOS, it still works. But I wouldn’t start there today.

Free tier limits: None; it’s paid only.

Cost: About $9/month for individual plans, more for teams.

AI features: None.

Best for: macOS-only workflows
Downside: Collaboration lagging, and desktop based

12. Inkscape

I’ve used Inkscape when budget was zero and vector work was unavoidable. Logos, icons, simple illustrations.

It’s powerful. There’s no denying that. But it’s also clunky, and you feel that clunkiness constantly. Simple tasks take longer than they should.

I respect it more than I enjoy it. It’s a tool of necessity, not preference.

Free tier limits: Completely free with all features intact. Support and polish are the compromises.

AI features: None.

Best for: Free vector design
Downside: UX feels dated

13. GIMP

GIMP is the tool I open when I need Photoshop-level image editing and don’t want to pay Adobe. I’ve used it for image manipulation, masking, and cleanup tasks.

It can do almost anything Photoshop can, but it makes you work for it. The interface is not forgiving, and the learning curve is real.

Once you know it, it’s powerful. Until then, it can feel hostile.

Free tier limits: Always free, but UI and usability are dated.

AI features: None native; some plugins exist.

Best for: Image manipulation
Downside: Learning curve

14. Photopea

Photopea has saved me more than once. Usually when someone sends a PSD and I don’t have Photoshop handy.

It’s surprisingly capable for a browser-based tool. I wouldn’t use it for heavy work, but for quick edits, it’s excellent.

It’s one of those tools you don’t think about until you need it. Then you’re very glad it exists.

Free tier limits: Ads; some advanced export options are limited.

Paid plan: A small monthly fee (~$5–$7) removes ads and speeds performance.

AI features: None.

Best for: Quick Photoshop-style edits
Downside: Not for large projects

15. Pixlr

Pixlr sits somewhere between “photo editor” and “design tool”. I’ve used it mainly for quick image tweaks when opening a heavier tool felt like overkill.

It’s fast and accessible, but I don’t rely on it for anything critical. More of a utility than a workspace.

Useful to have bookmarked. Not something I’d build a workflow around.

Free tier limits: Ads and limited save options.

Paid plan: Around $5–$10/month. Removes ads and adds some assets.

AI features: Some automatic adjustments and background removal tools.

Best for: Quick edits
Downside: Limited depth

Final thoughts

What all of this has taught me is that there is no “best” graphic design tool in isolation. There are only tools that fit a situation well and tools that fight you.

Canva is fast. Figma is precise. Affinity is powerful. The mistake is expecting one tool to do all three.

If I had to simplify it:

  • Fast and simple: Canva, Snappa, VistaCreate
  • Professional work: Figma, Affinity Designer
  • Presentations and data: Visme, Piktochart

Pick tools based on the job, not the trend.

Missing a tool?

Am I missing an email marketing tool from this list? Let me know!

Related Posts

  • Best AI Video Clipping Tools (2025)
  • Best AI Presentation Builders (2025)
  • Best Marketing Automation Tools for SaaS Growth

Disclosure: Editorial rankings are based on hands-on testing, evaluation of public user feedback, and real-world usage.

Reviewed: The Best Email Marketing Tools (2026 Update)

Updated: January 2026

Have you been tempted to switch email platforms because you feel your newsletters could be doing more?

Maybe you want to automate onboarding emails.

Maybe you want to nurture leads or re-engage past users.

Or maybe you’re tired of the “spray and pray” approach and need something that actually ties into your customer journey.

At StreamAlive, we use Substack for newsletters and Customer.io for transactional emails. Substack makes it simple to publish thought leadership and updates to our audience, while Customer.io handles the system messages triggered from the product. But we often get asked why we don’t just use tools like Mailchimp, Constant Contact, or Brevo for everything.

The short answer: those tools are built for different kinds of companies. Some want to focus on nurturing leads. Some focus on simple newsletters. Others focus on e-commerce to sell to existing customers. Some aim to be mini-CRMs. The key is knowing which type of email marketing tool fits your business model and maturity stage.

Make sure you’re looking at the right email marketing tools

Before you continue, let’s make sure you’re looking at the right set of tools for your email marketing requirements.

Email Campaign TypeWhat It’s Used ForTypical GoalsBest Tool CategoriesExamples of ToolsWhy These Fit
Newsletters & Content UpdatesRegular communication to build audience trust and share updates, insights, or thought leadershipBuild brand awareness, nurture relationships, stay top of mindEmail Newsletter PlatformsSubstack, MailerLite, MoosendPrioritise simplicity, clean templates, and low friction. Designed for consistent, one-to-many content delivery.
Lead Nurture & Marketing AutomationAutomated drip campaigns that guide leads from signup to activationEducate, convert, retain usersEmail Automation PlatformsBrevo, GetResponse, ActiveCampaign, HubSpotAllow multi-step workflows, conditional logic, segmentation, and CRM sync for personalised sequences.
Transactional & Product EmailsSystem-triggered emails based on user behaviour (e.g. password resets, onboarding steps, usage summaries)Drive engagement, improve product activationTransactional Email PlatformsCustomer.io, Postmark, SendGrid, Amazon SESHandle high reliability and deliverability for one-to-one product messages. API-first and event-driven.
Promotional & E-Commerce CampaignsSales-focused announcements or offers sent to existing customersIncrease conversions, repeat purchasesE-Commerce Email PlatformsKlaviyo, Omnisend, Mailchimp, BrevoDeep integrations with Shopify, WooCommerce, etc. Offer product recommendations and cart recovery.
Cold Outreach & ProspectingOutbound campaigns to new leads not yet opted inGenerate meetings, demos, or repliesCold Email ToolsInstantly.ai, Woodpecker, Apollo, LemlistBuilt for deliverability, sequencing, and personalisation at scale; not for opt-in subscribers.
Customer Announcements & Internal UpdatesCompany or product announcements, team updates, internal communicationsInform and engage existing audiencesGeneral Email Marketing PlatformsConstant Contact, MailchimpEasy to use, good for one-off updates, moderate segmentation, and brand consistency.

How to use this framework

  • If your company’s focus is audience building or brand trust, start with a newsletter platform.
  • If your focus is lead conversion or lifecycle management, use an automation-first tool.
  • If you’re in e-commerce, go for a sales-centric platform.
  • If you need reliability for product-triggered emails, choose a transactional provider.
  • And if your goal is outreach to net-new prospects, cold-email tools are a separate category entirely.

Why this matters

Too many companies use one tool for every email need and end up frustrated when it doesn’t scale or integrate properly. At StreamAlive, for instance, Substack handles newsletters beautifully but isn’t suited for product-triggered messages or product promotions that’s why Customer.io fills that gap. Similarly, a tool like Brevo could bridge the gap between newsletters, lead nurture automation and webinar invites, but it would still fall short for transactional reliability or cold-outreach use cases.

The best email marketing platforms

Here are six standout platforms I’ve tested or evaluated, with deeper takes on features, pricing, user feedback, and which kinds of organisations each suits best.

Tools covered:

  • Constant Contact
  • Mailchimp
  • GetResponse
  • Brevo (formerly Sendinblue)
  • MailerLite
  • Moosend

How I selected the platforms

I compared each tool on:

  • Automation depth — how well it handles drip campaigns, triggers, and sequences
  • Ease of setup and design — how quickly a non-designer can send a polished email
  • CRM and integration fit — compatibility with marketing stacks, landing pages, and forms
  • List management and segmentation — how effectively you can target by persona or stage
  • Analytics and deliverability — open, click, and conversion tracking quality
  • Pricing and scalability — real cost per contact or email at small and growing volumes
  • User sentiment — what real users praise and dislike in daily use

Pricing snapshot

PlatformFree / Trial TierEntry Paid PlanWhat You Get in Entry Tier
Constant Contact14-day trialFrom $12/monthBasic newsletters, templates, contact list management, simple automation
MailchimpFree up to 250 contactsFrom $13/monthNewsletter templates, scheduling, basic automation, analytics
GetResponse30-day trialFrom $19/monthAutomation workflows, funnels, landing pages, lead nurturing
Brevo (Sendinblue)Free plan (300 emails/day)From $9/monthEmail + SMS, workflows, segmentation, CRM, marketing automation
MailerLiteFree up to 500 subscribersFrom $10/monthSimple newsletters, drag-and-drop builder, basic automation
Moosend30-day free trialFrom ~$9/monthEmail campaigns, automation, segmentation, analytics

Note: Pricing is approximate and subject to change. Always check vendor sites for the latest details.

1) Constant Contact: The easiest way to start sending professional emails

I tried Constant Contact years ago when I worked with smaller B2B clients who didn’t have a marketing tech stack. It’s one of those tools that feels like it was built for people who don’t really want to think about email marketing software. You log in, pick a template, drop in your logo and text, and you’re ready to send.

That simplicity is its strength. For small companies that just need to send newsletters or customer updates, Constant Contact makes sense. It’s easy to use, the templates look clean, and it’s hard to break anything. Deliverability is solid, and it has direct integrations with things like Eventbrite or Shopify if you want to promote events or online stores.

But once you start caring about segmentation or drip campaigns, you quickly hit its limits. I remember trying to set up a simple “if opened → send follow-up” flow and realising the automation options were barebones. Reporting is basic too, which makes it hard to learn much from campaigns beyond open and click rates.

Constant Contact Pricing & Value

Plans start around $12/month, which looks fair until you realise how quickly costs rise once your contact list grows. The entry tier covers basic newsletters, but automation and segmentation require upgrading fast, which erodes the early affordability. For what you get, it feels expensive once you reach scale. Solid and reliable, but limited sophistication for the price.

What Constant Contact Does Well

  • Simple drag-and-drop builder, great for non-technical users
  • Reliable sending and good deliverability reputation
  • Integrations for event invites, small-scale e-commerce, and surveys
  • Helpful support for first-time users

Where Constant Contact Can Bite You

  • Automation and segmentation are too basic for B2B lead nurture
  • The editor feels dated and limited compared to newer tools
  • Pricing climbs quickly as lists grow
  • Reporting is minimal which fine for newsletters, poor for analysis

Verdict

Constant Contact is fine if you’re a local business, nonprofit, or consultant who just needs to stay in touch with customers. It’s not a growth or conversion platform. For a SaaS or content-driven business, you’ll outgrow it quickly once you need to segment audiences or run automated onboarding and follow-ups.

2) Mailchimp: Still the all-rounder for small teams

Mailchimp has been around forever, and honestly, it’s still good at what it does. At eG Innovations, our regional teams used it for newsletters and webinar invites because it was fast to set up and everyone could use it without training. You can design nice-looking emails, clone campaigns, and schedule follow-ups without involving a developer.

Mailchimp also scales a little better than Constant Contact. It has more templates, slightly stronger automation, and better analytics. I like that you can segment users by behaviour or tags, which is enough for most small-team marketing.

Where it struggles is when you start layering multiple automations, like onboarding sequences, webinar invites, and product updates. The UI gets clunky, and costs climb fast as your list grows. I’ve seen companies hit $400 a month just to keep sending weekly emails to a 50,000-contact list.

Mailchimp Pricing & Value

Mailchimp starts at roughly $13/month and scales by contact count, not usage, which means costs creep up even if you don’t email often. You’re paying for polish and familiarity rather than raw functionality. It’s good value for small lists and simple automation but quickly becomes overpriced for larger audiences or complex workflows.

What Mailchimp Does Well

  • Beautiful templates and a friendly UI
  • Decent analytics for campaign performance
  • Basic automation for welcome sequences or lead magnets
  • Integrations with tools like Stripe, Typeform, and WordPress

Where Mailchimp Can Bite You

  • Expensive once you have more than a few thousand contacts
  • Automation is fine for beginners but limited for complex logic
  • List management is rigid, making advanced segmentation tricky
  • Support and deliverability vary by plan

Verdict

Mailchimp remains the most balanced “default” choice. It’s perfect for small or mid-size teams that need something quick, reliable, and familiar for newsletters, announcements, and light automation. But if your marketing matures beyond that, for example, if you want triggered emails tied to product behaviour or sales stages, then you’ll need to graduate to something more flexible like Brevo or GetResponse.

3) GetResponse: a step up when you want automation and funnel-style email marketing

I evaluated GetResponse a while back when I was at Unmetric. We were looking for something cheaper than Hubspot, but ultimately chose Mailchimp over GetResponse. What intrigued me was that it’s not just an email sender: it feels closer to a lightweight marketing automation platform. You get landing pages, signup forms, workflows, and automation sequences all in one place. For a product with drip onboarding or lead nurture needs, that’s useful.

I tried a simple onboarding flow using GetResponse: new trial user signs up → welcome email → feature tour → follow-up check-in. It was easy enough to set up and took away manual follow-up burden. The platform isn’t as heavyweight as a full marketing automation suite, but it hits a good middle ground.

If you run webinars, product announcements, or need to nurture leads over time, GetResponse gives flexibility at a reasonable price point. That makes it a realistic alternative when you need more than a newsletter tool but don’t need an enterprise-grade CRM.

GetResponse Pricing & Value

Starting at $19/month, GetResponse feels like strong value for what you get: automation, funnels, and landing pages in one package. It’s cheaper than HubSpot or ActiveCampaign at similar capability levels. Costs climb with list size and advanced features, but it remains one of the better mid-market deals for B2B automation.

What GetResponse does well

  • Automation workflows and funnel support (multi-step email sequences, triggers, landing pages)
  • Unlimited monthly email sending even on entry-level plan so it’s scalable for growing lists
  • Built-in tools for list capture, opt-ins, and conversion-focused outreach (good for SaaS onboarding, lead nurture)
  • Decent balance between features and cost, so not overkill if your needs are moderate

Where GetResponse can bite you

  • UI and feature set feels more complex than simple newsletter tools with some learning curve
  • If you only send occasional newsletters, you may not utilise its full potential (and pay for unused features)
  • As automation and campaigns get more sophisticated, you might hit limitations compared to enterprise tools

Verdict

GetResponse is the right “next-level” email marketing tool for companies that need more than newsletters but aren’t ready for a full marketing stack. For B2B SaaS companies with drip campaigns, feature announcements, or onboarding flows, it offers a sensible balance of cost, flexibility, and automation.

4) Brevo (formerly Sendinblue): a flexible, volume-based platform for growing businesses

I used Brevo when I was helping a small non-tech B2B company. What stood out was its flexibility: unlike contact-based pricing, Brevo charges based on volume of emails. That made sense when our list was large but we were only emailing occasionally. It felt cost-efficient and forgiving.

On top of that, Brevo felt more modern than many legacy tools. The interface was easy to navigate, campaign creation was smooth, and automation rules were surprisingly capable for the price. We were able to manage newsletters, promotional blasts, and customer updates all in one place without overpaying for unused features.

For businesses that don’t send frequent blasts but want the option to scale up later, or who combine email with occasional SMS or multi-channel touchpoints, Brevo offers a very sensible entry point.

Brevo Pricing & Value

Brevo’s pricing starts at just $9/month and is one of the fairest models available. You’re not penalised for having a large list, only for how much you actually send. It’s excellent value for companies with big databases but irregular campaigns, though heavy senders may eventually find flat-fee plans cheaper.

What Brevo does well

  • Flexible pricing based on email volume rather than contact count makes it cost-efficient for large but lightly-treated lists
  • Solid automation and segmentation capabilities for the price, so decent for email + light marketing workflows
  • Clean UI and straightforward workflow for sending campaigns or building simple sequences
  • Good for mixed-use: newsletters, product updates, promotional campaigns, and occasional multi-channel outreach (email + other channels)

Where Brevo can bite you

  • Free plan limits number of daily sends, not ideal for high-volume email windows
  • As you scale further (frequent emailing, complex segmentation, heavy automation), you may outgrow the mid-tier plans
  • Some features may remain too basic compared to enterprise-grade marketing platforms

Verdict

Brevo is a smart choice for small-to-mid businesses, startups, or any company that values flexibility and wants to avoid paying hefty contact-based fees. It hits a nice sweet spot between affordability and functionality. If you want to run occasional newsletters, customer updates, or moderate marketing campaigns with room to grow, Brevo gives you everything you need without much overhead.

5) MailerLite: clean, lightweight, and great for simple newsletters or content-based outreach

I looked at MailerLite when evaluating tools for a small, non-tech B2B company but ultimately went with Brevo. What I liked was how smooth and minimal the experience was. No bloatware, just a nice editor, clean templates, and predictable pricing. If your main goal is to publish newsletters, blog-update emails, or regular content digests then MailerLite delivers without overcomplicating things.

I used it to send monthly digests and announcements. The learning curve was minimal, and I rarely needed to touch advanced settings. That made it ideal when I didn’t want marketing overhead but just wanted to deliver content consistently.

For small teams or solo creators, MailerLite gives the essentials. It’s not feature-rich compared to automation-first tools, but that can also be a strength: there are fewer distractions, fewer settings to manage, and less risk of messing up complex workflows.

MailerLite Pricing & Value

MailerLite’s paid plan starts at $10/month to send unlimited emails to 500 subscribers, offering excellent value for creators or small businesses. The free plan also allows upto 500 subscribers but limits you to sending 12,000 emails a month, which is the equivalent of sending 24 emails to your entire list each month which no business is likely to be doing. MailerLite is inexpensive but doesn’t strip out essentials like templates and automation. As your subscriber count grows, it remains affordable longer than most competitors, though you’ll outgrow its simplicity before you outspend it.

What MailerLite does well

  • Simple, clean interface and easy setup with very low friction for content-based newsletters or small-scale campaigns
  • Affordable pricing and free plan exists which good for startups or side projects that don’t yet need heavy automation or segmentation
  • Enough functionality for typical newsletter needs: templates, campaign scheduling, basic forms, and segmentation for small audiences

Where MailerLite can bite you

  • Lacks advanced automation vs. dedicated marketing tools so not ideal for lead nurturing or behavioural-triggered flows
  • Limited segmentation and reporting for larger or more complex contact bases
  • As needs scale (e.g. you need onboarding flows, multiple segmentation layers, automated triggers), you may outgrow the platform

Verdict

MailerLite is a great “lean and mean” tool for content-heavy or small-scale email work. If your aim is regular newsletters, occasional updates, or simple outreach within a limited budget then MailerLite is hard to beat. For more complex marketing automation or scaling, you’ll outgrow it, but until then it’s efficient and easy.

6) Moosend: budget-friendly automation for small businesses exploring email marketing

I tested Moosend briefly for StreamAlive but ultimately decided to stick with Substack. What I liked instantly was that even at a low price you get more automation and segmentation than many “starter” tools. It felt like a bridge between barebones newsletter tools and full-fledged marketing suites.

I set up a simple drip campaign tied to purchase behaviour and saw decent results. For the price point, I didn’t expect much, but Moosend delivered enough to make me take it seriously as a budget automation tool.

For small businesses or bootstrapped startups that need automation but don’t want to pay big, Moosend offers a realistic entry point. It’s not flashy, but it gets the job done.

Moosend Pricing & Value

At $9/month to start, Moosend is one of the cheapest ways to access real automation. It undercuts most alternatives while offering enough sophistication for startups and freelancers. Value drops as needs become more complex, but for lightweight automation and newsletters, it punches above its price point.

What Moosend does well

  • Very affordable for basic automation and segmentation which makes it good value for small budgets or small businesses
  • Automation workflows and segmentation that can handle simple nurture flows or drip campaigns without much complexity
  • Clean, straightforward email builder and quick to launch campaigns

Where Moosend can bite you

  • Limited advanced features with fewer integrations, less sophisticated analytics and CRM-level functionality than bigger tools
  • May feel basic if you try to scale up to larger lists or more complex workflows
  • Support and ecosystem is lighter than more established platforms

Verdict

Moosend is a smart starting point for small teams, side projects, or early-stage businesses. If you want some automation but have minimal budget, it’s a workhorse for basic drip campaigns or marketing emails. But as you grow, you’ll likely need to migrate to a more robust platform.

Final picks & what you should use

The best email-marketing tool depends on your use case and stage of growth.

Need / Use CaseBest PlatformWhy This Platform Wins Over Others
Simple newsletters and announcementsConstant ContactDesigned for small organisations that just need to send updates without complex workflows. Reliable deliverability and quick setup.
All-round balance of ease and powerMailchimpBroad feature coverage with solid templates, analytics, and integrations. Good default for SMB marketing teams.
Lead nurture and conversion funnelsGetResponseCombines automation, funnels, and landing pages in one tool. Ideal for B2B and SaaS lead nurturing.
Multi-channel marketing on a budgetBrevo (Sendinblue)Offers automation, SMS, CRM, and fair pricing by email volume. Great for teams needing cross-channel workflows.
Low-cost newsletter simplicityMailerLiteBest for creators or small businesses sending simple newsletters without needing CRM or complex automation.
Affordable automation testing groundMoosendCheapest entry point to real automation and segmentation. Good for experimenting before scaling to a larger platform.

Overall thoughts: which tool for what kind of company

Based on my own background (using Substack for content newsletters and Customer.io for product-related emails), here’s how I see each tool fitting into a realistic email marketing strategy:

  • If you just need simple newsletters or occasional announcements: Constant Contact, MailerLite, or Moosend are easy, affordable, and low-maintenance.
  • If you want a reliable, general-purpose email platform that balances usability and modest marketing needs: Mailchimp remains a safe generalist, especially for small-to-mid teams not ready for heavy automation.
  • If you plan to grow marketing efforts, run drip campaigns, lead nurture, or use email as an ongoing funnel channel: GetResponse and Brevo stand out. GetResponse brings automation and funnel-style email flows. Brevo adds flexibility with volume-based pricing and is good if you expect fluctuations in send frequency.
  • If you’re budget-conscious but want automation without feature overload: Moosend and MailerLite give a “lean startup” email approach: simple, manageable, and scalable until you outgrow them.

For a SaaS or B2B company, if you foresee running lead nurture flows, onboarding sequences, product announcements, and occasional content newsletters, I’d lean toward Brevo or GetResponse. If you just want to send occasional newsletters or content updates and want minimal fuss, then MailerLite or Moosend keep it simple and cheap.

If you’re happy with your current split (Substack + transactional email tool) and only occasionally need marketing blasts, sticking with that or using a light tool like MailerLite might still make sense.

And if you’re a company like StreamAlive (using Substack for editorial newsletters and Customer.io for product triggers) these tools may overlap rather than replace your existing setup. Choose based on where you want automation to live: in your marketing funnel or your content workflow.


Missing a tool?

Am I missing an email marketing tool from this list? Let me know!

Related Posts

  • Best AI Video Clipping Tools (2025)
  • Best AI Presentation Builders (2025)
  • Best Marketing Automation Tools for SaaS Growth

Disclosure: Editorial rankings are based on hands-on testing, evaluation of public user feedback, and real-world usage.