I Studied 83 SaaS Businesses That Died in 2025. They All Made the Same 5 Mistakes.

There's a graveyard of SaaS products that launched in the last eighteen months, got a burst of attention, and then quietly disappeared. Domains expired. Twitter accounts went silent. Stripe dashboards flatlined.

I went through shutdown announcements, postmortems on Indie Hackers and Hacker News, archived Product Hunt launches that now 404, and public revenue dashboards that dropped to zero. In total, I catalogued 83 SaaS products that clearly died — meaning they either publicly announced shutdown, let their domains lapse, or showed zero activity for 6+ months after launch.

The surprising part wasn't that they failed. Most software products fail. The surprising part was how predictable the failures were. Nearly every single one fell into one of five patterns. And understanding those patterns doesn't just help you avoid failure — it reveals exactly where the real opportunities are hiding right now.

Where the Data Comes From

Let me be clear about methodology. I didn't interview anyone. I didn't call founders. I analyzed publicly available information: shutdown posts on Reddit and Indie Hackers, Product Hunt launches with dead links, open startup dashboards showing revenue decline to zero, and Hacker News "Show HN" posts where the product no longer exists.

Of the 83 products I tracked, 61 were solo-founder or two-person teams. 74 were bootstrapped. The median lifespan from launch to death was 7 months. Most never crossed $2K MRR.

These weren't bad founders. Many were talented developers who shipped fast and built polished products. They just walked into the same traps over and over.

Mistake #1: Building an AI Wrapper With No Defensible Layer

This was the single most common cause of death, accounting for roughly 30 of the 83 failures.

The pattern: take an OpenAI or Anthropic API, build a nice UI around it for a specific use case, launch on Product Hunt, get a spike of traffic, then watch as users realize they can do the same thing with a well-crafted prompt in ChatGPT or Claude.

The specific categories that got massacred:

• AI email writers. At least 8 of the dead products were some variation of "paste your context, get a professional email." The problem is that every LLM does this natively now, and Gmail and Outlook have built-in AI drafting.
• AI blog post generators. Another 7 casualties. Generic content generation is a commodity. The products that survived in this space — and there are a few — all had a specific distribution advantage or data moat, like SEO tools that combine generation with keyword intelligence.
• AI resume builders. 5 dead products. Same issue: the LLMs got good enough at this that a standalone tool felt redundant.

The lesson isn't "don't build with AI." The lesson is that the AI layer alone isn't the product. The products that survive have something the API can't replicate: proprietary data, a workflow that's deeply integrated into where users already work, or a network effect.

Consider the difference between a generic "AI writing assistant" and something like an AI tool that ingests your company's entire support ticket history, learns your brand voice from thousands of real interactions, and auto-drafts responses that match your specific escalation policies. The second one has a data moat that grows with usage. The first one is a ChatGPT prompt with extra steps.

If you're building with AI right now — and you should be — the question to ask yourself is: "What does my product know or do that the foundation model can't replicate on its own?" If the answer is "a nicer UI," you're in trouble. If the answer involves proprietary data, unique integrations, or compounding network effects, you have something.

This connects directly to something I've written about before: the SaaS tools that charge $500+/month all exploit a specific blind spot, and that blind spot is almost always about owning a data layer or workflow that customers can't easily replicate.

Mistake #2: Solving a Pain Point That's Real but Worth $0

This one is sneaky because the founders did validate demand. People genuinely wanted the product. They just didn't want to pay for it.

About 19 of the 83 dead products fell into this category. The pattern: the founder identifies a real frustration, builds a clean solution, gets enthusiastic beta users, launches with a $10-20/month price tag, and watches as conversion from free to paid hovers around 1-2%. Not enough to sustain the business.

The categories where this happened most:

• Personal productivity tools. Habit trackers, journaling apps, personal dashboards. Users loved them. Users did not love paying for them. The willingness-to-pay ceiling for personal productivity software is brutally low unless you're Notion-level.
• Developer tools for minor annoyances. Things like "a prettier way to manage your .env files" or "a dashboard for your GitHub stars." Developers appreciated the craft but didn't reach for their wallets.
• Social media scheduling for individuals. Not for agencies or businesses — for individual creators with small followings. The pain is real, but the budget isn't there.

The underlying principle: pain intensity and willingness to pay are different axes. Someone can be genuinely frustrated by a problem and still not value the solution at $10/month. This tends to happen when the pain is intermittent (not daily), when the user isn't spending money in the context where the pain occurs, or when free alternatives exist that are "good enough" even if they're worse.

The flip side reveals an opportunity pattern. The SaaS products that thrive tend to solve problems where money is already flowing. Billing software works because it's adjacent to revenue. Compliance tools work because the cost of non-compliance is enormous. Sales tools work because they're directly tied to deals closing.

I track these kinds of market gaps at SaasOpportunities, and the single biggest filter I use is: "Is the buyer already spending money to solve this problem in a worse way?" If they're using spreadsheets, hiring consultants, or paying for a clunky legacy tool, that's a green light. If they're just mildly annoyed and living with it, that's a red flag.

The filters that predict SaaS success almost always come back to this: willingness to pay is the hardest thing to manufacture and the easiest thing to validate before you write a line of code.

Mistake #3: Launching Into a Category Where the Winner Already Won

About 14 of the 83 dead products launched directly into a market where a dominant player had already established strong network effects or switching costs.

This is different from entering a competitive market. Competitive markets are fine — competition validates demand. The problem is entering a market where the competitive dynamics have already tipped in favor of an incumbent, and the only way to win users is to be dramatically better on every dimension simultaneously.

Examples from the graveyard:

• Project management tools that were "like Notion but simpler" or "like Linear but for non-engineers." The switching costs in project management are enormous because your entire team's context lives in the tool. Being 20% better isn't enough to overcome the migration pain.
• CRM alternatives that were "AI-native" but still required you to manually import contacts, set up pipelines, and rebuild your workflow from scratch. The AI features were genuinely good, but the switching cost was a brick wall.
• Design tools positioned against Figma. Even with interesting AI-powered features, the collaborative network effects of Figma (your whole team is already there, your design system is already built, your developers already know how to inspect Figma files) made switching nearly impossible.

The pattern is clear: if the incumbent's advantage is primarily about network effects or accumulated user data, incremental improvements won't dislodge them. You need to either find a segment the incumbent is ignoring entirely, or change the game so fundamentally that the incumbent's advantages become irrelevant.

The SaaS products that succeeded in competitive-looking markets did something specific: they picked a narrow vertical and went deep. Instead of "a better CRM," they built a CRM for real estate wholesalers with built-in skip tracing and deal analysis. Instead of "a better project management tool," they built workflow software for post-production studios with frame-accurate timeline integration.

This is the vertical SaaS playbook, and it works because it sidesteps the network effects problem entirely. A real estate wholesaler doesn't care that Salesforce has millions of users. They care that the vertical tool speaks their language and solves their specific workflow.

The niches that quietly crossed $50K MRR in 2025 are almost all vertical plays that look bizarre from the outside but are deeply valuable to the specific audience they serve.

Mistake #4: Pricing for Consumers When the Real Buyer Is a Business

This was a more subtle failure mode, but it showed up in about 11 of the 83 dead products. The founder built something that businesses would gladly pay $200-500/month for, but priced it at $15/month and marketed it to individuals.

Real examples from the graveyard:

• An AI-powered contract analysis tool priced at $12/month for freelancers. Freelancers don't review enough contracts to justify a subscription. But law firms, real estate agencies, and procurement teams review contracts daily and would pay 20x that price without blinking.
• A data visualization tool priced at $9/month for "anyone who works with data." The individual hobbyist market for data viz is tiny. But marketing agencies, consulting firms, and internal analytics teams have real budgets for tools that make their deliverables look professional.
• An AI meeting summarizer priced at $8/month per user. Individual users can get meeting summaries from dozens of free tools. But companies with compliance requirements around meeting documentation — financial services, healthcare, legal — would pay enterprise pricing for something that integrates with their existing stack and meets their regulatory needs.

The mistake isn't just leaving money on the table. The low price point actually makes the business unviable because consumer acquisition costs are high, churn is brutal (consumers cancel subscriptions casually), and the support burden per dollar of revenue is unsustainable.

When I see a product die at $15/month, I often think: that exact same product, repositioned for a specific business buyer at $200/month with the right integrations and compliance features, might have been a real business.

This connects to a broader insight about what separates profitable SaaS ideas from failed ones: the product is often fine. The positioning and pricing are what kill it.

Mistake #5: Building for a Trend Instead of a Workflow

The final pattern accounted for about 9 of the 83 failures, and it's the most interesting one because these founders were often the most technically impressive.

They built products around a trending technology or concept rather than around an existing workflow that needed improvement. The technology was the pitch, not the problem it solved.

Examples:

• "Web3 analytics dashboards" that launched right as crypto interest was peaking. The dashboards were technically sophisticated, but they were built for a market of speculators whose attention moved on. The products that survived in crypto analytics were the ones serving institutional traders and compliance teams — people with a durable workflow need, not a trend-driven curiosity.
• "AI agent platforms" where you could build custom AI agents for... anything. The technology was genuinely cool. But "build an AI agent for anything" is a solution looking for a problem. The agent platforms that are actually growing in 2025 are the ones that picked a specific workflow — like "AI agents that handle inbound sales qualification for B2B companies" — and went deep on that one use case.
• "Metaverse" collaboration tools from the 2023-2024 era. The spatial computing technology was impressive. The problem was that nobody's actual workflow required a 3D virtual office. The pain point (remote collaboration) was real, but the solution was a technology in search of a user, not a user need in search of a solution.

The lesson: trends create real opportunities, but the opportunity is never "the trend itself as a product." The opportunity is taking a durable workflow problem and solving it better using the new technology that the trend makes possible.

AI is the current mega-trend, and this mistake is happening at massive scale right now. Thousands of products are being built where the pitch is essentially "AI for X" and the X is vague. The products that will survive are the ones where X is a specific, painful, daily workflow that someone is already spending money to manage.

What the Survivors Have in Common

Studying failures is useful, but the real payoff comes from inverting the patterns to see what success looks like.

Across the same time period, I also looked at products that launched in similar categories but survived and grew. The surviving products consistently did the opposite of the five mistakes:

They built proprietary data layers, not just UI wrappers. The AI products that survived accumulated user data that made the product better over time. Each customer's usage made the AI more valuable, creating a flywheel that a new competitor couldn't replicate on day one.

They sold to buyers with budgets, not users with opinions. The survivors charged $50-500/month and sold to businesses or professionals who had a line item in their budget for this type of tool. They didn't try to convince consumers to add another $10/month subscription.

They picked narrow verticals and went absurdly deep. Instead of competing with Notion, they built the operating system for a specific type of business. The market looked small from the outside, but the willingness to pay was enormous because the tool was built exactly for their workflow. This is the same pattern behind the bizarre niches that are quietly printing money.

They attached to existing workflows instead of asking users to adopt new ones. The survivors integrated into tools people already used daily — Slack, email, spreadsheets, existing industry software. They reduced friction in an existing process rather than asking users to change their behavior.

They solved problems that existed before the trend and will exist after it. The AI-powered products that survived weren't "AI products." They were compliance tools, sales tools, operations tools, and analytics tools that happened to use AI to be dramatically better than the previous generation of solutions.

The Opportunity Map That Emerges

When you overlay the failure patterns with the survival patterns, a clear opportunity map emerges for what to build right now.

The highest-probability opportunities share these characteristics:

Vertical AI for regulated industries. Healthcare, finance, legal, insurance, and construction all have painful daily workflows, high willingness to pay, and regulatory requirements that prevent users from just using ChatGPT directly. A compliance-aware AI tool for a specific regulated workflow is almost the opposite of an AI wrapper — it requires deep domain knowledge, careful data handling, and specific integrations that create a real moat.

Workflow middleware that connects existing tools. Many businesses run on 5-10 SaaS tools that don't talk to each other well. The opportunity isn't replacing any of those tools — it's building the connective tissue between them for a specific industry or function. Think: a tool that automatically syncs data between a construction company's project management software, their accounting system, and their compliance documentation, with AI handling the translation between formats.

B2B tools priced at $200-500/month for the "missing middle." Enterprise software costs $2,000+/month. Consumer tools cost $10-20/month. There's a massive gap in the $200-500/month range for tools that serve small-to-medium businesses with specific professional needs. The analysis of SaaS pricing pages shows this pricing tier is where solo founders can build sustainable businesses without needing enterprise sales teams.

AI-powered tools for non-obvious back-office functions. Everyone is building AI for content creation and customer-facing use cases. Almost nobody is building AI for insurance claims processing, inventory reconciliation, permit applications, or supplier qualification. These are unglamorous workflows with enormous volume, high error rates, and clear ROI when automated.

How to Apply This Before You Build

Before you commit to your next SaaS idea, run it through the five failure filters:

1. The wrapper test. Can a user get 80% of your product's value by using an LLM directly with a good prompt? If yes, you need a deeper moat — proprietary data, integrations, or a workflow layer that the API alone can't provide.

2. The willingness-to-pay test. Is your target user already spending money to solve this problem? Not "would they theoretically pay" — are they currently paying for a consultant, a legacy tool, or a manual process that your product replaces? If the answer is no, the pain probably isn't intense enough to monetize.

3. The switching cost test. Are you entering a market where the incumbent's advantage comes from network effects or accumulated data? If yes, you need a fundamentally different approach — usually a vertical niche or a new workflow paradigm — not just a better version of the same thing.

4. The buyer test. Is your buyer a business with a budget, or a consumer who will churn after the free trial? If you're building something that businesses would value, price it for businesses and market it to businesses. Don't default to consumer pricing because it feels safer.

5. The workflow test. Does your product solve a problem that existed before the current technology trend and will exist after it? If your product only makes sense in the context of a hype cycle, you're building on sand.

A product that passes all five filters isn't guaranteed to succeed. But a product that fails even one of them is swimming against a strong current.

The Graveyard Is the Best Market Research You'll Ever Do

Most founders study success stories. They read about companies that grew to $10M ARR and try to reverse-engineer the playbook. That's useful, but it's subject to survivorship bias. You're only seeing the winners.

Studying failures gives you the complementary view. It shows you the minefields that the success stories happened to avoid — sometimes through skill, sometimes through luck.

The 83 products I studied weren't built by incompetent people. Many were technically excellent. Several had beautiful designs and smooth onboarding flows. A few even had genuine early traction before the wheels came off.

They failed because they walked into structural traps that no amount of execution quality could overcome. An AI wrapper in a commoditizing market will die no matter how good the UX is. A $12/month tool for a problem that businesses would pay $300/month to solve will die no matter how many Product Hunt upvotes it gets.

The good news is that every one of these failure patterns has a corresponding opportunity pattern. Every dead AI wrapper reveals a market that wants AI-powered solutions but needs something deeper than a prompt playground. Every mispriced consumer tool reveals a B2B opportunity hiding in plain sight. Every failed horizontal play reveals a vertical niche that's underserved and willing to pay.

The founders who win in 2025 and 2026 won't be the ones who build the fastest or ship the most features. They'll be the ones who study the graveyard, understand why products die, and build the thing that the dead products were almost — but not quite — right about.

Start by picking one of the failure patterns that resonates with a market you know. Find the dead products in that space. Read their postmortems. Look at what they got right and where they went wrong. Then build the version that fixes the structural mistake.

The best SaaS ideas aren't invented from scratch. They're excavated from the wreckage of products that got the hard part right — identifying real pain — but got the business model, positioning, or market wrong.

The graveyard is full of almost-right ideas. Your job is to figure out what "right" actually looks like.