Everyone counts the dead. We measured the living. The result is less dramatic than 90% failure and considerably more useful.
Almost every startup failure statistic you have read is a quote of a quote. The 90% figure gets repeated without a primary source. The "42% fail from no market need" line traces back to a single CB Insights post-mortem study. And a growing number of validation tools now claim that validated ideas succeed 60% to 70% of the time, a number that appears in no published research we could locate.
So we measured something different. Instead of counting startups that died, we measured what the survivors actually earn. BigIdeasDB tracks revenue for 8,600+ startups, and the distribution is the real story: most startups do not explode, and most do not collapse either. They launch, they work, and they earn almost nothing.
It comes from nowhere in particular. The 90% figure circulates as received wisdom, and when you trace citations they tend to loop back to other blog posts rather than to a dataset. The closest thing to an authoritative survival number is the U.S. Bureau of Labor Statistics Business Employment Dynamics series, which tracks new employer establishments over time. It puts roughly one in five out of business within the first year and about half gone by year five. That is a serious number, but it is not 90%, and it covers all new employer businesses rather than software startups specifically.
The gap between 50% and 90% matters because the two numbers imply different behavior. A 90% failure rate suggests startups are lottery tickets. A 50% five year survival rate suggests something more mundane and more fixable, which is that a lot of businesses run for years without ever becoming meaningful.
Our data supports the mundane reading. The problem is rarely a dramatic collapse. The problem is that the product ships and nothing happens. As one founder put it after two months of full time building:
"Users: 0. Revenue: $0. Product-market fit: LOL no." – r/indiehackers
Failure databases count deaths. That is a survivorship problem in reverse: you learn a great deal about companies that raised money and then imploded publicly, and almost nothing about the far larger population that quietly never worked.
BigIdeasDB approaches it from the other side. Our revenue intelligence dataset tracks 8,600+ startups with verified revenue signals rather than self-reported claims, which lets us describe the whole distribution instead of just the tail. We cross-referenced that against 650+ live acquisition listings in our SellSide dataset, 30,000+ companies already taking payment in the Stripe Index, and 17,000+ funded companies in the Funded database.
The reason this matters for anyone reading a failure statistic: the question you actually care about is not "will I shut down." It is "will this ever make money." Those have very different answers.
Here is the full percentile spread across every tracked startup that reports any revenue at all. This is the number that should replace the 90% talking point.
| Percentile | Monthly revenue | What it means |
|---|---|---|
| 25th | $29 | A few hobby subscriptions |
| 50th (median) | $145 | Does not cover hosting plus tools |
| 75th | $894 | A meaningful side income |
| 90th | $5,107 | Approaching part time replacement |
| 99th | $58,404 | The tail that distorts every average |
Notice the shape. The distance from the median to the 99th percentile is a factor of roughly 400. This is why quoting an average startup revenue is close to meaningless: a small number of large earners drag the mean far above where almost everybody actually sits. Any benchmark that gives you a single average without a distribution is hiding the thing you need to see.
Before you even reach the distribution above, there is a larger group that the distribution excludes. Of 8,600+ tracked startups, 4,900+ report no revenue at all. That is 56%, and it is the single largest bucket in the dataset by a wide margin.
This is the part that failure databases structurally cannot capture. A startup earning nothing has not necessarily shut down. It usually still has a landing page, a working product and a founder who has not formally given up. It simply never converted. In the language of post-mortems it is not dead, so it never gets written up, which is precisely why the public record over-represents dramatic collapse and under-represents quiet non-starts.
"2 signups and $0 revenue taught me the real lesson: I spent too much time coding as i fell in love with the idea and not enough time was spent validating." – r/indiehackers
"There were no customers to tell me anything." – r/indiehackers
Among startups that earn anything, 2,800+ of 3,700+ stay under $1,000 per month. That is 76% of earners, and measured against every tracked startup including the zero-revenue majority, it means roughly 1 in 10 startups ever clears $1,000 per month.
$1,000 per month is a useful threshold because it is roughly where a project stops being a cost center. Below it, most founders are paying for hosting, domains, email and analytics out of pocket. One founder described exactly this arithmetic when explaining why they shut down a B2B product after two years:
"I got 1 sign-up (a friend) and grossed about $2k across 2 years, which was eclipsed by my poorly managed AWS costs." – r/indiehackers
If you want a realistic first target, our breakdown of how side projects reach their first $1K MRR is a better planning document than any generic growth framework, because it is calibrated to where the distribution actually bends.
230+ of the startups we track clear $10,000 per month. Against the 3,700+ with any revenue that is 6%. Against all 8,600+ tracked it is about 1 in 38.
$10,000 per month is the threshold most people implicitly mean when they say a startup "worked." It is roughly a replacement salary for a solo founder in many markets. The fact that it is a 1 in 38 outcome rather than a 1 in 3 outcome is the most important calibration in this entire article, and it is almost never stated in the guides that promise validated ideas succeed most of the time.
| Outcome | Share of earners | Share of all tracked |
|---|---|---|
| No revenue at all | n/a | 56% |
| Under $100/mo | 44% | 19% |
| Under $1,000/mo | 76% | 33% |
| $10,000/mo or more | 6% | About 1 in 38 |
Of the 3,600+ tracked startups that report 30 day growth data, 1,400+ are shrinking, 850+ are flat and 1,200+ are growing. In percentage terms that is 41% shrinking, 24% flat and 36% growing, with a median 30 day growth rate of 0.0%.
A median growth rate of exactly zero is the cleanest single expression of the stall. The typical tracked startup is not dying and is not compounding. It is holding still. This is a fundamentally different problem from the one most startup advice addresses, because advice aimed at avoiding collapse does not help a product that is stable and irrelevant.
"Your beautiful code means nothing if nobody knows it exists." – r/indiehackers
"Revenue is the only metric. Your signup count, page views, and GitHub stars are just dopamine hits." – r/indiehackers
Category medians in our dataset include zero-revenue startups, which is why so many land at $0. That is not a data gap, it is the finding: in several popular categories, more than half of everything tracked earns nothing.
| Category | Startups tracked | Median MRR | Read |
|---|---|---|---|
| Mobile Apps | 400+ | $42 | Highest median, still tiny |
| Sales | 50+ | $15 | Crowded, low conversion |
| Social Media | 90+ | $10 | High build rate, low monetization |
| Health & Fitness | 160+ | $9 | Consumer pricing pressure |
| Developer Tools | 530+ | $0 | Majority earn nothing |
| Content Creation | 230+ | $0 | Extreme winner-take-most tail |
| Real Estate | 70+ | $0 | Long sales cycles |
Developer Tools is the instructive one. It is the single largest tracked category at 530+ startups and its median is $0, which tells you that developers overwhelmingly build for other developers and that the category is saturated relative to willingness to pay. If you are weighing a category, our low competition SaaS analysis and boring industries breakdown both point away from this trap.
"'Everyone' isn't a customer. It's a marketing fantasy." – r/indiehackers
If the revenue distribution were wrong, the exit market would contradict it. It does not. Across 650+ live acquisition listings we track, the median business shows $121,500 in trailing twelve month revenue, which is about $10,000 per month, and carries a median asking price of $195,800 at an average 7.3x profit multiple and 2.3x revenue multiple.
Read that against the distribution above and the picture is coherent. The businesses that reach a sale are drawn from roughly the top few percent of the revenue distribution. Everything below that threshold does not get acquired, it gets abandoned or quietly maintained. Our SaaS valuations analysis and valuation multiples breakdown cover what those multiples mean in practice, and the SellSide validation guide explains how to read listings as demand evidence rather than as shopping.
The most durable finding in startup post-mortem research is that the largest single cause is building something nobody needs. CB Insights' analysis of startup post-mortems puts no market need at 42%, the top category in their dataset. It is the one widely quoted startup statistic that does trace to real underlying work.
Our data is consistent with it, from a different direction. If the dominant failure mode were execution, you would expect a bimodal distribution: products that work and products that break. Instead you get a long left tail of functioning products earning nothing, which is what a demand problem looks like in aggregate.
"I've wasted a few weeks here and there when I built out features that no one really wanted." – r/indiehackers
"Distribution > Product." – r/indiehackers
This is also why our State of SaaS Pain Points research starts from documented complaints rather than from idea generation. A complaint is evidence that a problem already annoys somebody enough to write about it, which is a materially stronger starting signal than an idea that sounds good.
The statistics describe the shape. The quotes describe the experience. Every one of these comes from a founder writing publicly about a product that did not reach meaningful revenue.
"People don't want to do 'Mom tests' with strangers. They're busy. You're nobody." – r/indiehackers
"I launched a few other things trying to Make It Peter Levels Style and they all failed." – r/indiehackers
"Turns out my product isn't something people are searching for on Google." – r/indiehackers
"I've had an affiliate system live for months now and I get a ton of applications... 99% get 0 sign ups." – r/indiehackers
"2 months is 2 competitors launched, 200 customer conversations they had, and 2000 reasons you're behind." – r/indiehackers
"I was not willing or able to market it." – r/indiehackers
The pattern across all of them is the same and it is not incompetence. These are people who shipped working software. What they lacked was evidence, before building, that anyone was waiting for it.
Because this topic attracts confident numbers, it is worth naming the ones that circulate without support. We checked each of these against published sources and could not find primary research behind them.
| Circulating claim | Status | What we can say instead |
|---|---|---|
| "90% of startups fail" | No primary source | BLS survival data puts about half of new employer businesses gone by year five |
| "Validated ideas have 60-70% success rates vs 10-20% unvalidated" | No primary source located | 6% of startups with revenue reach $10K/mo, validated or not |
| "Validation ROI ranges from 10:1 to 100:1" | No primary source located | Unmeasurable without a control group nobody has published |
| "Benchmark: 3-5% email signup, 1-2% pre-order" | Asserted, varies wildly by traffic source | Depends entirely on channel, offer and audience temperature |
| "42% fail from no market need" | Real | CB Insights post-mortem analysis, correctly cited |
We are pointing this out for a practical reason rather than a rhetorical one. If you are choosing what to build based on a claimed success rate, and that success rate was invented, your expected value calculation is wrong by an unknown margin. Prefer numbers with a method attached, including ours.
The dataset cannot prove causation, but the correlations are consistent and they line up with what our other research shows.
They start from a documented problem. BigIdeasDB indexes 1M+ real complaints across Reddit, G2, Capterra and app store reviews, plus 40,000+ Capterra feature gaps recording what users explicitly ask for and do not get. Products built against a documented complaint start with evidence that the pain exists.
They pick categories where money already moves. The Stripe Index covers 30,000+ companies already taking payment across 83 active categories. A category with existing payers is a validated market, which is very different from an empty one. Our micro-SaaS competition map shows where those payers concentrate.
They go narrow. The category table above is a warning about breadth. Founders who reported turning things around overwhelmingly described narrowing, not broadening.
"1% of a big market is still massive. Better to own 50% of a small market than 0.1% of a big one." – r/indiehackers
"Nail one market first. If you're not dominating your home market, international won't save you." – r/indiehackers
They avoid single points of dependency. One team described losing a $30,000 per month business overnight to a platform enforcement email:
"in less than 3 months we went from: $30k MRR → $0 MRR → $2k MRR." – r/indiehackers
"Running a SaaS with third party platform dependency is a classic 'don't do that' move." – r/indiehackers
The single highest leverage change available to most founders is moving the evidence step before the build step. Concretely:
Check whether the complaint already exists. Search the complaint corpus for the problem you intend to solve. If nobody has complained about it, that is information. Our pain points database and complaints index are both searchable directly, and the complaint analysis guide explains how to read severity and market gap scores.
Check whether anyone already charges for it. An existing competitor is validation, not a disqualifier. The question is contestability. See market saturation for which categories are crowded and which are open.
Check whether people pay for the manual version. Freelance demand is a leading indicator: if businesses are hiring humans to do a thing repeatedly, software has room. Our Upwork demand analysis walks through the method.
Check the revenue reality of the category. Before committing months, look at revenue benchmarks by category and real solo developer revenue examples. If the category median is $0, you need a specific reason to believe you are the exception.
Do this before you write code. BigIdeasDB turns 1M+ real complaints, 30,000+ companies taking payment on Stripe, 17,000+ funded companies and 8,600+ revenue-tracked startups into a single check: does this problem exist, and does anyone pay to solve it?
Search the evidence →There is a small industry of startup failure databases now, most of them cataloguing somewhere between 250 and 1,700 documented shutdowns, usually weighted toward venture-backed companies with public post-mortems. They are genuinely interesting to read. They are also a badly biased sample for the decision most founders are actually making.
Three problems compound. First, they select on visibility. A company only enters a failure database if its collapse was documented, which requires it to have been notable enough that somebody wrote about it. The 4,900+ zero-revenue startups in our dataset are invisible by that standard: nothing dramatic happened to them.
Second, they select on funding. Post-mortems cluster heavily among companies that raised capital, because raising capital creates the obligation to explain what happened. We track 17,000+ funded companies, and they are a small fraction of the software businesses being started. If you are a solo founder shipping a tool, the failure modes of a company that burned $50M in venture money are not your failure modes.
Third, and most importantly, they tell you about endings rather than about odds. Reading fifty post-mortems teaches you the taxonomy of ways things go wrong. It does not tell you the probability that your specific idea earns $1,000 per month, which is the number that should drive whether you build it. A revenue distribution answers that. A list of dead companies does not.
This is why we built the datasets the way we did. Our acquisitions research looks at businesses being sold, our indie SaaS revenue study looks at what they earn, and our metrics benchmarks describe the distribution rather than the anecdote. The goal is base rates you can plan against.
Given how much invented data circulates in this category, a short filter is worth more than any individual number. Four questions catch almost everything.
1. What is the denominator? "60% of validated ideas succeed" is meaningless without knowing what pool was measured and what counted as success. Notice that we had to state two denominators throughout this article, because 6% of earners and 1 in 38 of all tracked startups are the same fact described against different populations. Anyone who gives you one number without telling you the base is either being careless or hoping you will not ask.
2. Is it a median or a mean? Our mean and median differ by more than an order of magnitude in several categories. Content Creation has a median of $0 and an average above $15,000, because a handful of very large earners sit in the same bucket as hundreds earning nothing. Any average reported without a percentile spread should be treated as marketing.
3. Is there a control group? Claims about validation improving success rates require comparing validated and unvalidated ideas from the same starting population. We have not seen that study published, including by companies whose product is validation. We do not make the claim either, which is why the section above on what separates the top 6% is explicitly labelled correlation.
4. Can you get to the primary source in one click? The CB Insights 42% figure survives this test. The 90% figure does not. If a page cites a number and the citation leads to another blog post citing the same number, you have found a rumour with a bibliography.
Applied to this article: our denominators are stated, our percentiles are published, we disclaim causation, and the methodology table below lists what each dataset cannot tell you. Hold other sources to the same standard, and hold us to it too.
Every figure in this article was re-queried on August 26, 2026. We publish the method and the limitations because a statistic without one is the problem this article is about.
| Source | Scale | Used for | Limitation |
|---|---|---|---|
| Revenue intelligence | 8,600+ startups | Revenue distribution, percentiles, growth rates, category medians | Skews toward publicly discoverable, web-based products. Offline and enterprise businesses are under-represented. |
| SellSide acquisition listings | 650+ live listings | Exit-market revenue and multiple benchmarks | Sellers self-report revenue. Listings are asks, not closed prices. |
| Stripe Index | 30,000+ companies, 83 categories | Category saturation and existing-payer signal | Presence on Stripe indicates payment capability, not revenue level. Price tier data is largely unknown. |
| Complaint corpus | 1M+ records | Demand evidence and failure-cause context | Complaint volume tracks product popularity as well as product quality. Large products generate more complaints. |
| Capterra feature gaps | 40,000+ records | Unmet-demand signal | Reflects requests from existing users, not from the unserved market. |
| Funded company database | 17,000+ companies | Capital concentration by sector | Funding amounts and dates are incomplete in this dataset and were not used. |
| Founder quotes | Public Reddit posts | Qualitative failure patterns | Self-selected and self-reported. Anonymised to subreddit only. Illustrative, not statistical. |
Revenue tracking is not a census. We track startups that are publicly discoverable. A profitable private business with no web footprint does not appear, which likely biases our distribution downward relative to all software businesses.
Zero revenue is not the same as failure. Some zero-revenue entries are pre-launch, deliberately free, or monetised off-platform. We report them as zero revenue because that is what is measurable, not because we assert they failed.
Correlation only. The section on what separates the top 6% describes patterns that co-occur with higher revenue. We do not have a control group and we are not claiming causation. Anyone who does claim it, including anyone selling validation software, should be asked for their control group.
Snapshot, not trend. These are August 2026 figures. We are not comparing them across time in this article because our tracking coverage has expanded, which would make a period comparison misleading.
It depends on the definition. If failure means shutting down, BLS business survival data puts roughly 20% of new employer businesses out in year one and about half gone by year five. If failure means never earning meaningful money, it is far higher: 56% of the startups we track report no revenue at all, and only about 10% clear $1,000 per month.
No reliable primary source supports a flat 90%, and it is almost always quoted without one. Government survival data lands closer to 50% at five years. The more useful framing is that most startups stall rather than collapse. The median tracked startup with any revenue earns $145 per month.
Averages mislead here. Among 3,700+ startups with any revenue the median is $145 per month, the 25th percentile is $29 and the 75th is $894. The 99th percentile is $58,404, which is what pulls every average away from the typical experience.
About 6% of startups that earn anything reach $10,000 per month. Measured across every startup we track, including the zero-revenue majority, it is roughly 1 in 38.
The largest single cited cause is building something nobody needs. CB Insights puts no market need at 42% of post-mortems. Our distribution is consistent with a demand problem rather than an execution problem, because the dominant pattern is a functioning product earning nothing.
Stalling is far more common. Of tracked startups reporting growth data, 41% are shrinking, 24% are flat and 36% are growing, with a median 30 day growth rate of 0.0%. Most founders never experience a clean failure event.
Most never do at scale. With 56% reporting zero revenue and a median earner at $145 per month, the useful question is whether demand exists at all rather than how long revenue takes. Checking documented demand first is cheaper than discovering its absence later.
Validate against evidence that already exists instead of opinions you collect. Check whether the complaint is documented, whether anyone already charges for a solution, and whether the category has real payers. Start with the idea validation hub, the SaaS idea validation tool guide and our complaint-backed SaaS ideas.
If this reframed the odds for you, these go deeper on the specific decisions that move them: how to validate a startup idea, how to find startup ideas in 2026, micro SaaS ideas for 2026, lessons from real business failures, the biggest business pain points of 2026, finding business ideas on Reddit and the most complained-about software of 2026. For the product side, see the revenue intelligence tool guide and indie hacker validation workflow. The full methodology behind our datasets lives on the research hub.
BigIdeasDB Research. (2026). Startup Failure Statistics 2026: What 3,700+ Revenue-Verified Startups Actually Earn. BigIdeasDB. Retrieved from https://bigideasdb.com/startup-failure-statistics-2026