Most numbers about startup revenue come from surveys, and surveys have a selection problem: the founders doing badly usually don't answer them. This report doesn't ask anyone. Every figure below comes from startups that connected a live payment account (Stripe, RevenueCat, Polar, Lemon Squeezy, Paddle or Dodo) to a public revenue verification service. The revenue is read from the payment processor rather than reported by the founder.
The picture that produces is harsher than the survey version, and more useful if you are deciding what to build next. It is the same reason the Mom Test exists: what people report about their own behaviour drifts from what they do, and revenue is one of the few places where the drift disappears.
The headline number
Of 8,817 startups with verified payment data, 4,763 have monthly recurring revenue of exactly $0. That is 54.0%.
| Monthly recurring revenue | Startups | Share |
|---|---|---|
| Exactly $0 | 4,763 | 54.0% |
| $1 – $100 | 1,625 | 18.4% |
| $100 – $1,000 | 1,373 | 15.6% |
| $1,000 – $10,000 | 795 | 9.0% |
| $10,000 – $100,000 | 246 | 2.8% |
| $100,000+ | 25 | 0.3% |
Three numbers worth keeping:
- 54.0% earn nothing at all.
- 12.1% clear $1,000 a month, roughly the threshold where a side project starts covering its own costs.
- 3.1% clear $10,000 a month.
The average is $2,298 and the median is $0. When a distribution does that, the average is describing the 25 companies above $100k rather than the other 8,792. Plan against the median.
Revenue is mostly a function of age
The most common question a founder asks is how long this takes. The cohort data gives a rough answer.
| Founded | Startups | Make $0 | Clear $1k/mo |
|---|---|---|---|
| 2022 | 130 | 35% | 29.2% |
| 2023 | 350 | 40% | 23.7% |
| 2024 | 770 | 46% | 16.0% |
| 2025 | 2,863 | 54% | 11.6% |
| 2026 | 3,088 | 63% | 6.7% |
Four years after founding, roughly three in ten have reached $1,000 a month. In the first year, fewer than one in fourteen have.
$1,000 a month is also where the growth rate changes sign. Below it the median product that moves at all is shrinking; above it, it grows. Our pre-PMF benchmarks cover what normal looks like in that band, including the 42 customers it takes to get there.
Two readings are possible and both are probably true at once. Revenue takes years to build, and the products that never get there quietly stop being counted. Either reading leads to the same planning assumption: a first year without revenue is the normal case.
That is also why the cheap work matters more than it looks. Twelve months is a long time to spend on a problem nobody has, and demand signals are visible before you write any code.
Category changes the odds more than AI does
The assumption in 2026 is that AI products are easy to build and hard to monetise. The first half holds up. The second doesn't show up in the data.
| Category | Startups | Make $0 | Clear $1k/mo |
|---|---|---|---|
| Mobile Apps | 442 | 24% | 21.0% |
| Marketing | 479 | 53% | 15.4% |
| Education | 376 | 50% | 15.2% |
| Health & Fitness | 313 | 38% | 15.0% |
| Artificial Intelligence | 1,971 | 49% | 13.6% |
| Content Creation | 401 | 54% | 12.7% |
| SaaS | 839 | 56% | 11.8% |
| Analytics | 225 | 58% | 10.7% |
| Fintech | 234 | 54% | 8.1% |
| Developer Tools | 524 | 69% | 6.5% |
| Design Tools | 204 | 59% | 5.9% |
| Productivity | 574 | 58% | 4.2% |
AI products reach $1,000 a month slightly more often than everything else (13.6% against 11.4%), and they manage it while being the most crowded category in the dataset by a wide margin: 1,971 of 8,817.
Developer tools have the worst monetisation rate of any major category, with 69% at zero. Developers are the audience most indie founders know best and the one least likely to pay for a tool they could assemble themselves. Design tools and productivity sit in the same trap, surrounded by free alternatives and buyers who improvise workarounds. This is the gap between a product being useful and a product getting paid for, visible at the scale of a whole category.
Mobile apps run the other way, at 24% zero-revenue and the highest share above $1k. App stores take 15% to 30% of every sale, and in exchange the buyer arrives with a payment method already attached.
Category is not destiny, but it does set the base rate you are working against. If you are building developer tools, the willingness to pay question deserves an answer before the architecture does.
What these companies sell for
Among startups listed with an asking price, the median is $6,000, at a median multiple of 2.8× annual revenue (interquartile range 1.6× to 6.0×).
That is coherent with the revenue table. A median asking price of $6,000 is roughly what a product making a couple hundred dollars a month is worth. The acquisitions founders read about belong to the 3% above $10k a month.
The listings behave strangely once you break them down by size, with the smallest products asking more than double the multiple of the largest. Our acquisition report covers the 2,196 startups that carry an asking price.
Where the founders are
| Country | Startups | Make $0 | Clear $1k/mo |
|---|---|---|---|
| United States | 1,704 | 44% | 20.0% |
| France | 604 | 52% | 14.2% |
| Canada | 253 | 49% | 13.4% |
| Brazil | 170 | 49% | 12.4% |
| Spain | 187 | 57% | 12.3% |
| Germany | 250 | 55% | 10.8% |
| United Kingdom | 613 | 55% | 9.8% |
| India | 799 | 71% | 2.9% |
US-based founders reach $1,000 a month at roughly seven times the rate of India-based founders, 20.0% against 2.9%. That gap is much larger than any category gap in the data, which points at distribution and pricing power: who you can reach, and what those people are used to paying. It is an argument for defining the market you can actually serve rather than the one your category implies.
What to do with these numbers
The point of these numbers is to change what you check before you start building.
If 54% of products with a live payment account earn nothing, the binding constraint is rarely the code. It is whether a group of people already spends money on the problem. That is checkable in an afternoon: existing paid alternatives, complaint threads describing the workaround people built instead, job postings for the manual version of the work. Market validation is the cheap step, and the year of building is the expensive one.
These numbers also sit alongside the failure data. Poor product-market fit shows up in 43% of recent shutdowns, and roughly half of new businesses are gone within five years. The full set is in our startup idea validation statistics reference. Revenue distribution and failure rate are two views of the same problem: most products never find someone willing to pay.
The same 8,817 startups also answer the question in reverse. Split by whether they ever took a payment, 23.3% never did and another 30.7% did and stopped, with the median stopped product having earned $136 across its whole life. That breakdown is in our startup mortality report.
Method, and what this data can't tell you
Source. Public revenue profiles from TrustMRR, where founders connect a payment provider so their revenue can be verified independently. Snapshot: 8,817 startups, August 2026. Payment providers represented: Stripe (60%), RevenueCat (14%), Dodo (9%), Polar (8%), Lemon Squeezy (4%), Paddle (2%).
Selection. These are startups whose founders chose to connect a payment account to a public verification service. That skews indie and bootstrapped, with no venture-funded companies in the set, and probably skews slightly toward founders comfortable showing their numbers. It is a census of a particular and growing population rather than a sample of all startups.
What "$0" means. The figure is current monthly recurring revenue read from the payment provider. It cannot distinguish a product that never sold anything from one that sold and then stopped, or from one that connected its account before launching. All three are real, and all three describe a product not currently earning.
Counting notes. The revenue-band counts sum to 8,827 rather than 8,817 because ten startups land exactly on a band boundary and appear in both adjacent rows; all percentages are calculated against the 8,817 total. The founding-year cohorts cover 7,201 startups, since not every profile lists a year. The category table shows the twelve largest categories, covering 6,582 startups between them.
What is missing. No costs, so none of this is profit. No churn history. And the founding-year cohorts mix survivorship with age: companies that shut down stop appearing, which flatters the older cohorts.
Sources
TrustMRR — public revenue profiles The underlying data. Founders connect Stripe, RevenueCat, Polar, Lemon Squeezy, Paddle or Dodo so their monthly recurring revenue is read from the processor and published on a public profile. Every figure in this report is an aggregate computed over the 8,817 profiles visible in the August 2026 snapshot. The per-company figures belong to TrustMRR and to the founders who chose to publish them.
For failure rates, survival curves and the reasons startups shut down, see our companion reference on startup idea validation statistics, which draws on CB Insights, the U.S. Bureau of Labor Statistics and Startup Genome.
Cite this
54% of indie startups with verified payment data make exactly $0 in monthly recurring revenue, and only 12.1% clear $1,000 a month. — Scoutr, Indie Startup Revenue Report 2026 (n = 8,817), from TrustMRR public revenue profiles
If you reference these numbers, a link back lets your readers reach both this analysis and the underlying profiles.