Benchmark reports are written for companies with a finance team. They open at $1M in annual revenue and report on net revenue retention, CAC payback and magic numbers. A founder with eleven customers has nothing to compare against.
These figures come from 8,817 startups that connected a live payment account (Stripe, RevenueCat, Polar, Lemon Squeezy, Paddle or Dodo) to a public verification service. Revenue and customer counts are read from the payment processor. 97% of them are under $10,000 a month, which is exactly the segment the published benchmarks skip.
The inflection is at $1,000 a month
Month-over-month revenue change, split by size:
| Monthly revenue | Products | No change | Median change (of those moving) | Share declining |
|---|---|---|---|---|
| $1 – $100 | 1,521 | 58% | −2.2% | 58% |
| $100 – $1,000 | 1,335 | 33% | −1.2% | 52% |
| $1,000 – $10,000 | 777 | 27% | +0.2% | 49% |
| $10,000+ | 262 | 18% | +2.2% | 41% |
Below $1,000 a month, the median product that changes at all is shrinking. Above it, the median grows. The sign of the median flips between the second and third row.
The other column moves too. 58% of the smallest products showed no revenue change at all in 30 days, against 18% of the largest. At the bottom of the market, most months nothing happens.
This is the most useful number here for a founder deciding whether to keep going. Stalling under $1,000 a month is the normal condition of that band rather than a signal that one product is uniquely broken. The band above behaves differently, and reaching it is what changes the trajectory.
How many customers each level takes
| Monthly revenue | Products | Median customers | Median revenue per customer |
|---|---|---|---|
| $1 – $100 | 91 | 9 | $2.37 |
| $100 – $1,000 | 104 | 42 | $6.22 |
| $1,000 – $10,000 | 67 | 183 | $15.17 |
| $10,000+ | 46 | 1,048 | $26.49 |
Revenue per customer rises with size: $2.37 at the bottom, $26.49 at the top. Bigger products are not just selling more, they are charging more.
The practical version: reaching $1,000 a month takes about 42 customers at the prices in that band, or 183 by the time a product is established in the band above. A founder counting on ten customers at $100 each is planning for a price point almost nobody in this dataset achieves.
That gap between the plan and the data is a willingness to pay question, and it is answerable before the pricing page exists.
Almost all of it is recurring, and most of it lapses
Across 3,709 products with both figures, the median product's monthly recurring revenue is 98% of its total 30-day revenue. One-off sales are rare: only 21% take less than half their revenue from recurring charges. This is a subscription market almost everywhere.
Which makes the retention figure matter more. Among 388 products reporting both lifetime customers and currently active subscriptions, the median product has 34% of its customers still active. 56% have fewer than half.
That is not a monthly churn rate, and it should not be read as one. It is cumulative: everyone who ever subscribed against everyone still subscribed today. An older product will look worse than a newer one with identical churn, simply because it has had longer to accumulate former customers. It is a floor on retention rather than a rate.
What normal looks like
Five figures a founder under $10,000 a month can measure against:
- Revenue per customer: $2.37 under $100/month, rising to $15.17 in the $1k–$10k band.
- Customers at $1,000 a month: 42 is the median in that band.
- Monthly change under $1,000: the median mover is down 1–2%.
- Months with no change at all: 33% to 58%, depending on size.
- Customers still active: 34% of everyone who ever subscribed.
Set against the rest of the distribution, these are the numbers of a band most products never leave. 54% of the same 8,817 startups are at exactly $0 a month and only 12.1% clear $1,000, which is covered in the revenue report. Reaching the $1,000 band is the change in trajectory; the benchmarks above describe what the road there looks like.
Method, and what this data can't tell you
Source. Public revenue profiles from TrustMRR, where founders connect a payment provider so revenue can be verified independently. Snapshot of 8,817 startups, August 2026.
Sample sizes differ by table. Revenue and growth figures cover thousands of products. The customer-count table covers 308, because most profiles report revenue without a customer count, and the retention figure covers 388. Treat the smaller tables as indicative.
No trial conversion, no monthly churn, no costs. The payment processor reports revenue and subscription counts. It does not report trials, cancellation dates or spending, so three of the numbers a founder most wants are absent here.
Growth is a 30-day window. At these revenue levels one customer moves the percentage a long way, which is part of why the median mover is close to zero and the spread is wide.
Selection. These founders chose to connect a payment account to a public verification service. That skews indie and bootstrapped, with no venture-funded companies. It is a census of a particular population rather than a sample of all early-stage products.
Sources
TrustMRR — public revenue profiles The underlying data. Founders connect Stripe, RevenueCat, Polar, Lemon Squeezy, Paddle or Dodo so their revenue and subscription counts are read from the processor and published on a public profile. Every figure here is an aggregate computed over the August 2026 snapshot. The per-company figures belong to TrustMRR and to the founders who chose to publish them.
Companion reports from the same snapshot: the revenue distribution, the mortality report on which products ever took a payment, and the acquisition report on what they sell for.
Cite this
Among 8,817 indie startups with verified payment data, median monthly revenue growth is negative below $1,000 MRR and positive above it, and reaching $1,000 a month takes a median of 42 customers. — Scoutr, Pre-PMF Benchmarks 2026, from TrustMRR public revenue profiles
If you reference these numbers, a link back lets your readers reach both this analysis and the underlying profiles.