TL;DR: I pulled and analyzed the full TrustMRR dataset of 986 indie startups with verified Stripe revenue. The median MRR is $445. Profit margins sit at 81%. Most indie hackers are making less than $500 per month, but they're keeping almost all of it. The real story is more complicated and more interesting than the highlight reels suggest.
Marc Lou built TrustMRR as a public database of verified indie revenue. Founders connect their Stripe accounts and the numbers show up live. No self-reported vanity metrics. No screenshots that could be Photoshopped. Real revenue, verified in real-time.
I spent a full day analyzing this dataset because I was tired of seeing the same $10K MRR screenshots on my timeline without any context. What does the actual distribution look like? How many people are really making it? And what separates the ones who break through from the ones stuck at zero?
The numbers tell a story that's more honest and more useful than any success thread on X.
The Three Biases You Need to Know First
Before I share a single number, three things change how you should read everything that follows.
Bias 1: This is Marc Lou's audience. Marc is French. His community is disproportionately French and European. France is second in the database with 97 startups, behind only the US at 273. This isn't a random sample of all indie hackers worldwide. It's a specific community with specific characteristics.
Bias 2: Survivors only. The founders on TrustMRR cared enough to connect their Stripe accounts publicly. That's already a filtered group. More motivated, more community-oriented, more likely to be intentional builders. The person who tried something quietly for three months and got zero revenue isn't here.
Bias 3: The for-sale effect. About 38% of projects in the database are actively listed for sale. Think about what that means. If you had a project growing 30% month-over-month and exciting you every morning, would you list it on an acquisition marketplace? Probably not. The projects listed for sale tend to be the ones that have plateaued. Decent revenue, but stalled growth or lost energy.
The data confirms this exactly. For-sale projects have a median MRR of $727, higher than the not-for-sale median of $319. But their average 30-day growth rate is just 119%, versus 651% for projects not listed for sale. The plateau-ers get sold. The rockets get kept.
What the Numbers Actually Show
The core dataset, after removing statistical outliers above approximately $16,400 MRR, contains 528 projects with positive revenue.
The median MRR: $445 per month.
About 27% of these projects make under $100 a month. Tools like Supaframe, an embedding tool for Supabase data into Notion, sitting at $88 MRR. Or JobBoardSearch, a job board aggregator, at $99. They exist. They have real users. They're making almost nothing.
More than half the core make under $500. Only 13% have crossed $5,000 MRR.
Marc Lou shared his own read of the data recently: "81% made $1+, 44% made $1,000+, average time to first $1: 5 months." His numbers look slightly more optimistic because he includes the full live platform data. But the order of magnitude matches what I found.
Now layer those three biases on top. The $445 median already looks low. But it's calculated on a dataset that skews toward stagnating projects, excludes the true failures, and over-represents a community where transparency is a cultural value. The real distribution of all indie projects ever attempted would look considerably more bottom-heavy.
The Margin Story Changes Everything
Here's where it gets genuinely interesting.
Among the 464 startups that reported margin data, the median profit margin is 81%. The average is 78%. About 80% of them run at margins above 70%.
Think about that for a second. A traditional business (restaurant, service company, retail shop) would celebrate 20% margins. These projects, even the ones making $445 a month, are keeping most of what they earn.
$445 at 81% margins, with zero employees and a laptop you already own, is a very different animal than $445 in a traditional business.
And margins don't drop much between the for-sale and not-for-sale groups. The plateau-ers have a median margin of 85%. The growing projects: 80%. The economics of the business model are sound at almost every level. What varies is growth, not structure.
The failure mode for most of these projects isn't bankruptcy or debt. It's "I made $445 a month for a while and then stopped." That's a strange kind of risk to be afraid of.
The Outlier Class: What $16K+ MRR Looks Like
The 103 startups above the $16,400 MRR threshold are a different species entirely. These aren't side projects. They're real businesses.
What separates them from the core dataset?
Time. Most outliers have been at it longer. You don't stumble into $16K+ MRR in three months. These founders stuck with their product through the $445 median phase and kept pushing.
Focus. The outliers tend to serve narrower markets with deeper solutions. They picked a specific pain point and went all in on solving it well, rather than building a general tool hoping to find an audience.
Distribution. They figured out one acquisition channel and rode it hard. Not five channels at once. One that works, repeated consistently.
Pricing. Higher MRR almost always comes from higher per-customer revenue, not just more customers. The outliers charge more because they deliver more targeted value.
The $500 MRR Gravity Well
The data reveals something I've been writing about for months: there's a gravity well around $500 MRR that most indie hackers can't escape.
You build something. You get your first users. You hit $200, $300, $500 in monthly revenue. And then... nothing. Growth stalls. The product is good enough to retain existing users but not compelling enough to attract new ones organically.
The TrustMRR data shows this pattern in sharp relief. The distribution clusters heavily between $100 and $1,000 MRR. Getting past that range requires a fundamentally different approach than what got you there.
What got you to $500: building a decent product and telling a few people about it.
What gets you past $500: systematic distribution, deliberate positioning, and understanding exactly which bottleneck is holding your growth back at your specific stage.
Most founders at $500 MRR are guessing about what to work on next. They bounce between tactics, try a little of everything, and make zero progress. The data shows that consistency on one channel beats experimentation across five.
Country Distribution: Geography Matters More Than You'd Think
The US leads with 273 startups. France follows with 97. Then the UK, India, Germany, and Canada.
But here's what's interesting: the revenue per startup varies significantly by geography. US-based projects have higher median MRRs than the global average. French projects cluster closer to the median.
This isn't about talent or effort. It's about market access. Building for a US audience, where credit card penetration is high and willingness to pay for SaaS is culturally normal, gives you a structural advantage over building for markets where those conditions don't exist yet.
For founders outside the US, the takeaway isn't "move to America." It's "build for an American audience." The internet lets you serve customers anywhere. Choose the market with the best economics.
Subscription Models Dominate (But Not Exclusively)
The vast majority of projects in the dataset run on monthly subscriptions. This makes sense for TrustMRR, which specifically tracks Stripe revenue, biasing toward recurring payment models.
But the successful outliers often use tiered pricing rather than single-plan pricing. Multiple tiers allow founders to capture different willingness-to-pay segments and grow revenue per customer over time.
The median pricing across the dataset sits in the $29 to $49 range for the core group. The outliers tend to charge $99+ per month or have enterprise tiers that bring the average up significantly.
The lesson: if you're pricing below $29/month, you need massive volume to reach meaningful MRR. At $49+, you need fewer customers and can afford higher customer acquisition costs.
What This Data Actually Means for You
If you're building right now and making less than $445 a month, you're in the majority. That's not a failure. That's the median outcome for verified indie startups with real revenue.
If you're making nothing, you're in a bigger majority that this dataset doesn't even capture.
If you're past $1,000 MRR, you're in the top 30% of all projects in this database. If you're past $5,000, you're in the top 13%. If you're past $16,400, you're a statistical outlier.
The question isn't whether these numbers are depressing or inspiring. Both readings are valid. The question is what you do with them.
The data says margins are excellent. The economics work. The problem isn't the business model. It's distribution, focus, and knowing which bottleneck to solve at which stage.
Common Mistakes the Data Reveals
Building without distribution. The projects with the lowest MRR tend to be technically impressive tools with zero marketing effort. The market doesn't care how good your code is if nobody knows it exists.
Pricing too low. Projects at $9/month or $19/month need hundreds or thousands of customers to reach meaningful revenue. Projects at $49 to $99 need dozens. The math favors higher prices in narrow markets.
Giving up too early. The average time to first dollar is 5 months. Many founders quit at month 3. The data shows that persistence compounds, but only if you're persistent about the right things.
Copying what worked for someone else. Marc Lou's community has produced many successful launches, but copying the launch playbook without understanding why it worked leads to the $100 MRR cluster.
The Category Breakdown: What People Are Building
The dataset reveals clear category clusters. Developer tools, productivity apps, and marketing automation dominate. Social media tools and content creation platforms also show up frequently.
But here's what's counterintuitive: the categories with the most projects don't always produce the highest MRRs. Developer tools are popular because developers build them easily, but the market is crowded and pricing pressure is intense. Niche B2B tools in less sexy categories (compliance, HR, vertical SaaS for specific industries) often reach higher MRR with fewer competitors.
The data suggests a trade-off: build what you know (developer tools, consumer apps) and compete with everyone, or learn a specific market's pain points and build something only you understand. The outlier class overwhelmingly chose option two.
The Solo vs Team Dynamic
One detail worth noting: the projects that break through often add a second person at some point. Not a co-founder necessarily, but a contractor, a part-time designer, or a support person. The truly solo projects tend to cluster in the lower revenue ranges.
This makes sense when you think about it. Below $1,000 MRR, doing everything yourself is manageable. The product is simple, support volume is low, and you can wear every hat without burning out. Past $5,000 MRR, the operational demands start exceeding what one person can handle well. Support tickets increase. Feature requests pile up. Marketing requires consistent effort.
The founders who recognize this transition point and get help at the right time tend to grow faster than those who try to stay solo indefinitely. The data doesn't prove causation here, but the correlation is visible.
What the Growth Rates Tell Us
The average 30-day growth rate across the dataset is 247%, according to Marc Lou's own analysis. That sounds incredible until you realize what it means statistically.
That number is pulled up by a small number of projects experiencing explosive early growth (going from $10 to $30 MRR counts as 200% growth). The median growth rate is much lower and closer to single digits for established projects.
Growth rates also vary dramatically by stage. Projects under $100 MRR show wild percentage swings because the base is tiny. Projects above $5,000 MRR tend to show 3-8% monthly growth, which is healthy and sustainable.
The takeaway: stop comparing your growth rate to headline numbers. A 5% monthly growth rate on $5,000 MRR ($250 more per month) is better than 200% growth on $15 MRR ($30 more per month). Absolute dollars matter more than percentages once you have real revenue.
The Real Takeaway
The TrustMRR data tells a story that's neither the doom and gloom of "most startups fail" nor the sunshine of "just ship and you'll make $10K MRR." The reality is more nuanced.
Most indie projects make very little money. But they make it at incredible margins. The ones that break through share common traits: narrow focus, higher pricing, consistent distribution, and persistence past the $500 plateau.
The distribution of outcomes is heavily skewed. A few projects do extraordinarily well while most hover near zero. This isn't unique to indie hacking. It's the distribution of any creative or entrepreneurial endeavor. What's different is the low cost of failure and the high margins of success.
The hardest part of reading data like this isn't understanding the numbers. It's knowing which number applies to your situation. Are you stuck at $445 because of a distribution problem, a pricing problem, or a product problem? Each one requires a completely different response, and picking the wrong one means months of wasted effort pointed at the wrong thing.
Luka connects your analytics, error data, and user signals across sources, finds what they're saying together, and tells you which bottleneck to fix today based on where your product actually is. You check it in the morning, know where to aim, go execute. See how Luka works.
What Successful Founders Did Differently: A Pattern Analysis
Looking specifically at the outlier class, patterns emerge that go beyond "work harder."
The most consistent trait: they built something that served a workflow, not just solved a problem. The difference is subtle but important. A problem-solver gets used once (export this PDF, convert this file). A workflow tool becomes embedded in how someone works daily (manage my social media scheduling, handle my invoicing). The workflow tools have lower churn because switching means disrupting an entire process, not just finding an alternative for a single task.
Second pattern: they raised prices at least once within the first year. Not dramatically, but enough to test the market's actual willingness to pay. Many found they could charge 2-3x their initial price with minimal customer loss. The customers who stayed were more engaged, churned less, and required less support.
Third pattern: they had at least one distribution channel producing consistent, predictable leads. For some it was SEO. For others, a specific community or referral program. The common thread wasn't which channel, it was having one channel they understood deeply rather than spreading thin across many.
Frequently Asked Questions
How reliable is TrustMRR data?
The revenue numbers are verified through direct Stripe API connections. What you see is real, current revenue. The caveat is selection bias: the founders who chose to make their revenue public aren't representative of all indie hackers.
What's the average time to reach $1,000 MRR?
The data suggests most projects that reach $1,000 MRR do so within 6 to 12 months of their first paying customer. But many never reach it at all. The distribution is bimodal: some projects get there quickly, most never do.
Is $445/month MRR worth pursuing?
At 81% margins with no employees, $445/month is $360 in profit for minimal ongoing effort. It won't replace a salary, but it's proof of concept. The question is whether you can grow it, not whether it's worth having.
What percentage of indie startups actually reach $10K MRR?
Based on this dataset, roughly 5-7% of projects with positive revenue reach $10K+ MRR. When you include all projects ever attempted (including those not in TrustMRR), the percentage is likely much lower.
Why do so many projects plateau around $500 MRR?
The $500 plateau happens when initial channels (friends, Twitter following, Product Hunt launch) are exhausted. Breaking through requires systematic distribution and retention optimization rather than product improvements.
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