I analyzed 5,079 Stripe-verified indie projects. Here's the data nobody wants to show you.
I spent the last few weeks pulling full data from TrustMRR (all revenue is Stripe-verified, not self-reported) and running a systematic analysis. Here's what I found.
The market is more brutal than the success stories suggest
Median monthly revenue across all 5,079 projects: $169
Let that sink in. Half of all projects on the platform make less than $169/month. The Twitter success stories are real — but they're extreme outliers.
The actual distribution:
AI is the most crowded, not the most profitable
1,245 AI projects (24.5% of total). Median monthly revenue: $156.
Comparison:
The least competitive categories are quietly making the most money.
The 4-year compounding effect
This one surprised me the most:
16x difference between year 0 and year 5. The founders who survive year 2 are playing a completely different game.
Only 48 projects are genuinely breaking out
I defined "breaking out" as: revenue > $10K/month AND growth > 50%/month.
Out of 5,079 projects, 48 qualify. That's 0.9%.
The projects hitting both bars right now: Postiz (+234%, $61K MRR), GojiberryAI (+99%, $130K revenue), Speel.co (+252%, $66K MRR).
What I'm building next
I'm turning this analysis into a weekly deal flow digest aimed at people acquiring indie projects. Every week: new listings on TrustMRR pre-scored by revenue quality, growth trajectory, and red flags.
The goal is to save acquirers 3-4 hours of manual research per week.
If you're someone who buys (or is seriously considering buying) indie projects, I'd love to get your input before I launch. What would make this actually useful for you?
Also happy to share the full scoring methodology if anyone's interested — just ask below.
Data source: TrustMRR API (Stripe-verified). Analysis date: March 2026.
Great work pulling this together. Median $169/month is the kind of reality check most founders need early. The year 0 to year 5
compounding is especially useful framing. If you share methodology, I’d love to see how you normalized for age/category mix and
how you handled outlier skew. This could become a really strong recurring benchmark post.