After spending a weekend building a scraper for IndieHackers' public product pages, I ended up with a dataset of 4,500 products — and the MRR numbers tell a very different story than the Twitter highlight reel.
I went in expecting to see a long tail of hobby projects and a handful of unicorns. What I actually found was a surprisingly healthy middle class of indie SaaS.
Here's what the data looks like after a few hours with pandas.
The numbers
Out of 4,500 products scraped, 1,544 (34%) self-report MRR on their profile. The rest either keep it private, haven't updated in forever, or are pre-revenue.
The MRR distribution for the reporters:
| Bucket | Products | Share |
|---|---|---|
| $0 – $100 | 476 | 31% |
| $100 – $1K | 447 | 29% |
| $1K – $10K | 401 | 26% |
| $10K+ | 220 | 14% |
The takeaway that surprised me: the $1K–$10K bracket is where most "successful" indie products actually live. Not the $100K MRR screenshots that go viral. A product doing $3,500/mo is doing better than ~85% of everything publicly listed.
Median MRR among revenue reporters: ~$750/mo. Not glamorous, but it's a real number, and it puts the "just quit your job" advice in perspective.
What's growing
Tagging each product by its stated category and doing a rough vertical split:
- SaaS tools — still the biggest slice
- Developer tools — overrepresented vs the broader market (no surprise given the audience)
- AI / automation — the fastest-growing category in the listings; dozens of products added in the last 90 days alone
- Productivity — steady but saturated
- Marketing / growth — lots of entries, but most sit in the $0–$100 bucket
The AI bucket is the one worth watching. A year ago it was a sliver. Now it's pushing ~18% of new listings.
Who's behind them
Looking at the team size field:
- Solo founders dominate the $1K–$10K bracket (62% of products in that range list a team of 1)
- Products with 2+ founders skew toward the $10K+ bracket
- Nobody in the $100K+ club is solo — every one has a team
That matches the intuition that revenue-per-founder has a ceiling, but the data is cleaner than I expected.
Filtering the data in Python
If you want to isolate, say, profitable AI products in the dataset, it's a one-liner with pandas:
import pandas as pd
df = pd.read_csv("indiehackers_products.csv")
ai_winners = df[
(df["mrr"] > 1000) &
(df["tags"].str.contains("ai", case=False, na=False))
]
print(ai_winners[["name", "mrr", "tags", "url"]].sort_values("mrr", ascending=False).head(20))
From there you can pivot on launch date, team size, or pricing model to find the patterns you care about.
How I'm using this
A few angles that turned out to be useful:
- Competitor analysis — pick a vertical, sort by MRR, and you instantly see who the players are and roughly what they're pulling in.
- Niche validation — if a category has 40 products but none above $500/mo, that's a signal (probably a bad one).
- Founder outreach — if you sell to indie founders, this is basically a lead list with qualification data attached.
- Pricing benchmarks — cross-referencing MRR bracket with listed price gives you a rough sense of how many paying customers each product has.
The dataset
I cleaned the full 4,500-row CSV up and put it on Payhip if you'd rather skip the scraping step: IndieHackers Products Dataset
Includes product name, URL, tags, MRR (where public), team size, launch date, and short description. Single CSV, no tracking, no subscription.
If you end up finding something interesting in the data, I'd genuinely like to hear about it — leave a comment and I'll follow up.
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