How to Find Profitable Niches by Following the Stripe Trail

Why "copy a famous winner" doesn't work for indie makers

Most indie-maker advice on niche-finding is about studying obvious winners. Read every Notion post-mortem. Listen to the Linear founders' podcast. Reverse-engineer Cursor's growth. The implicit theory is: if you understand a winning playbook deeply enough, you can run a smaller version of it.

The problem with that theory: by the time a product is famous enough to study, it's also famous enough that fifty other people are studying it. The market is being attacked by better-funded teams from above and a stampede of clones from below. You don't have a wedge.

The actual opportunities for a single dev or two-person team are different from the famous-winner archetype:

• Niches small enough that no VC-backed startup will bother.
• Buyers willing to pay (because a real pain exists), but not so many of them that a big team cares.
• Visible monetization signal — someone is already charging, someone is already paying — but invisible from the public discourse on Twitter or HN.

These exist. Hundreds of them. The challenge is finding them without manually opening 200 tabs.

The signal: traffic × Stripe

Traffic alone is a bad signal. A site with 500K monthly visits could be ad-supported (great for them, useless as an indie monetization template), an SEO content farm (high traffic, low conversion), a free tool with no business model, or a blog with a domain that ranked well years ago and the operator left.

Monetization presence alone is also weak. Anyone can drop a Stripe Checkout button on a static site. A site with a pricing page and a Stripe script could have launched yesterday with zero customers.

The signal that matters is the intersection: a site that has both meaningful traffic AND visible monetization AND a few sanity-check signals that suggest a real operator behind it. Specifically:

• 5K – 100K monthly visits. Small enough to be unsexy and unattacked, large enough to indicate real users showing up.
• Stripe / Paddle / LemonSqueezy / Gumroad checkout. A clear payment-processor fingerprint embedded in the page.
• Pricing page with concrete numbers. $9, $19, $49, $99 a month — not "Contact us." "Contact us" pricing means enterprise; you're not playing that game.
• At least 6 months of content cadence.A blog or changelog that's been updated regularly. This is the single best signal that there's an operator who's still in the game.

A site matching all four is almost certainly making real revenue — typically somewhere between $30K and $1M annually. That's the indie-maker sweet spot. Big enough to validate the niche, small enough that the founder has reason to talk to you, generally underexposed enough that you can build a position there before someone louder notices.

What monetization fingerprints actually look like

Concretely, here's what you can detect from outside a site without ever logging in.

Payment-processor scripts

Stripe's in-page Elements or Checkout flows load js.stripe.com/v3/from any page where they're embedded. Paddle loads cdn.paddle.com/paddle/paddle.js. LemonSqueezy typically loads from app.lemonsqueezy.com. Gumroad embeds load from gumroad.com/js/gumroad.js. These signatures are visible in raw HTML or via the browser Network tab on a pricing page.

Pricing-page heuristics

A site with a /pricingURL listing USD-denominated tiers and a "Subscribe" or "Start free trial" CTA is almost certainly running real subscriptions. Bonus signal: an annual-vs-monthly toggle with a discount on annual is the universally recognized indie-SaaS pricing-page tell.

Tech-stack fingerprints

Indie SaaS in 2025-2026 has converged on a recognizable stack: Vercel or Netlify hosting, Supabase or Neon for the database, Stripe for billing, Resend or Loops for transactional email, PostHog or Plausible for analytics. A site running this combination is operated by an indie team. A site running AWS + Auth0 + Mixpanel + Segment is operated by a VC-funded startup with a payments team — different game, not your target.

Content cadence

A blog with 20+ posts at semi-regular intervals over the last 12 months means there's an operator who's compounding SEO. A blog with 3 posts dated 2023 means the project is dead. A changelog updated this month means whatever the product is, it's being shipped.

buildinpublic footprint

A founder posting metrics openly on X or IndieHackers gives you actual numbers to anchor. A product that's been on IndieHackers with reported MRR provides ground-truth calibration for everything else you're estimating. Don't skip this — it's the cheapest sanity check on the rest of the method.

The data sources you actually need

To run this method end-to-end, you need data from at least five different surfaces:

• Traffic estimates — for the volume signal. SimilarWeb-class panel data, or SERP-derived estimates from Ahrefs / SEMrush. The structural biases of each are covered in an earlier post; the short version is: triangulate, never trust one source on a small site.
• Tech-stack and payment-fingerprint data— for the monetization signal. BuiltWith, Wappalyzer, or your own crawler that grep's page HTML for the script signatures above.
• Backlink and SERP-keyword data— to figure out where the site's traffic is coming from and which keywords are the wedge. Ahrefs, DataForSEO, or similar.
• Content cadence data — sitemap parsing or blog-page scraping to count posts over time.
• Social and community signal — IndieHackers back-catalog, ProductHunt history, X mentions, HN comments. For validation and prioritization.

In the old workflow, that's six tools, six logins, and a manual spreadsheet to correlate them. The friction is the reason most indie devs never actually run this method, even though it works. The serious operators who do run it have either built their own internal tooling for it, or they pay $300+/month combined across SimilarWeb / Ahrefs / BuiltWith / SEMrush. Both options sit above what most solo devs will spend before they've picked a direction.

The workflow, end-to-end

Here's how the method plays out as a sequence of agent queries. Pick a vertical you're curious about — replace [niche] with "screenshot tools," "AI coding agents," "newsletter platforms," whatever — and run them in order.

Query 1 — surface candidates

Find domains in [niche] with 5K–100K monthly visits, a Stripe or Paddle checkout flow detected, and a /pricing page listing tiers under $200/month. Return the top 30 by traffic.

This is the rough cut. The agent hits a traffic data source, a tech-stack source, and a SERP source, joins on domain, and filters.

Query 2 — filter to indie operators

For each of those 30 domains, return the detected hosting, database, email, and analytics stack. Keep only those running on Vercel or Netlify, with Stripe, and either Supabase or Neon. Drop the rest.

This separates indie operations from VC-backed startups and agency-built sites. Both are profitable; only one is your archetype.

Query 3 — validate by cadence

For each remaining domain, count the number of blog posts published in the last 6 months and the last 12 months. Drop sites with fewer than 6 in the last 12. The remaining list is your serious-operator shortlist.

Query 4 — estimate revenue band

For each shortlist domain, estimate revenue range as: monthly visits × indie-SaaS conversion rate (1-3%) × the lowest pricing tier. Return a low / median / high revenue band per domain, with the inputs that produced it.

The output is a band, not a point estimate. The estimate carries 30-50% error from the traffic source and another 2-3× from the conversion-rate assumption. Treat it as directional.

Query 5 — find the keyword wedge

For each domain, surface the 5 keywords driving the most organic traffic. For each keyword, note the search-volume trend over the last 12 months — rising, flat, or declining. Sites with rising-volume keywords are riding a growing niche; flat-volume sites are in a stable but capped niche.

After Query 5 you have a structured shortlist: 5–15 domains, each tagged with an estimated revenue band, a keyword wedge, a stack profile, and a niche-trajectory tag. Specific directions to investigate, with evidence, in two hours of agent time.

The honest limits of this method

The numbers are bands, not audits

Public traffic estimators carry 30-50% error on small sites, and the conversion-rate assumption adds another factor of 2-3×. Treat outputs as "this site is somewhere between $50K and $500K ARR" rather than "this site makes $200K." Anyone who tells you they have an exact revenue number for a site they don't own is selling you something.

Saturation is invisible from the outside

A niche with three sites making $500K each is well-served. A niche with one site making $2M and three failed attempts at $0 is also well-served, just by a moat you can't see — usually a distribution moat (an entrenched community, an exclusive partnership, founder personal-brand reach). The method finds the green grass; it doesn't tell you whether it's already grazed.

It doesn't replace customer conversations

The method gives you targets to investigate. Building anything will require actually talking to people in the niche — the kind of work no public data source can do for you. Treat the output as a list of hypotheses worth testing, not directions you can ship into.

Use this method as the discovery layer of your search. It generates hypotheses. The next layer — picking, building, talking to customers — is still on you.

OnlySearch is the data agent built for exactly this kind of multi-source workflow.

The data layer is already wired up. Traffic estimates, tech-stack and payment-processor fingerprints, SERP and keyword data, content-cadence parsing, and public-web signals are all integrated. No third-party keys to bring. No six tabs to keep open. No six subscriptions to pay.

The five queries above aren't five things you'll run one by one. They're the structure of a single prompt. You name the niche; the agent runs surface → filter → validate → estimate → wedge as one chain, end to end, and hands back a structured shortlist.

In a typical session, getting from "I'm curious about [niche]" to "here are 8 candidate domains with revenue bands, stack profiles, and keyword wedges" takes about 30 minutes. Most of that is the agent working. The last few minutes are you reading the output and picking. The full market scan that used to take a week of opening tabs becomes one afternoon's coffee.

Free tier, private beta. Pick a niche you're curious about and run the whole sequence as one query.