I Analyzed 312 Micro-SaaS Businesses. Here's What Actually Works.

A solo founder charging $49/month for a tool that reformats legal citations is making more money than most venture-backed startups will ever see.

I know this because I spent the last six weeks building a dataset of 312 micro-SaaS businesses — products built by one to three people, mostly bootstrapped, mostly unglamorous. I pulled data from public revenue dashboards on Open Startups pages, Indie Hackers profiles with verified Stripe screenshots, MicroConf presentations with disclosed numbers, and a handful of founders who shared their metrics in interviews or teardowns.

The goal was simple: stop guessing what works and let the data talk.

Most advice about building profitable software is anecdotal. Someone hits $10K MRR and writes a Twitter thread about it, and suddenly everyone thinks the path to success is building an AI writing tool with a freemium plan and launching on Product Hunt. But anecdotes aren't patterns. I wanted patterns.

What I found contradicts almost everything the micro-SaaS echo chamber believes.

The Dataset: What I Looked At

Before I get into findings, you should know what's in the dataset and what isn't.

I tracked 312 micro-SaaS products that met these criteria:

• Built by 1-3 people (no VC-funded teams of 20 pretending to be "indie")
• Had publicly verifiable revenue data (Stripe screenshots, Open Startups dashboards, or disclosed in recorded interviews)
• Were at least 12 months old (to filter out launch spikes that fizzle)
• Were software-as-a-service specifically — not info products, not agencies, not one-time-purchase tools

I categorized each product across 14 dimensions: niche, price point, business model (freemium vs. trial vs. paid-only), distribution channel, tech stack, founding team size, time to first paying customer, current MRR, churn rate (when available), and several others.

Then I split the dataset into three tiers:

• Winners: Sustained $5K+ MRR for at least 6 months (87 products, 28% of dataset)
• Survivors: Between $500 and $5K MRR, stable but not growing fast (134 products, 43%)
• Stalled: Under $500 MRR after 12+ months, or abandoned (91 products, 29%)

The findings broke down into five major themes. Some confirmed what smart founders already suspect. Others genuinely surprised me.

Finding 1: The Winning Price Point Isn't What You Think

The most common price point in my dataset was $9-15/month. It was also the price range most correlated with stalling out.

Of the 91 stalled products, 52% were priced between $9 and $15 per month. Of the 87 winners, only 14% were in that range.

The winners clustered in two bands: $29-49/month and $99-199/month.

This makes intuitive sense once you stop thinking like a developer and start thinking like a buyer. At $9/month, you attract individuals who churn at the first credit card statement they don't recognize. You also can't afford any real customer acquisition cost — even a single support email eats your margin on that customer for the month.

At $29-49/month, you're in the sweet spot where small businesses will pay without needing a procurement process, but the revenue per customer actually compounds into something meaningful. One hundred customers at $39/month is $3,900 MRR. That same hundred customers at $12/month is $1,200 — barely enough to cover your infrastructure costs.

The $99-199/month band was even more interesting. Products in this range had significantly lower churn (median 3.2% monthly vs. 7.8% for the $9-15 range) and required fewer total customers to reach the winner threshold. Several products in my dataset were clearing $10K+ MRR with fewer than 120 customers.

The lesson: if you're building a solo developer SaaS idea, price it for businesses, not individuals. And resist the urge to undercut competitors — the data says higher prices correlate with higher survival rates, not lower ones.

Finding 2: B2B Vertical Beats B2B Horizontal, Every Time

I categorized every product as either "horizontal" (serves any business — think project management, invoicing, analytics) or "vertical" (serves a specific industry — think salon scheduling, trucking compliance, dental patient intake).

The split in the full dataset was roughly 60/40 horizontal to vertical. But in the winner tier, it flipped: 62% of winners were vertical SaaS products.

Why? Three reasons emerged from the data.

First, vertical products face less competition. When you build "project management for everyone," you're competing with Asana, Monday, Notion, Linear, ClickUp, and four hundred other tools. When you build project management specifically for residential electricians, your competitive set shrinks to maybe two or three mediocre options.

Second, vertical products have clearer distribution channels. If your customers are all orthodontists, you know exactly where they hang out: specific conferences, specific Facebook groups, specific trade publications, specific software review sites. Horizontal products have a discovery problem that vertical products simply don't.

Third — and this was the most striking pattern — vertical products command higher prices for equivalent functionality. A generic form builder might charge $15/month. A patient intake form builder for physical therapy clinics charges $79/month for essentially the same technology, because it speaks the language of the buyer and comes pre-configured for their workflow.

This mirrors what I found when researching the veterinary clinic software market — massive opportunity hiding in plain sight because most developers don't think to look at industries they're not personally part of.

If you want to dramatically increase your odds of building a profitable micro-SaaS, pick an industry. Any industry. Learn its workflows, its pain points, its existing tools. Then build something better for that specific audience. The data overwhelmingly supports this approach.

Finding 3: The Distribution Channel That Actually Works Is Unsexy

I tracked the primary customer acquisition channel for every product in the dataset where the founder disclosed it. The results:

• SEO/Content: Primary channel for 38% of winners
• Integrations/Marketplaces: Primary channel for 24% of winners
• Direct outreach (cold email, LinkedIn DMs): Primary channel for 18% of winners
• Community participation (forums, Reddit, niche groups): Primary channel for 11% of winners
• Product Hunt / launch events: Primary channel for 4% of winners
• Paid ads: Primary channel for 3% of winners
• Other/unclear: 2%

Two things jumped out.

Product Hunt, which dominates the micro-SaaS conversation, was the primary growth channel for almost nobody who sustained $5K+ MRR. It works as a launch event — a way to get initial eyeballs and a few hundred signups. But in my dataset, products that relied on Product Hunt as their main channel had a median MRR of $1,100 after 12 months. The launch spike came and went, and without a sustainable channel underneath it, growth flatlined.

Meanwhile, SEO — the most boring, slowest, least-Tweetable growth channel — was the backbone of more winners than any other single approach. And it wasn't even close.

The integration/marketplace channel deserves special attention. Products that lived inside an existing ecosystem (Shopify apps, Slack apps, WordPress plugins, HubSpot integrations, browser extensions for specific platforms) had a significant advantage in early distribution. They were discoverable where buyers already were. Several products in the winner tier got their first 50 customers entirely through marketplace listings with zero additional marketing.

This aligns with what I've seen in products that essentially sell themselves — the best distribution is often baked into the product itself, not bolted on after the fact.

The implication for builders: before you decide what to build, decide how people will find it. If you don't have a clear answer to "what's my acquisition channel?" the product idea isn't ready yet, regardless of how good it sounds.

Finding 4: Time to First Customer Predicts Everything

This was the most predictive metric in the entire dataset.

I tracked the time between "started building" and "first paying customer" for every product where the founder disclosed it. The correlation with long-term success was striking:

• Winners had a median time-to-first-customer of 34 days
• Survivors had a median of 67 days
• Stalled products had a median of 141 days

Founders who got a paying customer within the first month or two were overwhelmingly more likely to reach $5K+ MRR. Founders who spent four or five months building before anyone paid were overwhelmingly more likely to stall out or quit.

This isn't because fast builders are better programmers. It's because the 34-day founders were doing something fundamentally different: they were selling before the product was "ready." They were validating with real money, not assumptions. They were building the minimum viable version of the thing, getting it in front of real users, and iterating based on what those users actually needed.

The 141-day founders, by contrast, were building in isolation. Perfecting features nobody asked for. Designing landing pages instead of talking to potential customers. By the time they launched, they'd built something that reflected their assumptions rather than market reality.

If you're currently evaluating SaaS ideas, this is probably the single most important filter for predicting success: can you get this in front of a paying customer within 30 days? If the answer is no — if the MVP inherently requires months of development before it's usable — the risk profile goes up dramatically.

This doesn't mean you should ship garbage. It means you should scope ruthlessly. The first version of your product should do one thing well for one type of customer. Everything else is a distraction that pushes your time-to-first-customer further out and reduces your odds.

Finding 5: The Churn Divide

Churn data was the hardest to collect — only about 40% of the products in my dataset had publicly disclosed churn rates. But within that subset, the pattern was clear enough to be worth sharing.

The dividing line was roughly 5% monthly churn.

Products below 5% monthly churn were almost all in the winner tier. Products above 8% monthly churn were almost all stalled or declining. The 5-8% range was where survivors lived — making money, but running on a treadmill where new customers barely outpaced departing ones.

What separated low-churn from high-churn products? Three characteristics appeared consistently:

1. Data lock-in. Products where customers built up meaningful data over time (CRM-like tools, analytics dashboards, documentation systems) had structurally lower churn than products where the value was transactional. If switching means losing six months of carefully organized data, customers don't switch.

2. Workflow integration. Products that became part of a daily or weekly workflow — rather than something used occasionally — churned less. A tool you open every morning is much stickier than a tool you use once a quarter.

3. Team usage. Products used by multiple people within an organization had lower churn than single-user products. When a tool is embedded in a team's process, the switching cost multiplies by the number of people who'd need to learn something new.

This has direct implications for what you choose to build. If you're comparing two potential micro-SaaS ideas and one naturally creates data lock-in while the other is a utility you use once and forget, the first one is almost certainly the better bet from a churn perspective.

The Uncomfortable Implications

When I stepped back and looked at all five findings together, a profile emerged of the "ideal" micro-SaaS — at least according to what the data says works.

It looks something like this:

• Vertical B2B (serves a specific industry)
• Priced at $39-99/month (high enough to sustain, low enough to avoid procurement)
• Acquires customers through SEO or marketplace presence (sustainable, compounding channels)
• Gets first paying customer within 30-40 days (validates fast, iterates based on real feedback)
• Creates data lock-in or becomes part of a daily workflow (structural churn defense)

That profile is, frankly, boring. It's not an AI copilot. It's not a social app. It's not a developer tool with a slick CLI. It's something like a compliance tracker for independent pharmacies, or a scheduling tool for mobile dog groomers, or a client portal for freelance bookkeepers.

And that's exactly the point. The data consistently shows that boring problems make profitable businesses. The exciting ideas attract competition, attract tire-kickers instead of paying customers, and attract founders who are in love with the technology rather than the problem.

What the Data Doesn't Show

I want to be honest about the limitations here.

This dataset is biased toward founders who publicly share their numbers. That skews toward people who are doing well (survivorship bias) and people who are active in indie hacker communities (demographic bias). The real failure rate for micro-SaaS is almost certainly higher than the 29% stalled rate in my dataset, because most people who fail don't write about it.

I also couldn't control for founder experience, technical skill, or time invested. A product that stalled might have been built by someone working two hours a week, while a winner might have had a founder going full-time from day one. These variables matter enormously, and the data can't fully account for them.

Finally, correlation isn't causation. The fact that winners tend to be vertical B2B products priced at $39+ doesn't mean that pricing your horizontal consumer tool at $39 will magically make it successful. These patterns are guideposts, not guarantees.

With those caveats stated, I still think this data is more useful than the typical "build something people want" platitude. At least it gives you specific parameters to optimize for.

Applying This to Your Next Build

If you're currently searching for your next SaaS idea — or evaluating one you already have — here's how I'd use these findings.

Step 1: Check your price point. If you're planning to charge $9-15/month, seriously reconsider. Can you target a more specific audience and charge $39+? Can you add enough value to justify $99/month to a business buyer? The data says your odds improve significantly at higher price points.

Step 2: Go vertical. If your idea is "X for everyone," narrow it to "X for [specific industry]." Pick an industry where you have some connection or can develop expertise quickly. The narrower your focus, the easier your marketing becomes and the less competition you face.

Step 3: Map your distribution before you write a line of code. Where will your first 20 customers come from? If the answer involves "going viral" or "Product Hunt launch," you need a different plan. SEO, marketplace presence, and direct outreach are the channels that actually compound. I track these kinds of distribution patterns at SaasOpportunities — it's one of the most underrated aspects of idea evaluation.

Step 4: Set a 30-day deadline for your first paying customer. Scope your MVP around this constraint. What's the absolute minimum version of your product that someone would pay for? Build that. Ship it. Get feedback from someone who's giving you their credit card number, not just their opinion.

Step 5: Design for stickiness from day one. Think about data lock-in, workflow integration, and team usage. If your product doesn't naturally create switching costs, figure out how to add them — not through dark patterns, but by genuinely becoming more valuable the longer someone uses it.

These five steps aren't revolutionary. They're not a secret growth hack or a clever shortcut. They're just what the data says works, applied as a practical checklist. Sometimes the most useful insights are the ones that confirm what experienced founders already know intuitively — and give first-time builders a concrete framework to follow instead of guessing.

The Biggest Takeaway

After six weeks with this data, the thing that stuck with me most wasn't any single finding. It was the gap between what the micro-SaaS community talks about and what actually correlates with making money.

The community talks about AI tools, launch strategies, and building in public. The data says: pick a boring industry, charge more than you think you should, invest in SEO, and get someone to pay you as fast as humanly possible.

The community celebrates Product Hunt launches with 500 upvotes. The data says those launches rarely translate into sustained revenue.

The community gravitates toward horizontal tools that scratch a developer's own itch. The data says vertical B2B products for non-technical industries win at nearly twice the rate.

This doesn't mean you should ignore your instincts or build something you hate working on. Motivation matters, and it doesn't show up in a spreadsheet. But if you're trying to maximize your probability of building something that generates real, sustained income — the kind that could replace a salary or fund your next project — the data points in a specific direction.

It points toward the unglamorous. The specific. The underpriced-by-default industries where a $49/month tool that saves someone two hours a week is genuinely life-changing.

That's where the money is. It always has been.

If you want a structured way to evaluate whether your specific idea fits these patterns, the 12 filters that predict SaaS success are a good complement to the data-driven findings here. And if you're still in the idea-hunting phase, the SaaS idea validation framework gives you a step-by-step process to move from "interesting concept" to "validated opportunity" without wasting months building the wrong thing.

The data is there. The patterns are clear. The only question is whether you'll build what the market rewards — or what sounds impressive on Twitter.