We counted where one-person software actually clusters, then checked it against what those categories earn. The two maps barely overlap.
Every list of SaaS ideas has the same silent assumption: that the shortage is ideas. Search the term and you get fifty of them, then a hundred, then five hundred, each one a sentence of description and a category label. None of them tells you how many companies are already taking money in that category, or what those companies earn.
We can check both, because we maintain the two datasets that answer them. The Stripe Index is a snapshot of 30,322 companies listed on Stripe’s public directory, each tagged with a category, a micro SaaS flag and a buildability score. Alongside it sits a revenue-verified set of 8,699 products reporting real monthly revenue.
Laying one over the other produces an uncomfortable result. The category where solo builders concentrate hardest is not the category that pays best. It is close to the opposite.
Before the data, the argument the audience is already having. Through 2026 the loudest threads in r/SaaS are not requests for ideas. They are objections to the premise that ideas are what is missing.
“If your idea is so simple that someone can spin up a clone in a weekend with AI and a few prompts, why exactly would anyone pay you for it? Why would they trust it? Why would they trust you?”
That thread drew 746 upvotes and 494 comments, and its central claim is the one this research is a test of: “Coding is becoming the cheap part. The expensive parts are trust, distribution, support, reliability, integrations, compliance, reputation.”
The second recurring theme is that unglamorous categories outperform. From a thread at 470 upvotes: “The ones making $20k MRR right now? Boring, ugly B2B tools for unsexy industries.” The same founder put the willingness-to-pay gap in one comparison: a fitness app where “getting a consumer to pay $9.99 a month was like pulling teeth,” against “a crappy inventory management tool” where “the owner paid $500 month without blinking.”
And the third, at 852 upvotes, is an explicit rejection of novelty: “Pick an idea that’s been done before. New ideas are risky.” Echoed by a founder at 809 upvotes whose AI product died and whose plain one worked: “the idea that felt too simple, too obvious, too unsexy was the one that actually had demand behind it.”
Three claims, all testable against a population of companies actually taking money. That is what follows. Quotes are attributed to platform only, never to accounts.
Two first-party corpora, queried live on September 21, 2026. Neither is a census, and both carry the limitations listed below. The numbers are counts from those snapshots, not market estimates.
| Source | Evidence type | Volume | Limitation |
|---|---|---|---|
| Stripe Index category | AI classification from public positioning | 30,322 | A directory snapshot, not every SaaS company in existence; categories are model judgements |
| Micro SaaS flag | AI classification | 30,322 | Definitional edge cases exist; read the distribution, never a single label |
| Buildability score | AI 1-10 feasibility rating | 30,322 | Apparent difficulty from a public page, not measured engineering effort |
| Agentic flag | AI classification | 30,322 | Detects stated agent capability, which lags actual shipping in both directions |
| Revenue-verified products | Self-reported, revenue-verified MRR | 8,699 (4,000+ reporting MRR > 0) | Skews toward indie products that publish numbers, so medians run low against the whole market |
| Category medians | Median and p90 MRR | Categories with 25+ reporting products | Two taxonomies, so category names differ between the two corpora and comparison is directional |
The last limitation matters for how you read the two tables below. The Stripe Index uses operational categories such as ai-tools and home-services-trades. The revenue set uses product categories such as Artificial Intelligence and Productivity. They are not the same taxonomy, so the overlay is a directional comparison rather than a join.
2,012 of 30,322 companies, 6.6%, are flagged as one-person software businesses. That is the number every category below is measured against. Five categories sit well above it.
| Category | Companies | Micro SaaS | Share | Avg buildability |
|---|---|---|---|---|
| ai-tools | 955 | 331 | 34.7% | 5.2 |
| invoicing-billing | 495 | 67 | 13.5% | 4.3 |
| workflow-automation | 422 | 46 | 10.9% | 4.6 |
| creator-monetization | 408 | 44 | 10.8% | 6.3 |
| education-elearning | 1,278 | 127 | 9.9% | 4.5 |
| subscription-management | 762 | 75 | 9.8% | 4.4 |
| lead-generation | 532 | 50 | 9.4% | 4.4 |
| All categories (baseline) | 30,322 | 2,012 | 6.6% | — |
The gap between first and second place is the finding. ai-tools at 34.7% is not merely the most crowded category, it is 2.6 times denser than the next one, invoicing and billing at 13.5%. One category absorbs the attention of solo builders in a way no other does.
The buildability column explains why. ai-tools averages 5.2 out of 10, the highest of any large category except creator monetisation at 6.3. It is the easiest large category to ship into, so it is the one people ship into. That is a rational individual decision that produces an irrational collective outcome, and we found the same pattern in buildability scores when we looked at MVP size.
Crowding only matters if you know what the prize is. Across the revenue-verified set, restricted to categories with at least 25 products reporting MRR above zero, the spread between best and worst is fourteen-fold.
| Category | Products reporting MRR | Median MRR | p90 MRR |
|---|---|---|---|
| Sales | 38 | $640 | $14,463 |
| Marketing | 213 | $276 | $10,495 |
| Education | 175 | $208 | $4,000 |
| Artificial Intelligence | 941 | $203 | $5,005 |
| Recruiting & HR | 42 | $168 | $4,746 |
| SaaS (general) | 353 | $156 | $6,398 |
| E-commerce | 55 | $143 | $8,151 |
| Real Estate | 34 | $132 | $2,337 |
| Content Creation | 172 | $123 | $4,378 |
| Health & Fitness | 190 | $101 | $2,453 |
| Analytics | 87 | $77 | $6,953 |
| Developer Tools | 165 | $68 | $3,506 |
| Productivity | 223 | $46 | $639 |
Sales carries a median MRR of $640, the highest of any measured category, from just 38 products reporting revenue. It is both the best-paying category and one of the thinnest. That combination is what an opportunity looks like in this data.
Productivity is the inverse. 223 products reporting revenue, a median of $46, and a 90th percentile of $639. Every other category has a tail that rewards the products that win; Productivity’s p90 is roughly what Sales pays at the median. There is no upside to survive into.
Note also the p90 column against the median. E-commerce has a modest $143 median but an $8,151 p90, and Analytics pairs a $77 median with a $6,953 p90. Those are lottery-shaped categories: most products earn little, a few earn a lot. Sales is the rare category that is strong at both ends.
Put the two maps together and the misallocation is plain. This is the central table of the research.
| Where builders go | Micro SaaS share | Nearest revenue category | Median MRR | Read |
|---|---|---|---|---|
| ai-tools | 34.7% | Artificial Intelligence | $203 | Most crowded, middling pay |
| workflow-automation | 10.9% | Productivity | $46 | Crowded, worst pay |
| creator-monetization | 10.8% | Content Creation | $123 | Crowded, low pay |
| lead-generation | 9.4% | Sales | $640 | Moderate crowding, best pay |
| crm | 6.6% | Sales | $640 | At baseline, best pay |
| ecommerce-platform | 0.8% | E-commerce | $143 | Nearly empty of solo builders |
| home-services-trades | 0.4% | (no close match) | — | 963 buyers, 4 solo products |
Two rows carry the argument. The most crowded category in the index pays $203 at the median, and the best-paying category sits at or near the 6.6% baseline. Lead generation and CRM both map to Sales, and neither is remotely as contested as AI tooling.
This is the numeric version of what the founder in that 470-upvote thread described. Boring B2B tools for unsexy industries pay better than the category everyone is racing into, and they pay better because everyone is racing into the other one. The same logic runs through what micro SaaS actually charges, where the median price point is $25 and 82% of products offer no free tier.
The clearest signal in the whole dataset is a large company count paired with a micro SaaS share far below 6.6%. It means the category has demonstrated buyers and almost no one-person software serving them.
| Category | Companies taking payments | Micro SaaS | Share |
|---|---|---|---|
| ecommerce-platform | 3,452 | 29 | 0.8% |
| consulting | 1,496 | 13 | 0.9% |
| travel-hospitality | 1,463 | 20 | 1.4% |
| marketplace | 1,479 | 34 | 2.3% |
| home-services-trades | 963 | 4 | 0.4% |
| nonprofit-fundraising | 647 | 1 | 0.2% |
| software-dev-agency | 593 | 1 | 0.2% |
Home services and trades has 963 companies taking payments and four one-person software products serving them. Nonprofit and fundraising has 647 companies and one. These are not niches nobody wants; they are niches nobody who reads idea lists wants.
Be honest about why. Low micro SaaS share is partly opportunity and partly warning. These categories score low on buildability, home services at 3.3 and nonprofit at 3.2 against ai-tools at 5.2, and they demand domain knowledge, integrations and a sales motion rather than a launch post. The emptiness is a real barrier, not an oversight. It is also why the barrier holds once you are through it, which is the thing an AI wrapper never gets.
One more open room worth naming. Only 1,159 of 30,322 companies, 3.82%, are flagged agentic. After two years of agent discourse, fewer than four in a hundred companies taking payments describe themselves that way. We mapped where that concentrates in the state of micro SaaS competition.
The practical output of this research is a two-number test you can run on any idea before you write code. Neither number alone is enough, which is why idea lists that carry neither are not decision tools.
Run that on the default 2026 idea and it fails on step two: an AI tool enters the one category at 34.7% density with a $203 median. Run it on an invoicing product for home services contractors and it passes on one and two, and the work shifts to where the Reddit thread said it would, which is trust, integrations and distribution rather than code.
That is the honest answer to what SaaS ideas make money in 2026. Not a list of fifty. Two numbers, applied to whichever idea you already have, and the discipline to drop it when the numbers say the room is full.
By revenue per product, sales tooling leads. Across revenue-verified products the Sales category carries a median MRR of $640, against $276 for Marketing, $208 for Education, $203 for Artificial Intelligence and $46 for Productivity. Sales reaches that median from only 38 revenue-carrying products, making it the least crowded of the high-paying categories.
They are the most crowded option, not the most lucrative one. 34.7% of the ai-tools category is micro SaaS against a 6.6% baseline, which is 5.3 times the average, and Artificial Intelligence has more revenue-carrying products than any other category, 941, at a median MRR of $203.
The largest populations of paying companies have almost no one-person software in them. Ecommerce platforms: 3,452 companies, 0.8% micro SaaS. Home services and trades: 963 companies, 0.4%. Consulting: 1,496 and 0.9%. Nonprofit and fundraising: 647 companies and a single micro SaaS product.
Founders increasingly say it is not, and the data agrees. Idea supply concentrates in the easiest-to-build category, ai-tools at buildability 5.2, and that category pays a median of $203 per month. As the most-upvoted version of the argument put it, coding is becoming the cheap part while trust, distribution, support, reliability, integrations, compliance and reputation stay expensive.
Productivity, on the numbers. 223 revenue-carrying products, the lowest median MRR of any measured category at $46, and a 90th percentile of only $639. Most categories have a tail that rewards winners. Productivity effectively does not.
2,012 out of 30,322 classified companies, or 6.6%. That is the baseline every category should be read against.
The revenue data leans B2B at the category level: Sales at $640 and Marketing at $276 sit well above consumer-leaning categories such as Health & Fitness at $101 and Entertainment at $76. We broke the segment question down in detail in who micro SaaS actually sells to.
3.82%, or 1,159 of 30,322 companies. Agentic products remain a small share of everything taking payments.
Read two numbers together. The count of companies already taking payments proves buyers exist; the micro SaaS percentage measures how many solo builders are already there. High count with low share is an underserved market. A high share, as in ai-tools at 34.7%, means the race is already running.
Two first-party corpora queried live on September 21, 2026: the Stripe Index, an AI-enriched snapshot of 30,322 companies on Stripe’s public directory, and our revenue-verified set of 8,699 products. Limitations are listed in full in the methodology table above.
BigIdeasDB Research. (2026). SaaS Ideas That Make Money: 30,000+ Companies, Checked Against Revenue. BigIdeasDB. Retrieved from https://bigideasdb.com/saas-ideas-that-make-money