We located 436 companies taking real Stripe payments from Lovable, Replit, Vercel and Netlify subdomains. Six have reached established. The tool you pick turns out to predict the business you end up with.
The most upvoted honest post in r/vibecoding this month is not a demo. It reads: "None of my vibecoded projects have made any money, but my portfolio of vibecoded projects landed me a 100k/yr job." It drew 1,242 upvotes and 269 comments, in a subreddit where the surrounding posts are people shipping a Photoshop alternative in an afternoon.
That gap between what gets built and what earns is the actual question, and it is usually argued with anecdotes. We went looking for evidence instead.
Companies that take payments through Stripe appear in Stripe's public directory, and a project built on a vibe-coding platform that never moved to a custom domain still carries the platform in its URL. So we searched 30,000+ companies in our Stripe Index for websites still sitting on lovable.app, replit.app, vercel.app and netlify.app subdomains. We found 436.
These are not demos. Every one of them is set up to take money. What happened next is the interesting part.
The practical read for a founder: the build step is no longer your constraint and has not been for a while. If your vibe-coded product has no users, the tool did not fail you. The choice of what to build did.
Our Stripe Index holds 30,000+ companies listed on Stripe's public directory, each enriched with a classification layer covering category, business model, maturity and whether the product reads as micro SaaS. Every company in it is, by definition, set up to accept payments.
We matched the resolved domain and website fields against the four platform subdomains that vibe-coding tools hand out by default. A company still on something.lovable.app was built on Lovable and never migrated. That gives us a verifiable, if partial, fingerprint of the tool used.
For completeness we also checked two older no-code hosts. Bubble returned 3 companies and Webflow 8, both too small to analyse, so they are excluded from everything below.
| Limitation | Effect on the findings |
|---|---|
| This is the unmigrated cohort, not all vibe-coded businesses | The moment a builder buys a custom domain, this method cannot see them. Any successful product almost certainly migrates. So 436 is a floor, not a count, and the maturity mix is biased toward the early and the abandoned. We do not claim 436 is how many vibe-coded businesses exist, nor that 6 of 436 is a success rate. |
| Comparisons between platforms are the usable finding | The same migration bias applies to all four platforms, so the differences between them survive it even though the absolute levels do not. |
| Stripe directory only | Products monetising through app stores, Paddle, Lemon Squeezy or ads are invisible here. |
| Maturity and micro-SaaS labels are model-assigned | Classification is automated, so treat band boundaries as estimates rather than audited facts. |
| Vercel and Netlify are not vibe-coding tools | They are deployment hosts used by many workflows, including conventional development. Read them as "developer-adjacent" rather than as AI-built, which is why we separate them from Lovable and Replit throughout. |
| Platform | Companies | Micro SaaS | Avg buildability | Indie | Growing | Established |
|---|---|---|---|---|---|---|
| Vercel | 157 | 45.2% | 5.89 | 98 | 58 | 1 |
| Lovable | 119 | 21.0% | 4.92 | 87 | 29 | 3 |
| Netlify | 102 | 15.7% | 5.20 | 60 | 40 | 2 |
| Replit | 58 | 41.4% | 5.79 | 39 | 19 | 0 |
Vercel leads on volume, which is unsurprising given it hosts far more than AI-built projects. Lovable at 119 is the striking number: a tool aimed squarely at non-technical builders has put more paying businesses on its own subdomain than Replit and is within range of Netlify, a general-purpose host with a decade's head start.
Read the last column. One established company on Vercel. Three on Lovable. Two on Netlify. Zero on Replit. Six in total, out of 436.
Even allowing generously for migration bias, the shape is hard to miss: the population is overwhelmingly indie, with a thin layer of growth and essentially no maturity. 284 of the 436 are classified indie, which is 65%.
The honest interpretation is not "vibe coding fails." It is that this cohort is young and the survivors leave. But it does establish something useful: being able to charge money is not the bottleneck. Every one of these 436 cleared that bar. Whatever separates a weekend project from a business happens after the payment link works, and the tooling boom has not touched it.
That matches what we measured in how long it takes to grow a SaaS, where the constraint is time and distribution rather than shipping speed.
The 436 only mean something next to the other 30,000+ companies in the index. Comparing the vibe-hosted cohort against the full population produces the sharpest result in this study.
| Measure | All indexed companies | Vibe-hosted (436) | Difference |
|---|---|---|---|
| Classified as micro SaaS | 6.6% | 31.2% | 4.7x more |
| Reached established | 24.6% | 1.4% | 17.6x less |
| Average buildability score | 4.18 | 5.45 | Easier to build |
| Classified as agentic | 3.8% | 4.1% | No difference |
Three of those rows tell one story. The vibe-hosted cohort is 4.7 times more likely to be micro SaaS, builds things that score meaningfully higher on buildability, meaning simpler to construct, and is 17.6 times less likely to have reached established.
Simpler products, more of them small software, almost none mature. Even after discounting heavily for the migration bias, a gap of 24.6% against 1.4% is not something selection alone comfortably explains. The buildability number is the tell: this cohort is not attempting the hard things.
The fourth row is a genuine null and worth stating because it cuts against expectation. Agentic classification is 3.8% across the index and 4.1% here, statistically indistinguishable. Despite AI tooling being the thing that built these products, they are no more likely to be agentic products themselves. Builders are using AI to make ordinary software, not to make AI-native software. That is consistent with what we found mapping the agent connector landscape, where genuinely agentic products remain a thin frontier rather than a wave.
Abstractions hide the texture, so here is a random sample of the real companies, named because Stripe's directory is public, with the problem each says it solves.
| Company | Platform | Category | Problem it solves |
|---|---|---|---|
| Lumiere | Replit | AI tools | Music-synced cinematic videos from a script and photos, in the browser |
| ResaleAI | Replit | Online store tools | Resellers waste time creating listings, pricing and cross-posting |
| Easy Budget | Lovable | Fintech | A simple way to track money and stay on budget |
| Door | Lovable | No-code tools | One-file animation toolkit for DaVinci Resolve, no manual keyframes |
| PastorOfFitness | Lovable | Scheduling | Booking and payment for structured personal training programs |
| Exito Consulting | Lovable | Consulting | Helps small businesses organise operations and plan growth |
The pattern in the sample matches the category data. The Replit entries are recognisably software products solving a workflow problem. The Lovable entries skew toward personal finance, a coaching booking page and a consulting firm, which are businesses with a payment page rather than software products.
ResaleAI is the most instructive of the set, because it is the shape that tends to work: a narrow, dull, repetitive task that a specific group performs constantly, automated. That is the profile we keep finding in single-feature micro SaaS ideas and micro SaaS ideas from the Stripe Index, and it is notably not what most of this cohort is doing.
Micro-SaaS share splits the four platforms cleanly into two pairs. Vercel 45.2% and Replit 41.4% against Lovable 21.0% and Netlify 15.7%.
The developer-oriented platforms produce roughly twice the proportion of software products. The builders who land on Lovable and Netlify are disproportionately building something else: courses, services, storefronts, marketplaces. Average buildability scores point the same way, 5.89 and 5.79 for the developer platforms against 4.92 for Lovable.
This is worth pausing on, because the marketing for these tools is broadly identical. All of them promise you can build an app. In practice the population that self-selects into each one ends up making categorically different things, and if you are choosing a tool, you are also making a quiet bet about which kind of business you will end up in. Our breakdown of who micro SaaS actually sells to is a useful companion when making that bet deliberately.
| Category | Companies | Lovable | Vercel | Micro SaaS share |
|---|---|---|---|---|
| AI tools | 38 | 6 | 19 | 73.7% |
| Ecommerce platform | 34 | 7 | 10 | 2.9% |
| Marketplace | 31 | 10 | 10 | 6.5% |
| Scheduling and booking | 24 | 7 | 6 | 20.8% |
| Courses and coaching | 22 | 12 | 3 | 22.7% |
| Consulting | 22 | 7 | 5 | 4.5% |
| Education and elearning | 20 | 3 | 8 | 35.0% |
| Fintech and banking | 17 | 5 | 7 | 58.8% |
| Software dev agency | 13 | 5 | 4 | 0.0% |
Two things stand out. Lovable's single biggest category is courses and coaching, where it accounts for 12 of 22 companies, four times Vercel's share. A tool sold as an app builder is, in practice, being used heavily to stand up course and coaching businesses.
Vercel's is AI tools, 19 of 38, and that category runs at 73.7% micro SaaS density inside this set, more than double the index-wide rate. The developer crowd is building AI software; the no-code crowd is building information businesses with a payment page.
Note also the software dev agency row: 13 companies, 0% micro SaaS. These are agencies that spun up a site on a platform subdomain and took payments. Not a product at all, which is a reminder that "built with an AI tool" and "is a software product" are different claims.
Six of the 436 are classified established. We expected the list to be the best software products in the set. It is not. Here it is in full.
| Company | Platform | What it is | Micro SaaS | Buildability |
|---|---|---|---|---|
| Eugene's Inc | Vercel | Restaurant with reservations and event inquiries | No | 2 |
| Dhev Digital Solutions | Lovable | Agency building custom systems for small businesses | No | 2 |
| NN Wellness | Lovable | Non-medical daily assistance for elderly people | No | 2 |
| Stacy's Auto Sales | Netlify | In-house auto finance with online payments | No | 6 |
| Floraline | Netlify | Florist taking orders for delivery, weddings and events | No | 4 |
| Diaz MultiService Express | Lovable | Travel agency handling bookings and paperwork | No | 7 |
A restaurant, an agency, an elderly care service, a used car dealer, a florist and a travel agency. Not one of the six is a software product. Every single one is classified as not micro SaaS, and four of the six score 2 to 4 on buildability, meaning what was built is a website with a payment path.
Read that against the rest of the cohort, which is 31.2% micro SaaS and heavily concentrated in AI tools. The people trying to build software companies with these tools are, in this dataset, entirely absent from the established column. The people who reached established were already running a business and used a vibe-coding tool to put it online.
That is the most useful sentence in this study. The tool did not create those six companies. A florist was already a florist. What the tool replaced was a web developer, and it did that job well enough that the business now takes payments through it.
There is a real opportunity hiding in that observation, and it is the opposite of what most of this cohort is chasing. The durable use of these tools right now is giving existing offline businesses a digital front door, not launching new software into the most crowded categories on the internet. That is the same structural gap we keep measuring in service business ideas and small business software pain points: enormous numbers of operating businesses, very little software serving them.
The target-customer split explains why so little of this matures into software revenue.
| Target customer | Companies | Share | Micro SaaS share |
|---|---|---|---|
| Consumer | 247 | 56.7% | 30.8% |
| SMB | 101 | 23.2% | 25.7% |
| Prosumer | 52 | 11.9% | 57.7% |
| Mid-market | 21 | 4.8% | 9.5% |
| Enterprise | 12 | 2.8% | 8.3% |
| Developers | 3 | 0.7% | 33.3% |
56.7% target consumers. Consumer is the hardest segment to monetise for a solo builder with no distribution: low willingness to pay, high churn, and an acquisition cost you cannot cover without an audience. Only 2.8% target enterprise and 0.7% target developers, the two segments where a small product can charge enough per customer to survive on a handful of them.
The prosumer row is the interesting one. It is small at 52 companies, but 57.7% of them are micro SaaS, the highest density of any segment here. Prosumer buyers pay for tools that make them better at something they already do, and they are reachable without an enterprise sales motion. Our analysis of who micro SaaS actually sells to found the same segment punching above its weight, which makes its near-absence from this cohort a genuine missed opportunity rather than a quirk.
The three largest categories in this set are AI tools, ecommerce platform and marketplace. Those are crowded categories by any measure, and ecommerce platform in particular is the single most crowded category in the whole index.
The mechanism is not mysterious. The tools made it easy for everyone at once, and everyone had roughly the same idea at roughly the same moment. Speed is not an advantage when the advantage is universally distributed. We treat category crowding in depth in SaaS market saturation, and the counter-move, picking a category full of operating businesses rather than full of builders, is the argument in boring industries begging for micro SaaS and boring business ideas.
There is a second-order effect worth naming. When a category fills with products built in an afternoon, the cost of evaluating them rises for buyers, and the winners are increasingly chosen on trust signals rather than features. A buyer facing forty near-identical AI tools does not compare feature lists; they pick the one that looks maintained, answers support questions and has a real domain. That favours whoever has distribution or a reputation, which is precisely what a new builder lacks. Shipping speed moves you to the starting line faster and does nothing about the race, and it is why so few of the 436 have moved past indie.
OpenAI released GPT-6 Astra on September 3, 2026, with a 1M-token context window and state-of-the-art results on software engineering and computer use, alongside a major Codex upgrade. Within two weeks it dominated the vibe-coding conversation: three of the top dozen posts in r/vibecoding over the past month are Astra builds, including a Photoshop alternative at 1,810 upvotes and 668 comments, and an interactive history-of-Earth site built in roughly thirty minutes.
It is a genuine capability jump. It is also, for the purposes of this question, irrelevant. Every one of the 436 companies above had already cleared the build step and the payment step without it. Astra makes the part that was already solved faster and cheaper.
If anything it sharpens the problem. Lowering the cost of building raises the number of people who build the same crowded thing, which pushes the scarce resource further toward knowing what to build. The pricing makes that concrete: Astra runs at $10 per million input tokens and $50 per million output, so a capability that used to require a developer now costs a few dollars per project. When the constraint falls that far for everyone simultaneously, it stops being a source of advantage for anyone, which is the pattern we described in the state of AI tools. The one thread in that subreddit about a builder who reduced Astra usage by 98% through orchestration drew 530 upvotes; the thread about making no money drew 1,242. Both are about efficiency, but only one is about the right problem.
The community is more clear-eyed about this than its reputation suggests. Anonymised, attributed to subreddit only:
"None of my vibecoded projects have made any money, but my portfolio of vibecoded projects landed me a 100k/yr job." (r/vibecoding, 1,242)
"A guy i work with told me once, 'Im gonna quit my job and vibe code apps with AI.' I said, 'so just like the other billion people out there trying to do the same thing?'" (r/vibecoding)
The first is the honest outcome of a portfolio of well-built products nobody needed: the work had value, but as evidence of skill rather than as a business. The second names the distribution problem in one sentence. Neither is a complaint about the tools.
Put the two halves of this study together and a strategy falls out that almost nobody in the cohort is running.
The cohort that is trying to build software businesses concentrates in AI tools, ecommerce and marketplaces, targets consumers 56.7% of the time, builds things scoring 5.45 on buildability, and has produced zero established software companies in this dataset. The six that reached established were offline businesses that used the tool as a web developer.
The inversion is this: the reliable money in the vibe-coding era is currently going to businesses that existed before the tool, and to the people who build for them. A florist taking orders online is a real outcome. So is the agency in that list, Dhev Digital Solutions, whose entire stated business is building custom digital systems for small businesses, which is to say selling this capability as a service to people who will never open Lovable themselves.
That agency model is worth sitting with, because it is the one profile in the established list that deliberately monetised the tooling shift rather than merely using it. Building for operators beats building for builders, and the operator market is measurably underserved: the categories full of real businesses are the ones emptiest of software, which is the whole argument in boring business ideas and service business ideas.
The second inversion is segment. Consumer is 56.7% of this cohort and the hardest place to make a small product pay. Prosumer is 11.9% and the densest in micro SaaS at 57.7%. If you are going to build software rather than websites, the data points at prosumer and SMB buyers who already pay for tools, not at consumers who need to be persuaded that paying for software is normal. The complaint evidence for those segments is exactly what our complaint analysis platform is built to surface, and the gap between what a buyer objects to and what a user endures is mapped in who complains about what in software reviews.
None of this requires better tooling. It requires picking differently before the first prompt, which is a research problem rather than an engineering one. That is why the honest advice in the wake of a model release is usually not to go and build faster. Validation frameworks like multi-signal validation and niche viability checks matter more, not less, when the build step collapses toward zero.
One more read of that first quote is worth it. The builder got a $100k job out of a portfolio of products nobody bought. That is not a failure story, and treating it as one misses what actually happened: the work demonstrated capability, and capability was the thing with a buyer. If you are building to learn or to get hired, this cohort says you are doing it right. If you are building to be paid by customers, it says the skill you are practising is not the skill that is scarce.
It cannot tell you a success rate. The migration bias is severe and one-directional: products that work buy a domain and disappear from this method. Anyone quoting "6 out of 436" as a failure rate for vibe coding, including us, would be misreading it.
It also carries no revenue. Stripe's directory tells us a company is set up to take payments, not that anyone paid, or how much. A company here could be earning six figures or nothing at all, and we cannot tell the two apart.
What it does establish is comparative, and the comparison is sound because the same biases apply to all four platforms: developer-oriented tools produce about twice the micro-SaaS share, Lovable concentrates in courses and coaching, and the whole cohort piles into crowded categories.
A note on what would change our mind. If the migration hypothesis is right, then the successful vibe-coded businesses are sitting on custom domains and invisible to this method, and the honest test is to look for them directly rather than infer them. That means tracking a cohort of products from launch rather than sampling the survivors, which is the study we would run next and cannot run retroactively. Until somebody does, treat every confident claim about vibe-coding success rates, in either direction, as unevidenced, including the confident claims made by the companies selling the tools and by the people selling courses about the tools.
Stop treating build speed as the variable. 436 people already got to a working paid product. If that were the hard part, the established column would not read 1, 3, 2, 0.
Pick the category before the tool. The category mix here shows builders converging on AI tools, ecommerce and marketplaces, which is where competition is thickest. Choosing an underserved category is worth more than any model upgrade, and it is what idea validation and validating a startup idea are actually for.
Know what your tool biases you toward. If you pick Lovable you are statistically likely to end up with a course or service business; if you pick a developer platform you are likelier to end up with software. Neither is wrong, but drifting into one unintentionally is.
Buy the domain. A trivial point the data makes loudly: six of the 436 look like real businesses, and everyone who became one left this dataset. Staying on a platform subdomain is itself a signal about how seriously the product is being treated. If you need help there, see how to choose a domain name.
The upstream work matters more than any of it. Start from documented demand using the idea validation tool, how to find SaaS ideas and market research tools, then let the build tool do the part it is genuinely good at. Our complaint analysis platform and micro SaaS ideas both start from the problem rather than the stack.
The build step is solved. The choosing step is not. BigIdeasDB indexes 1M+ complaints alongside the Stripe Index, so you can pick a category with documented demand before you open a single prompt.
Find something worth building →Some do, but very few mature. We identified 436 companies taking Stripe payments directly from vibe-coding platform subdomains. Of those, 6 are classified as established businesses and 284 are indie. Taking payments is common; becoming a real business is rare.
Developer-oriented ones. 45.2% of Vercel-hosted and 41.4% of Replit-hosted companies classify as micro SaaS, against 21.0% for Lovable and 15.7% for Netlify.
Course and coaching businesses more than anything else. 12 of the 22 course and coaching companies in the set are Lovable-hosted, its largest category. Vercel's builders concentrate in AI tools, taking 19 of 38.
Usually because of what was built rather than how well. These companies cluster into AI tools, ecommerce and marketplaces, among the most crowded categories in the index. Building faster does not help when the tool made it equally easy for everyone competing with you.
It changes the build step, not the choosing step. Astra shipped on September 3, 2026 with a 1M-token context window and state-of-the-art software engineering performance. But all 436 of these companies already shipped and took payments without it. Their constraint was never code generation.
BigIdeasDB Research. (2026). Do Vibe-Coded Apps Make Money? We Found 436. BigIdeasDB. Retrieved from https://bigideasdb.com/do-vibe-coded-apps-make-money