Every other list ranks by funding or traffic. We ranked 8,600+ startups by what they actually earn. The results are far less flattering, and far more useful.
Search for the fastest-growing SaaS companies and you will find lists ranked by funding rounds, by valuation, by headcount growth, or by web traffic. All four are proxies. None of them tells you whether a single customer has paid the company anything.
We track revenue directly for 8,600+ startups, so we ranked them that way instead. The result is a much less flattering picture than the usual roundups, and a much more useful one if you are trying to decide what to build or where to place a bet.
The single most important finding: of those 8,600+ startups, 4,900+ earn exactly $0 per month, and only 230 clear $10K MRR. That is 2.6%. Meanwhile the cluster of companies posting the spectacular growth percentages you see quoted everywhere, an average of 951%, has a median monthly revenue of $64.
The established rankings each measure something real, just not the thing most readers assume. Funding-based lists measure investor conviction, which is a genuine signal but a lagging and concentrated one. Traffic-based lists measure attention, which correlates with revenue loosely and with hype strongly. Headcount-growth lists measure spending, which in a well-funded company can rise while revenue does not move at all.
Revenue is the only one of these that a customer has to agree with. A company can raise a round, hire 40 people and rank on three lists without anyone paying for the product. It cannot post MRR without someone paying.
This is also why our list contains names you will not recognise. The companies with verifiable, growing revenue at the scale we track are overwhelmingly small and independent rather than late-stage venture-backed. That is a feature if you are looking for patterns you could replicate, and a limitation if you came here for the household names. We say more about that in the limitations section, and our state of indie SaaS revenue report covers the same population in more depth.
Before any ranking, here is the shape of the whole population. This single table reframes every growth statistic that follows.
| Monthly revenue | Startups | Share of all tracked |
|---|---|---|
| $0 | 4,900+ | 56.4% |
| $1 to $99 | 1,600+ | 19.1% |
| $100 to $999 | 1,200+ | 14.0% |
| $1K to $10K | 660+ | 7.6% |
| $10K to $50K | 185 | 2.1% |
| $50K+ | 45 | 0.5% |
Read the bottom two rows together. 230 companies out of 8,600+ reach $10K MRR, the rough threshold at which a SaaS product supports one founder properly. That is the real base rate, and it is the number every growth ranking implicitly hides by only showing you the top.
Our clustering splits startups by growth pattern, and the top cluster looks spectacular until you check the second column.
| Growth cluster | Startups | Avg growth | Median MRR |
|---|---|---|---|
| Hypergrowth (above 50%) | 830+ | 951.1% | $64 |
| Steady growers (10 to 50%) | 580+ | 26.0% | $268 |
| Flat / stable (0 to 10%) | 3,000+ | 0.4% | $0 |
| Declining (below 0%) | 2,200+ | -46.8% | $38 |
The hypergrowth cluster has a median monthly revenue of $64 and the steady growers, at a 36x lower growth rate, earn four times more. This is the whole trap in two rows. A startup moving from $5 to $50 MRR books a 900% gain and lands in the top cluster.
This matters because percentage growth is the number founders publish and journalists repeat. When you see “growing 400% month over month” with no base disclosed, the base is usually very small. Our analysis of how fast SaaS startups actually grow works through the arithmetic in more detail.
Applying both filters at once, revenue above $5K MRR and month-over- month growth above 10%, produces a much shorter and more credible list. These are companies where the growth percentage represents real money. We report revenue in bands rather than exact figures, because per-company revenue is self-reported and precision would imply more confidence than it deserves.
| Company | Category | Revenue band | 30-day growth |
|---|---|---|---|
| GojiberryAI | Marketing | $100K to $250K MRR | ~92% |
| Resonant Mail | Artificial Intelligence | $100K to $250K MRR | ~100% |
| Speel.co | SaaS | $50K to $100K MRR | ~253% |
| DataExpert / TechCreator | Education | $50K to $100K MRR | ~90% |
| PolyPick | Fintech | $25K to $50K MRR | ~845% |
| bullgpt.io | SaaS | $25K to $50K MRR | ~138% |
| Peekaboo | Marketing | $25K to $50K MRR | ~223% |
| Tribe Social | Community | $25K to $50K MRR | ~2,548% |
| Muapi | Artificial Intelligence | $10K to $25K MRR | ~280% |
| Career Hound | Recruiting & HR | $10K to $25K MRR | ~129% |
| Barron Tech | Sales | $10K to $25K MRR | ~417% |
| Draftly | No-Code | $10K to $25K MRR | ~4,212% |
| Laper | Artificial Intelligence | $10K to $25K MRR | ~210% |
| LeadX | Artificial Intelligence | $10K to $25K MRR | ~95% |
| TheStatsApi | Developer Tools | $10K to $25K MRR | ~212% |
| Frontproxy | Developer Tools | $5K to $10K MRR | ~135% |
| 1clickwebsite.ai | Artificial Intelligence | $5K to $10K MRR | ~116% |
| RTMP.IN | Social Media | $5K to $10K MRR | ~482% |
| SourceGeek | Recruiting & HR | $5K to $10K MRR | ~120% |
| Bazzly | Marketing | $5K to $10K MRR | ~139% |
Two patterns stand out. First, the concentration in unglamorous commercial categories: marketing, sales, recruiting, developer tools, fintech. Nine of the 20 sell to businesses that measure the return directly, which is the shortest path to a renewal. Second, the sheer scarcity. Fewer than 30 companies in a population of 8,600+ met both filters at once.
Category averages tell you where the energy is. The median column tells you whether that energy has converted.
| Category | Startups | Avg growth | Median MRR |
|---|---|---|---|
| Artificial Intelligence | 1,900+ | 272.3% | $0 |
| Entertainment | 120 | 119.5% | $0 |
| Recruiting & HR | 74 | 101.4% | $0 |
| Developer Tools | 530+ | 94.9% | $0 |
| Education | 225 | 67.3% | $0 |
| SaaS (general) | 845 | 56.0% | $0 |
| Design Tools | 125 | 58.4% | $0 |
| Marketing | 483 | 31.4% | $0 |
| Content Creation | 231 | 29.7% | $0 |
| Productivity | 573 | 23.5% | $0 |
| Mobile Apps | 417 | 13.2% | $42 |
| Social Media | 93 | 12.0% | $10 |
| Health & Fitness | 166 | 12.4% | $9 |
The inversion in that table is the finding. The four fastest-growing categories all have a median MRR of $0. The three categories where the typical company earns anything at all are the three slowest growers.
That is not a coincidence. Mobile Apps, Social Media and Health and Fitness are older, more saturated categories where the easy growth is gone but monetisation is understood. AI and Developer Tools are the reverse: enormous entry volume, monetisation unsolved. Our revenue benchmarks by category break this down further, and the saturation analysis explains why crowded is not the same as closed.
AI deserves its own section because it distorts every aggregate on this page. It is the largest category we track at 1,900+ startups, it has by far the highest average growth at 272%, and its median company earns nothing.
Our business-model clustering shows the same thing from another angle: the AI-native tools cluster contains 2,000+ startups with a median MRR of $0 and an average of $1,390, meaning a small number of earners are carrying the entire average.
The gap between entry and revenue is also visible in where builders are crowding. In our index of 30,000+ companies taking real payment through Stripe, the AI Tools and Apps category runs a 34.7% small-software density, by far the highest of any category with real volume. Compare that with Home Services and Trades at 0.4% or Nonprofit and Fundraising at 0.2%. Solo builders are concentrating in AI at roughly 90 times the rate they build for home services, and the revenue data shows how that is going.
None of which means AI is a bad category. It means the average AI startup is not a useful benchmark, and that competing there requires a distribution or workflow advantage rather than a model. We covered the durable version of this argument in building a moat in the AI era and quantified it in the AI SaaS revenue reality check.
A median of $0 across a category of 1,900 startups means more than 950 of them have never taken a payment. It is worth sitting with that, because it is the default outcome rather than an edge case.
Founders in this position often diagnose it as a traffic problem. The more experienced read is usually different:
“35 users with zero payments this early isn’t failure, it’s a signal problem, not a traffic problem.” – r/microsaas
And the failure mode that produces a $0 median is remarkably consistent: build, launch, push traffic, never establish that anyone wanted it.
“I posted on x, linkedin outbound, promos on reddit, forced users through a 10 step onboarding without knowing retention... stuck at 0.” – r/EntrepreneurRideAlong
This is the argument for starting from documented demand rather than from an idea you like. Our guide to validating a startup idea and the 8-stage validation framework both exist because the $0 median is the thing they are trying to prevent.
If you run a SaaS business, the useful comparison is against your own revenue tier, not the blended average.
| Tier | Startups | Median MRR | Avg growth |
|---|---|---|---|
| Zero MRR / pre-launch | 4,900+ | $0 | 34.8% |
| Micro-SaaS (under $500) | 2,500+ | $50 | 221.5% |
| Small SaaS ($500 to $5K) | 827 | $1,265 | 50.5% |
| Mid-market ($5K to $20K) | 255 | $8,962 | 38.2% |
| Scale-ups ($20K+) | 130 | $35,562 | 40.6% |
Growth rate falls as the base rises, exactly as you would expect, and then stabilises around 40% once a company is past $5K MRR. If you are at $2K MRR growing 30% a month, you are behind your tier median of 50.5%. At $30K MRR growing 30%, you are close to the top of the distribution. Same growth rate, opposite verdicts, which is why blended averages are useless here. Our SaaS metrics benchmarks and revenue benchmark research go deeper on the percentile view.
Growth rankings are survivorship bias in table form. Here is the other side of our data: 2,200+ startups sit in the declining cluster at an average of -46.8%, and another 640+ are classified as turnaround plays at -41.5%. Together that is nearly 2,900 shrinking companies against 830+ hypergrowth ones.
More companies in our data are losing revenue than gaining it quickly. Any list that shows only the risers is describing roughly a tenth of what is happening.
The decline is rarely about product quality. It is usually retention, and retention usually breaks at onboarding:
“People weren’t leaving because the product was bad. They were leaving because they didn’t understand the value fast enough.” – r/microsaas
“Built this beautiful multi step tour and my activation rate was 23 percent. Killed it and replaced with simple flow, activation jumped to 61 percent.” – r/microsaas
Across the companies that cleared both filters, and the founder accounts in our complaint corpus, the same handful of patterns recur. None of them is a growth hack.
They sold to businesses that could measure the return. Nine of the 20 genuine growers are in marketing, sales, recruiting or developer tools. When a buyer can attribute revenue or hours saved to your product, renewal is a calculation rather than a preference.
They narrowed until the pitch was obvious.
“Stop selling generic software... niche down and sell solutions for a specific business type... get testimonials and case studies before scaling.” – r/EntrepreneurRideAlong
They did unscalable distribution work.
“We wasted weeks obsessing over growth hacks... what actually worked was boring, manual work: commenting on reddit, directories, founder stories on linkedin.” – r/EntrepreneurRideAlong
“Cold email is dying for micro-saas imo. Response rates have cratered because everyone’s inbox is full of AI-generated outreach.” – r/microsaas
They priced for the customer actually in front of them. A recurring complaint in our corpus is the missing middle tier:
“I wish there was a ‘Solo’ plan between ‘Free’ and ‘Team’ to choose from. Going from paying nothing to paying $20 p/m is quite a big jump.” – r/airtable
That gap between free and team pricing is where a meaningful share of the $0-MRR population is stuck. More on this in SaaS pricing strategies.
Our payment index points somewhere quite different from where builders are going. Ranking categories by companies taking real money against how many small software products serve them:
| Category | Paying companies | Small-software density | Read |
|---|---|---|---|
| Home Services & Trades | 960+ | 0.4% | Money present, tools absent |
| Nonprofit & Fundraising | 640+ | 0.2% | Money present, tools absent |
| Software Dev Agencies | 590+ | 0.2% | Money present, tools absent |
| Invoicing & Billing | 490+ | 13.5% | Small tools demonstrably work |
| Accounting & Bookkeeping | 280+ | 12.1% | Small tools demonstrably work |
| AI Tools & Apps | 950+ | 34.7% | Heavily crowded by builders |
Home Services has more paying companies than AI Tools and roughly one-eightieth the small-software competition. The buyers there have been saying the same thing for years:
“Every tool I tried felt like it was built for tech companies, not for people actually doing the work.” – r/EntrepreneurRideAlong
If you are choosing a category rather than defending one, that is the trade in a sentence: build where growth percentages look exciting, or build where the money already is. Our underserved software markets research, low competition SaaS ideas and niche opportunities by industry all start from the left-hand column.
Revenue is one lens. Before drawing conclusions from it, it is worth asking whether two independent signals point the same way: where investors are putting money, and where businesses are paying humans to do work manually. Both are leading indicators of software demand, and both are measured entirely separately from the revenue data above.
On the capital side, we track momentum and investment attractiveness across 17,000+ funded companies. The ordering is instructive when set against the growth table. Cybersecurity and security lead on momentum at 5.6, followed by AI infrastructure and developer tools at 5.4 each, fintech at 5.3, and business software at 5.1. Consumer sits last at 4.3.
Compare that with the revenue picture and a pattern emerges. Developer tools rank high on investor momentum (5.4) and high on growth (94.9%) but carry a $0 median MRR, which is the signature of a category with genuine long-term demand and an unsolved monetisation model at the small end. Security is the inverse: strong investor momentum, almost no presence in our indie revenue data at 33 tracked startups, because security buyers are enterprises and enterprises do not buy from solo founders without the compliance apparatus discussed above.
Consumer is the one category where all three signals agree negatively: last on funded momentum at 4.3, and in our app review corpus the consumer categories dominate complaint volume, with Health and Fitness combined with Lifestyle accounting for 2,300+ negative reviews, the single largest block. Consumers are unhappy and investors are cool on it. That combination usually means the problem is real but monetising the fix is hard, which is exactly what the revenue data shows.
The labour side gives a different kind of signal. When businesses hire freelancers repeatedly for the same task, they are paying a person to do something software should do, which is about as direct a demand signal as exists. The most frequently recurring pain points in our freelance data are all variations on manual, repetitive process work: time-consuming manual rendering, error-prone lead generation, manual bookkeeping and reconciliation, inefficient appointment scheduling, manual product listing management, inaccurate OCR on handwritten documents, and time-consuming presentation and proposal design.
Almost none of those map to the categories with the highest growth rates. They map to invoicing, scheduling, bookkeeping and document processing, which is precisely where our payment index shows small software already succeeding: Invoicing and Billing at 13.5% small-software density, Accounting and Bookkeeping at 12.1%. Three independent datasets, measured in completely different ways, point at the same unglamorous middle of the market. Our state of freelance demand report works through that signal in full.
The practical conclusion: when growth data, capital data and labour demand disagree, trust the one closest to a transaction. Someone paying a freelancer every month to reconcile invoices is a more reliable indicator of software demand than a category posting 272% average growth on a $0 median.
One more forward-looking read. Of the top 20 launches on the largest product launch platform over the last 90 days, roughly eight are explicitly agent infrastructure: an inbox for humans and agents, browser automation for agents, rails agents use to get paid, a publishing API for agents, an open-source agent workspace, a live data marketplace for agents.
That is 40% of the most-upvoted launches in a single theme in a single quarter. Launch volume leads revenue by a year or two, so this is the cohort that will populate next year’s growth rankings, and given the $0 median in AI-native tools today, most of them will populate the declining cluster instead. We looked at whether that bet has demand behind it in our scoring of YC's Request for Startups and in SaaS ideas for AI agents.
The genuinely open frontier inside that theme is smaller than the noise suggests: of 30,000+ companies in our payment index, fewer than a dozen support machine-payable transactions. Agentic commerce is not yet a market, it is an empty room with a lot of people building doors.
Growth is a means to an outcome, so it is worth anchoring on what the outcome looks like. Across 630+ live acquisition listings we track, SaaS startups average $517K in asking price on $221K of trailing revenue, at a 10.6x profit multiple. Mobile startups average $374K at 5.1x, and agency businesses $500K at 3.0x.
Set that against the distribution above. A company at $10K MRR, which only 2.6% of tracked startups reach, is roughly $120K in annual revenue and lands squarely in that acquisition band. You do not need a growth ranking to get there, and our buying versus building analysis and guide to selling your SaaS cover both directions of that trade. Asking prices are seller-set and usually above the clearing price, so treat them as a ceiling.
A checklist you can apply to any “fastest-growing” claim, including the ones in this article.
You can run these checks yourself against the same dataset using the revenue intelligence tool and the revenue intelligence guide.
The 185 companies in the $10K to $50K MRR band are the most instructive group in the dataset, because they have solved demand and now hit a different set of walls. Their tier average growth of 38.2% is lower than the tier below them, and the reasons are consistent across founder accounts.
The first is that revenue arrives attached to operational load. A product with paying customers is a support organisation whether or not you staffed one, and for a solo founder that time comes directly out of building.
“Running everything solo kills your time for strategy... Focus on one marketing channel... Save time by finding good convos and engaging smartly.” – r/EntrepreneurRideAlong
The second is that the roadmap stops being obvious. Early on the next feature is whatever the first ten customers asked for. At $10K MRR every request comes from someone paying, and prioritisation becomes the hard problem.
“One of the hardest things I see early-stage founders deal with is feature prioritization, deciding what to build next when everything feels important. Rank every feature by impact on revenue or retention, only ship what changes those numbers.” – r/EntrepreneurRideAlong
The third is the enterprise gate. Moving upmarket is the obvious way past the ceiling, and it is where compliance cost stops small companies cold, a pattern we found across our analysis of compliance demand as well.
The fourth is quieter and more encouraging: the tools these companies need are themselves underbuilt, which is where the next tier of products comes from.
“Most brand monitoring tools are overkill for small teams and pricey, but we mainly want to see when people mention the brand and whether the conversation is positive or negative.” – r/EntrepreneurRideAlong
That complaint shape, an enterprise tool priced and scoped for someone ten times larger, recurs constantly in our corpus and is the single most reliable source of viable micro SaaS products.
Founder accounts from public forums, anonymised to the subreddit. These describe the gap between the growth numbers and the operating reality.
“The day you get your first 20 paying users your life becomes a customer support role with a side gig of coding. I spend 30% of my time building, rest is support, docs, and outages.” – r/microsaas
“Got an inbound lead. Enterprise company. They wanted to pay $2,000/month. Then their procurement team asked: ‘Do you have SOC2?’... I can’t spend $30K+ on compliance.” – r/microsaas
“Lost runway after 4 years building... considering freelancing but risk losing momentum... need genuine advice.” – r/EntrepreneurRideAlong
“Most of my growth came from conversations, not posts.” – r/microsaas
“I’m getting many sign-ups on my platform, but users aren’t engaging. I send emails that don’t get responses or interest.” – r/EntrepreneurRideAlong
“Reddit DMs work only when they don’t include the pitch in the first message. I found 100 users in 48 hours with zero budget by detailed posts in specific communities.” – r/microsaas
“I was spending more time managing software than managing my business. Every app had a login. Every login came with a new workflow. Every workflow broke when one app updated.” – r/EntrepreneurRideAlong
That last one is a buyer, not a founder, and it is the most useful quote on this page. The complaint that fuels the next tier of growing companies is not a missing feature, it is the accumulated cost of the tools people already bought.
Check any category before you build in it. Every number here came from data you can query: revenue and growth on 8,600+ startups, 30,000+ companies taking real payment, and 1M+ documented complaints. Find out what a category actually earns before you spend a year in it.
Explore the data →Every figure was queried on August 30, 2026. Counts are rounded down. Per-company revenue is reported in bands rather than exact figures. The limitation of each source is stated alongside it.
| Source | What it measures | Used for | Limitation |
|---|---|---|---|
| Revenue tracking | MRR and growth for 8,600+ startups | The distribution, tier benchmarks, the grower list | Self-reported, skewed to indie founders who publish numbers. Not a census of all SaaS |
| Growth clusters | Startups grouped by growth pattern and revenue tier | The 951%-on-$64 finding, declining cohort | Cluster boundaries are our definitions, not an industry standard |
| Category analysis | Growth and revenue rolled up by category | The category table and the AI finding | Category labels are model-assigned; some products span two |
| Stripe index | 30,000+ companies taking real payment | Small-software density, where money is | Stripe-only, so it misses other processors entirely |
| Complaint corpus | 1M+ complaints across Reddit, G2, Capterra, app stores | Founder and buyer quotes | Quotes are illustrative, not statistically representative |
| Acquisition listings | 630+ live listings with revenue and multiples | Exit benchmarks | Asking prices are seller-set and above clearing price |
| Launch data | Launch volume and votes over 90 days | The agent-infrastructure leading signal | Votes measure launch-day attention, not durable success |
This is not a census. Our revenue data skews heavily toward independent and small SaaS companies, because those are the founders who publish revenue. Late-stage private companies do not, so if you came looking for the growth rates of well-known scale-ups, this dataset cannot give them to you and neither can any public source without an NDA. What it can give you is the base rate across thousands of companies at the stage most readers are actually at.
Self-reported revenue has a known bias. Founders are more likely to publish numbers when the numbers are good, which means the real distribution is probably worse than the one shown here, not better. Our headline finding, that only 2.6% reach $10K MRR, should be read as an optimistic ceiling.
Growth rates are volatile at small scale. A 30-day growth figure on a company earning $60 a month swings wildly on a single customer. This is precisely the point of the article, but it applies to our own table too: the companies listed with four-figure growth percentages may not sustain them, and we would expect most not to.
Category labels are imperfect. A product can be an AI tool and a marketing tool at once. Where a company could sit in two categories, our classification picks one, which slightly blurs category-level comparisons.
It depends entirely on whether you measure growth in percentage terms or in dollars, and the two produce almost opposite lists. Ranked by percentage growth, the leaders are tiny: our hypergrowth cluster of 830+ startups averages 951% growth on a median of just $64 MRR. Ranked by companies that are both growing and earning real money, the list shrinks dramatically. Of 8,600+ startups we track, only 230 clear $10K MRR at all, and only a few dozen of those are also growing above 10% a month.
Use the revenue tier you are actually in, not a blended average. In our data, startups in the $500 to $5K MRR band average 50.5% growth, the $5K to $20K band 38.2%, and companies above $20K MRR 40.6%. Growth rates fall as the base rises, which is normal. The number that matters more than your growth rate is whether you are in the 44% of tracked startups earning anything at all: 4,900+ of 8,600+ are at exactly $0.
Because they measure different things. Rankings built on funding measure investor conviction. Rankings built on web traffic measure attention. Neither measures whether customers pay. When you rank by revenue instead, categories reorder sharply: Artificial Intelligence leads on growth at 272% average across 1,900+ startups but has a median MRR of $0, meaning more than half the AI startups we track earn nothing.
By average growth rate: Artificial Intelligence at 272% across 1,900+ startups, Entertainment at 120%, Recruiting and HR at 101%, and Developer Tools at 95% across 530+. But every one of those categories has a median MRR of $0. The categories where the typical company earns something are different and much less exciting: Mobile Apps at a $42 median, Social Media at $10, Health and Fitness at $9.
On its own, no. Percentage growth off a small base is the most misleading number in early SaaS. A startup going from $5 to $50 MRR grew 900%, and our hypergrowth cluster shows exactly this pattern: 951% average growth against a $64 median MRR. Growth rate becomes a meaningful signal only once it is attached to a revenue base large enough that the percentage represents real dollars, which in practice means somewhere north of $1K MRR.
For the revenue picture in more depth, the state of indie SaaS revenue and how fast SaaS startups actually grow are the two closest companions to this piece, and profit multiples by SaaS category covers what the growth converts into.
If this article has convinced you to pick a category more carefully, start with the most profitable SaaS niches, the state of micro SaaS competition and SaaS ideas backed by pain points. For the funding-side view of the same market, what VCs are funding in 2026 and startup funding trends show where capital is moving, while micro SaaS ideas and our guide to finding SaaS ideas are the practical starting points. The idea validation tool and pain point database let you test a specific idea before you become another entry in the $0 column.
BigIdeasDB Research. (2026). Fastest-Growing SaaS Companies 2026, Ranked by Revenue. BigIdeasDB. Retrieved from https://bigideasdb.com/fastest-growing-saas-companies-2026