Somebody on r/microsaas asked for the number without the highlight reel. We matched founding year to trailing revenue across 630+ businesses listed for sale to find it.
A commenter on r/microsaas asked the question this article exists to answer: “how long did it honestly take, not the highlight-reel version?”
It never gets a straight answer, because the people best placed to answer it are selling a course, and the ones who took eight years do not write threads about it. So the visible sample is the fast one. That distortion is the same one documented in startup failure statistics and why startups fail.
There is a dataset that does not have that problem. When a founder lists a business for sale, they publish the founding date and the trailing revenue in the same document. Match those two fields across enough listings and you get an age-to-revenue curve built from disclosure rather than from memory.
We did that for 636 listings. Median revenue goes from about $5,167 a month in the first year to about $20,250 a month at nine years and older. Roughly four times, in roughly nine years. That is the honest version. For what those businesses charge to get there, see what micro SaaS actually charges; for what they build, see micro SaaS examples.
It recurs constantly and almost always goes unanswered with numbers. From r/Entrepreneur: “How long does it take to actually gain momentum?” and, in the same thread, “Which leads to my initial question: how long do I have to wait?”
From r/SaaS: “I wonder how long it took to reached to the 40 paying clients and what are your organic channels?” From r/microsaas, on a validation thread: “how long did you wait before knowing?” And on a first-users post: “How long did it take?”
From r/SideProject: “How long did it take you to build this?” From r/SaaS again: “How long have you been building for?” From r/microsaas: “How long have you been promoting this, and did you do SEO work on it?”
The version with money attached, from r/startups: “How realistic is it to aim for 7 digit USD acquisition after 3-4 years of running a startup?”
The source is our sell-side dataset, 656 live listings scraped from acquire.com, which describes itself as the largest marketplace for buying and selling profitable online businesses and reports $500M+ in closed deal volume and 2,000+ startups sold.
Two fields do the work: date founded and trailing twelve month revenue. Founding dates are free text, so we parsed a four-digit year with a regular expression, which succeeded on 638 of 656 listings. Restricting to listings with both a parsed year and a revenue figure leaves 636 businesses. Age is computed against 2026.
Monthly figures throughout are trailing twelve month revenue divided by twelve, not a live MRR reading. All queries were re-run on September 10, 2026.
| Source | Evidence type | Volume used | Limitation |
|---|---|---|---|
| Sell-side listings | Live acquisition listings with disclosed financials | 636 with age and revenue | Total survivorship bias: every listing has revenue above zero |
| Founding date | Seller-entered free text | 638 of 656 parsed | Self-reported; sellers have a mild incentive to look established |
| TTM revenue and profit | Seller-disclosed trailing twelve months | 656 revenue, profit where present | Pre-diligence figures, not audited |
| Asking price and multiples | Listed ask and derived multiples | 636 | Asks, never closing prices |
| Reddit ICP corpus | Question sentences from founder subreddits | 2,904 ICP-filtered | Self-selected communities; quotes are illustrative, not representative |
| Paying customers field | Listed customer count | Excluded entirely | Not a customer count: holds the leading integer of a churn band string (“5-10% Stable” -> 5) |
| Monthly churn field | Listed churn rate | Excluded entirely | Column is 100% NULL, but the churn bands survive in the raw record |
This has to come before the numbers rather than after them.
Every business in this dataset had revenue. Not one is at zero, because a business at zero is not worth listing. So this is not “how long does it take,” it is “how long did it take for the ones that got somewhere.”
The denominator lives elsewhere. Our startup failure statistics analysis found that zero is the single most common revenue outcome, with most products stalling rather than dying. Read that first if you want the base rate. Read this for the shape of the curve once you are on it.
The US Census Business Dynamics Statistics programme publishes firm birth and shutdown rates across the whole economy if you want a formal treatment of survival, though it covers all firms rather than software specifically.
| Age | n | Median MRR | 25th pct | 75th pct | Median ask | Profit multiple | Median margin | % over $10k/mo |
|---|---|---|---|---|---|---|---|---|
| 0-1 yr | 105 | $5,167 | $2,417 | $16,667 | $119,500 | 3.60x | 61.3% | 36.2% |
| 2 yrs | 97 | $6,500 | $3,167 | $22,083 | $140,000 | 3.00x | 64.1% | 42.3% |
| 3 yrs | 118 | $8,333 | $3,125 | $20,417 | $157,500 | 3.35x | 49.0% | 42.4% |
| 4-5 yrs | 114 | $9,542 | $4,208 | $28,708 | $150,000 | 2.90x | 52.0% | 50.0% |
| 6-8 yrs | 125 | $14,833 | $5,583 | $33,333 | $300,000 | 3.90x | 39.9% | 62.4% |
| 9+ yrs | 77 | $20,250 | $5,750 | $41,667 | $433,700 | 3.50x | 39.1% | 68.8% |
$5,167 to $20,250 is a 3.9x increase. It is spread across roughly nine years, which works out to something in the region of 16% compound growth per year at the median.
Sixteen percent a year is a perfectly respectable business and a completely unremarkable story, and it is roughly what the state of indie SaaS revenue and TrustMRR benchmarks show from other angles. Nobody writes a thread about it. That gap between what is normal and what is posted is the entire reason this question keeps getting asked.
For the percentage-growth framing rather than the absolute one, see how fast SaaS startups actually grow and SaaS metrics benchmarks.
This is the finding we did not expect. Median profit multiple by age band: 3.60x, 3.00x, 3.35x, 2.90x, 3.90x, 3.50x. There is no trend in that sequence, just noise inside a narrow band. Revenue multiples behave the same way: 2.25x, 1.90x, 2.00x, 1.60x, 2.15x, 2.00x.
The common assumption is that a longer operating history earns a premium, because it demonstrates durability. In asking prices, it does not. A nine-year-old business is worth more than a one-year-old business almost entirely because it earns more, not because each dollar of its earnings is valued more highly.
Median asking price does climb, $119,500 to $433,700, but that is the profit growing underneath a static multiple. Compare against SaaS valuation multiples and profit multiples by category, which slice the same market by category and price band instead of by age.
Median margin by age: 61.3%, 64.1%, 49.0%, 52.0%, 39.9%, 39.1%. It falls by roughly a third across the span.
A one-year-old product is one person and a server bill. A nine-year-old product has support obligations, legacy customers on legacy plans, accumulated tooling, and probably a contractor or two. The revenue grew and the cost base grew faster in percentage terms.
This matters for anyone planning an exit, because a flat multiple applied to a thinning margin means the compounding is slower than the revenue chart suggests. See how to sell your SaaS and the state of SaaS acquisitions.
The quartiles tell a different story from the medians, and a more useful one.
The 75th percentile grows from $16,667 to $41,667 a month, which is 2.5x. The 25th percentile grows from $2,417 to $5,750, which is 2.4x but from a base so low that it stays small in absolute terms. The distance between a good outcome and a poor one widens from about $14,000 a month to about $36,000 a month.
Time is not a rising tide here. It is a sorting mechanism. What does the sorting is mostly distribution and category choice, covered in how to find a profitable niche, the most profitable SaaS niches, and niche opportunities by industry.
A quarter of nine-year-old businesses in this dataset are still under $5,750 a month. They have been going for nearly a decade and they are roughly where a strong first year lands.
That is the most sobering number in the article, and it is the honest counterweight to the median. Persistence is necessary and it is visibly not sufficient. Related reading: why startups fail and failed business ideas and what they teach.
The share of listings above $10,000 a month climbs steadily: 36.2%, 42.3%, 42.4%, 50.0%, 62.4%, 68.8%. It takes four to five years for a coin flip, and even at nine years and older roughly three in ten are still below it.
$10,000 a month is a common definition of the point where a product replaces a salary, the milestone tracked in simple SaaS ideas for solo developers and the solopreneur SaaS toolkit. On this evidence, half of the businesses that make it far enough to be sellable take five years to get there. Compare with the first $1K MRR and solo developer revenue examples.
Slicing by cohort rather than by age band gives the same picture from another angle. Median trailing revenue by founding year runs: 2025 $68,500, 2024 $78,000, 2023 $100,000, 2022 $108,500, 2021 $125,500, 2020 $169,000, 2019 $233,500.
It is monotonic across seven consecutive cohorts, which is reassuring for the reliability of the age effect. Older cohorts thin out and get noisier, so we stop reporting them individually past 2019.
| Type | Listings | Median age at listing | Median MRR |
|---|---|---|---|
| SaaS | 293 | 3.0 yrs | $8,333 |
| Mobile | 86 | 2.5 yrs | $5,625 |
| Agency | 69 | 4.0 yrs | $22,917 |
| Ecommerce | 56 | 5.0 yrs | $12,750 |
| Shopify App | 29 | 5.0 yrs | $7,083 |
| AI | 28 | 2.0 yrs | $7,417 |
| Marketplace | 18 | 5.0 yrs | $10,208 |
| Digital | 18 | 5.0 yrs | $14,792 |
| Content | 17 | 8.0 yrs | $5,667 |
SaaS is the largest group at 293 listings, with a median age of three years and median revenue of $8,333 a month. If you want one number to plan against, that is the one. Cross-read it with SaaS ideas backed by pain points and B2B SaaS ideas.
It is also worth reading alongside price. Our analysis of 39,000+ real price points found a micro SaaS median price of $25 a month. At $25, $8,333 a month implies roughly 330 paying customers, which is a more concrete target than a revenue figure.
AI startups have a median age of two years at listing, the youngest of any type, at $7,417 a month. Some of that is simply that the category is young. Some of it is founders reaching a sellable number quickly and choosing to sell into a hot market.
It is not evidence of durability, and our AI SaaS revenue reality check is the appropriate corrective.
Mobile lists at 2.5 years with the lowest median revenue of the major types at $5,625 a month. Fast to build, fast to plateau, and squeezed by platform fees. See profitable mobile app ideas and the state of mobile app pain points.
Agencies post the highest median revenue of any type, $22,917 a month, at four years. That is nearly three times the SaaS median.
The catch is what a buyer pays for it. Services revenue carries lower multiples than software revenue, so high revenue does not translate proportionally into enterprise value. It is the clearest illustration in the data that revenue speed and business quality are different questions. See AI automation agency business ideas.
Both list at a median of five years. Ecommerce reaches $12,750 a month, Shopify apps only $7,083. Five years is a long time for $7,083, and it reflects how much of a Shopify app’s ceiling is set by the platform rather than the product.
The worst combination in the table. Median age eight years, median revenue $5,667 a month. More than twice as long as SaaS to reach two thirds of the revenue.
Content compounds, but on this evidence it compounds slowly enough that the timeline is the real cost. Anyone treating content as a fast path should look at this row twice.
Five years to $10,208 a month across 18 listings, which is a small sample and should be treated as directional. Marketplaces have the hardest cold start of any model, since both sides have to arrive. See subscription business ideas and scalable business ideas for adjacent models.
A nine-year median changes which ideas are rational, not just how patient you should be. An idea that only pays off at scale needs the scale to arrive inside a window you can afford. An idea that pays at 300 customers does not.
The founder questions in our corpus circle this constantly without naming it. From r/microsaas: “When you have a new SaaS idea, how long do you typically spend validating it before building?” and “How long does your validation process usually take, and what indicators do you consider the strongest signs of real demand?” From r/SaaS: “How do you decide if a SaaS idea is actually worth building?”
Those are timeline questions wearing validation clothes. The reason validation matters is precisely that the build-and-grow cycle is measured in years, so a wrong choice is not a wasted weekend, it is a wasted phase of your life. Our multi-signal validation approach, how to validate a startup idea, and common validation pitfalls all exist for that reason.
A r/startups commenter frames the cost of getting it wrong in the sharpest available terms: “If i asked you to find just 1 potential customer for the product online, how long would it take?” If that answer is measured in hours, the nine-year curve is going to be worse than nine years.
And an r/Entrepreneur commenter names the research shortcut people reach for instead: “Back in the day you had to hold focus groups, and go through months of customer research before releasing your product or service?” The months of research were never the expensive part. The years afterwards were.
For the demand-side evidence that shortens that first phase, see customer pain point analysis, customer complaint databases, finding SaaS ideas from real pain points, and how to find problems worth solving.
The practical translation is not “expect nine years.” It is: build the thing so that nine years is survivable, because the median path is long and the fast stories you have read are drawn from the tail.
A median first-year listing at $5,167 a month sounds encouraging until you remember the filter. These are the first-year businesses good enough to sell. The typical first year produces nothing, which is what the failure statistics show.
Do not calibrate your first year against this row.
Median MRR moves from $5,167 to $6,500 between year one and year two. That is a 26% increase across an entire year, and it is the smallest step in the whole table.
If your second year feels like nothing is happening, the data says that is the normal shape, not a signal. The r/Entrepreneur question “how long do I have to wait?” has an uncomfortable answer: longer than year two.
One r/SideProject post is titled “How I built a successful side project and quit my job in 2 months.” That is a real outcome and it is also a 75th-percentile-of-the-tail outcome. Both things are true.
The corresponding r/SideProject caution, from a commenter: “When you quit and work on your biz, there will be nobody to help you out, and you will need to wear A LOT of hats at once.”
Nine years is only a problem if you cannot afford nine years. The businesses in the stalled quartile did not fail, they just stayed small, and staying small is survivable when costs are near zero and fatal when they are not.
This is the strongest practical argument for the low-overhead approach in micro SaaS ideas, one-person business ideas, and bootstrapping a company in 2026.
One r/SideProject thread puts the test plainly: “Do I have 6–12 months of expenses?” Another asks the harder version: “why did you quit your job if you were vibe coding?”
Against a median that takes four to five years to cross $10,000 a month, a six-to-twelve-month runway is not a plan for the median. It is a bet on the tail.
The retrospective questions in our corpus are as consistent as the timeline ones. From r/Entrepreneur: “What’s one thing you wish you knew before starting your business?” From r/SaaS: “What’s the biggest mistake first-time SaaS founders make?” From r/indiehackers: “what do you wish you had done or tracked in your first weeks?” And from r/microsaas: “What would you do differently if you were starting out on this project today?”
From r/EntrepreneurRideAlong, a founder answering their own: “What I’d do differently with Chessable knowing what I know now. Honestly?”
Two more, both from r/indiehackers, that point at measurement rather than effort: “What was the biggest mistake you made so far?” and “Which one are you seeing more traction from right now?” The second is the better question, because it assumes you are comparing two things rather than waiting on one.
And from r/EntrepreneurRideAlong, on whether demand pulled or the founder pushed: “Did those companies approach you after seeing user traction, or did you deliberately start selling into them?”
The single most specific hindsight answer in the corpus, from r/SaaS: “what i’d do differently: start the content 6 months earlier.”
It is a common one, and it fits the age curve. If distribution compounds and the revenue curve is nine years long, then six months of head start on the compounding input is worth considerably more than six months of head start on the product.
A related r/indiehackers caution about launch posts: “Don’t list a feature as ‘coming soon’ or ‘pending approval’ in your launch post.” See where to launch your startup.
Two r/microsaas questions frame it: “What helped you get your first real traction?” and “What worked for you after getting the first bit of traction?” From r/SaaS, the version with no audience behind it: “How did you get them when you had almost no traction yet?”
Against this dataset, traction is better defined as movement between quartiles than as a revenue number. A business going from $2,400 to $5,700 a month over eight years has revenue and does not have traction.
See getting your first 100 SaaS users and how to get customers for a startup.
An r/SaaS commenter asks exactly the right question: “What signals made you continue or quit?” An r/microsaas founder gives the honest non-answer: “Why I kept going: Of all the products I’ve built, this one just felt right.”
This data deliberately does not answer it. What it does establish is that elapsed time alone is a poor signal, because the median path is slow enough that two flat years are unremarkable. Judge the inputs.
A useful reframe from r/Entrepreneur, asked by someone in exactly this position: “If you were in my position (working product, low traction, limited time), what would you do next?”
On the age curve, a working product with low traction at year two is sitting on the median, not below it.
The agency row proves it. Fastest to high revenue, and the least valuable per dollar of that revenue. An r/startups question probes the same tension from the buyer side: “Is it possible to perpetually build and sell startups as MVP after few months of operations?”
The multiple data says the arbitrage is thinner than it looks, because young businesses do not sell at a discount, they sell at the same multiple on a smaller number. See buying vs building a SaaS and how to find acquisition targets.
Worth noting because it adds to the timeline. The marketplace publishes seller testimonials describing the sale process, which should be read as vendor-selected marketing rather than a sample: “I sold my startup in about six months through Acquire.com.” “I was able to find a buyer in less than 3 weeks.” “Within a few days, I receieved tens of offers from people. And within the next two weeks, I had sold my first startup.” “Love the smooth and fast process that took around 1 month.”
Treat the range, weeks to months, as the usable signal and ignore the enthusiasm.
These are complementary, not competing. Growth rate tells you whether you are moving quickly. Age-to-revenue tells you where you should expect to be by now. A 20% monthly growth rate on $200 a month is a different situation from 5% on $9,000, and only the second is on the curve above.
Read how fast SaaS startups actually grow for the rate view and MRR vs ARR vs TTM for the definitions, since this analysis uses TTM.
Any single dataset can mislead, so it is worth triangulating. Our TrustMRR revenue benchmarks cover 8,699 tracked startups and show the same long-tail shape from a different source. The state of indie SaaS revenue and benchmarks by category add the category dimension.
Outside our data, MicroConf’s State of Independent SaaS is the long-running survey of bootstrapped founders, and the Y Combinator library is the standard reference for the funded path, which follows a very different clock. On the supply side, our Stripe Index covers 29,000+ companies listed onStripe’s public directory, which is a different slice again: businesses currently taking payments rather than businesses currently for sale.
If a dataset existed covering businesses that never reached sellable revenue, with founding dates attached, the curve would flatten dramatically and the medians would collapse. That dataset does not exist at scale, which is the honest limit of every analysis of this shape, including this one. The nearest available proxies are the state of small SaaS valuations, SaaS market trends, and the state of SaaS pain points.
Plan for a five-year path to $10,000 a month, not a one-year one. Keep fixed costs low enough that a slow year is boring rather than fatal. Start the distribution work earlier than feels necessary, because it is the input with the longest lag. And measure progress by quartile movement rather than by the calendar.
If you are still choosing what to build, the demand-side evidence is in industries still running on spreadsheets, business ideas that solve real problems, and low competition SaaS ideas.
636 of 656 listings carry both a parsed founding year and a revenue figure, so coverage within the dataset is 97%. The dataset itself is a snapshot of one marketplace at one moment, and marketplaces select for particular kinds of business. Smaller type groups, marketplaces at 18 and content at 17, are directional rather than conclusive.
Survivorship is total. Stated three times in this article because it is the thing most likely to be quoted out of context.
Founding dates are self-reported. Sellers have a mild incentive to appear established, which would bias ages slightly upward.
Asks are not closes. Every price and multiple here is what the seller wants.
Age is not tenure of effort. A business founded in 2018 may have been dormant for three of those years.
TTM is not MRR. We divide trailing twelve month revenue by twelve, which smooths seasonality and understates a business growing quickly right now.
One marketplace. Different marketplaces attract different sellers. See Product Hunt alternatives and startup directories for how platform selection shapes any dataset drawn from a platform.
Two columns in this dataset look quantitative and are not, and we are naming them because they would have produced confident, wrong numbers.
Paying customers is not a customer count. Every one of the 500 populated rows is at or below 10, with a maximum of exactly 10, which looks like bucketing. It is worse than that. Checking the underlying record shows the column holds the leading integer of a churn band string: “10%+ Stable” parses to 10, “5-10% Stable” to 5, “0-1% Stable” to 0. Any article reporting a “median customer count” from this column is reporting a mangled churn figure.
The monthly churn column is entirely empty, zero of 656 rows, but the churn data itself is not missing. It survives intact in the raw record, and once parsed correctly it is the most interesting field in the dataset: 35.2% of listings report 10%+ monthly churn, against 14.2% in the 0-1% band. Roughly a third of businesses healthy enough to sell are losing a tenth of their customers every month.
The general lesson, which applies to any scraped dataset: run minimum, maximum and percentiles before you cite a column, and when a column looks broken, open the raw record before concluding the data does not exist. We nearly published the claim that churn was underivable here. It was one join away.
Filter the listings to your category, parse the founding year, and plot revenue against age. The method is simple; the value is in having the listings in one place with the financials attached.
Start at discover, or read getting started with the sell-side database, using sell-side data as market validation, and reading the AI buyer thesis. The revenue intelligence guide covers the TrustMRR side, and the MCP server exposes all of it inside an AI assistant.
Acquisition listings with disclosed financials, revenue benchmarks across 8,699 tracked startups, and 1M+ documented complaints, in one place.
Start searching →Across 630+ businesses listed for sale, median monthly revenue rises from about $5,167 in year one to about $20,250 at nine years or older, roughly fourfold across nearly a decade. The share above $10,000 a month climbs from 36% to 69%.
This data cannot answer that, because every business in it already had revenue. It shows the pace after that point, not the time to reach it.
No. Median profit multiple stays between 2.90x and 3.90x across every age band with no trend. Age buys more profit, not a better multiple.
They get worse, from about 61% in the first year to about 39% at nine years and older. Larger in absolute revenue, thinner as a percentage.
Three years, at about $8,333 a month. Mobile lists at 2.5 years, AI at two, agencies at four, content at eight.
Median two years, the youngest of any type. Partly because the category is young, partly because founders reach a sellable number fast and sell into a hot market. It is not evidence of durability.
Content. Median eight years old at listing for about $5,667 a month, the worst combination measured.
The separation does not happen there. The 25th percentile crawls from about $2,417 to $5,750 across nine years while the 75th runs from $16,667 to $41,667. Time widens the gap.
The data cannot tell you, and distrust anyone claiming otherwise. It does show the median path is slow enough that a flat second year is normal rather than a verdict. Judge the inputs.
Completely. Every business had enough revenue to be worth listing. See startup failure statistics for the denominator.
About $119,500 under two years, $157,500 at three, $300,000 at six to eight, $433,700 at nine and older. Asks, not closes.
Growth rate measures percentage change. This measures absolute revenue at a given age, which answers where you should expect to be rather than how fast you are moving.
No. Agencies reach the highest median revenue of any type by year four but command lower multiples, because services revenue is valued differently from software revenue.
36.2% at zero to one year, 42.4% at three years, 62.4% at six to eight, 68.8% at nine and older.
Moderately. Free text parsed for a four-digit year, succeeding on 638 of 656 listings. Sellers have a mild incentive to look established, so ages may skew slightly old.
Plan for slower than the anecdotes and structure the business so slowness is survivable, which mostly means keeping fixed costs low enough that time is not the binding constraint.
BigIdeasDB indexes acquisition listings alongside revenue benchmarks, the Stripe Index and the complaint corpus. Start at discover. Further reading: the state of SaaS acquisitions, the SaaS valuation guide, finding acquisition targets, the due diligence checklist, what micro SaaS actually charges, micro SaaS examples, the best micro SaaS ideas, the micro SaaS competition map, SaaS market saturation, MRR tracking tools, how to validate a startup idea, how to find startup ideas, and getting started with TrustMRR.
BigIdeasDB Research. (2026). How Long Does It Take to Grow a SaaS? (630+ Businesses). BigIdeasDB. Retrieved from https://bigideasdb.com/how-long-does-it-take-to-grow-a-saas