How to Estimate a SaaS Competitor's Revenue (We Tested the Methods)

To estimate a SaaS competitor’s revenue, build a range from at least three independent proxies, then keep the overlap. Never trust one method. We tested the most popular one, paying customers times price, against 3,600+ startups whose revenue we can verify. Even with the exact customer count, it landed within 2x of real MRR only 49.9% of the time.

That result is the reason this page exists. Every guide on this topic lists the same tricks: headcount, traffic, pricing pages, ad libraries. None of them measures the error. We can, because we hold revenue-verified data on 8,600+ startups, pricing data on 30,000+ companies from Stripe’s public directory, 800+ acquisition listings with trailing revenue, and a complaint corpus of 1M+ records.

This is a method page. If you still need to find who your competitors are, start with competitive landscape analysis or the best competitor research tools for founders, then come back here to size them.

How do you estimate a competitor’s revenue? The short answer

The rest of this page shows each method, how wrong it tends to be, and a worked example you can copy. Figures come from read-only queries run on September 25, 2026.

What are you actually estimating?

Pin the metric down first, because founders mix them constantly. MRR (monthly recurring revenue) is the monthly value of active subscriptions. ARR is MRR times 12. Revenue includes one-time sales, setup fees and credit packs on top. TTM revenue is the last twelve months of actual collected money, which is what acquirers price. Our MRR against ARR against TTM guide covers the differences in depth.

For a SaaS competitor, you almost always want MRR. It is the number that tells you how many customers pay and whether the category supports a business. Use the free MRR calculator once you have inputs.

Why do you want the number?

Founders ask for three different reasons, and each needs a different precision. Validation (“does anyone pay here?”) needs only an order of magnitude. Positioning (“can they outspend me?”) needs a rough band. Fundraising needs a defensible method more than a precise answer.

“I had little to no answers for investor's questions on market & competitors analysis” – r/SaaS

“I'd love to figure out exactly how well they're doing, so that I can both estimate what I might be able to make and potential growth, as well as what resources they can marshal against me.” – r/SaaS

The second founder already had the right instinct. They wrote that their rivals sat “somewhere between $5000 MRR and $20,000 MRR, which is a very wide range.” That range is not a failure. As the data below shows, it is about as tight as honest public signals allow. If validation is your goal, pair the estimate with demand evidence from how to validate a startup idea.

What every other guide skips

The pages ranking for this topic share one template. A revenue-per-employee rule, a traffic tool, a pricing-page multiplication, and lately an ad-library trick. They present each as if it produces a number. None publishes how often the number is wrong.

“Exact values are not needed but how can i even make a general estimate?” – r/SaaS

We fill that gap with measurement. Wherever we have ground truth, we show the error. Wherever we do not, we say so. That is the difference between an estimate and a guess with a spreadsheet around it.

How we tested the methods

TrustMRR holds startups whose revenue is read from their payment provider, not typed in. 3,600+ of them report both MRR and a count of active subscriptions. That lets us run the most common estimation method in reverse: take the true customer count, multiply by an assumed price, and compare with the true MRR.

We tested three price assumptions: the startup’s own category median revenue per subscription, the median across all startups, and a flat $29 a month, a common default in founder napkin math. We counted how often each landed within 2x and within 25% of reality. We then pulled price points from the Stripe Index, headcount and revenue from acquisition listings, and review counts from Capterra to test the other proxies. Full detail sits in the methodology.

The headline numbers

Is customers times price accurate? It is a coin flip

Multiplying a real paying-customer count by the category’s median revenue per customer lands within 2x of true MRR for 49.9% of 3,600+ revenue-verified startups. In the other half, the estimate is off by more than 2x, split almost evenly: 24.0% too high and 26.1% too low. Within 25% of reality, the band most founders imagine they are in, it hits 19.3% of the time.

Remember what this test gave the method: a perfect customer count. For a real competitor you will be guessing that count too, so your error compounds. Using the all-startup median price instead of the category median drops accuracy to 42.8% within 2x. Category matters, and even category is not enough.

“Customer Count x Pricing: Estimate revenue by multiplying the number of customers by average pricing.” – r/SaaS

That advice is fine. It is just half the job. The method is the best single proxy we tested, which is exactly why it needs two more beside it.

The list price trap

A flat $29 per customer overstated MRR by more than 2x for 52.6% of revenue-verified startups, and landed within 2x for only 34.5%. The pricing page lies by omission. It shows what a new customer would pay today on a monthly plan. It hides annual discounts, grandfathered prices, coupons, free seats and the fact that most customers sit on the cheapest tier.

“Try to be optimistic and assume 4% - 6% of that as paid users.” – r/SaaS

“Consider 55% of paid users to be on lowest plan” – r/SaaS

That second heuristic points the right way. Our data says push it further: the realized average sits near the entry plan. For more on how founders set these numbers, read what micro SaaS actually charges and SaaS pricing strategies for 2026.

Price competitors at the entry plan, not the headline plan

The median revenue per active subscription across 3,600+ revenue-verified startups is $13.40 a month. The middle half runs from $6.00 to $30.50. In the Stripe Index, the median B2B SaaS lists an entry paid price of $12 and a top plan of $95. The median B2C SaaS lists $10 and $40, and the median micro SaaS $10 and $28.

Put side by side, real revenue per customer lands right next to the cheapest paid tier, nowhere near the plan marked “most popular.” The two datasets cover different companies, so treat this as a strong direction rather than a matched comparison. The practical rule holds: start at the entry price and only move up with evidence, like a visible enterprise sales team.

“In 77 days, we have converted 52 accounts (4% of signups) into paid @ avg $120/m.” – r/Entrepreneur

That founder sold one paid plan at $99 and averaged $120, because some accounts paid for more. Single-plan products with usage add-ons are the exception where realized price beats list. If a competitor sells seats, check how many seats a typical customer buys in its case studies.

Revenue per customer by category

Use your category’s band, not a global number. Marketing tools collect a median $30.90 per subscription with a 90th percentile of $355.40. Health and fitness apps collect a median $5.00. The gap between those two is six times, which is why a single “average SaaS price” is useless.

You can pull the same band for any category, live, in Revenue Intelligence. The revenue intelligence guide shows the filters, and TrustMRR clusters give a finer grain than category.

Revenue is brutally skewed

Among 3,600+ revenue-verified startups, the top 10% hold 89.5% of all MRR and the top half hold 99.5%. The 25th percentile startup makes $29 a month. The median makes $146. The 75th percentile makes $907, and the 90th makes $5,210. 1,600+ startups sit under $100 MRR, 870+ clear $1,000 and 220+ clear $10,000.

This is why estimating a single competitor is hard. Two products with the same number of customers can sit a decade apart on this curve. Our state of indie SaaS revenue report and the solo developer revenue examples show the same shape from other angles.

Why a category average will mislead you

Mean MRR across revenue-verified startups is $4,374. The median is $146, 30 times lower. A category average is pulled up by a handful of winners, so it describes a top-decile company, not the rival you are looking at. We made this point for categories in SaaS revenue benchmarks by category. For estimation the consequence is concrete: never anchor on an average. Anchor on the 25th to 75th percentile band, then decide where in that band the competitor sits.

Estimate a range, never a point

Among 240+ revenue-verified startups with 100 to 250 paying subscriptions, MRR ran from $53 to $61,139. The middle half sat between $929 and $4,821, a 5x spread. The 10th to 90th percentile ran from $517 to $10,297, a 20x spread. Same customer count band, wildly different businesses.

So write every estimate as low, likely and high. If someone hands you a single number for a private competitor, ask for their range. If they cannot give one, they guessed.

“No easy way.” – r/SaaS

The proxy ladder, from strongest to weakest

Not all signals are equal. Rank them by how close they sit to actual money, then work down the ladder until you have three that agree.

Method 1: find a verified disclosure first

Before you estimate anything, look for the real number. Public companies report revenue in annual filings on SEC EDGAR. Some founders publish live revenue through their payment provider. Acquisition listings often carry trailing revenue that the marketplace checked. Revenue-verified startups sit in TrustMRR, searchable by category.

“Acquire hack is pretty neat with verified statistics.” – r/SaaS

Two checks before you use a disclosure. What is the metric (MRR, ARR, gross revenue, bookings)? And what is the date? A number from last year’s launch thread tells you little about today.

Why screenshots are not evidence

A revenue screenshot is an image, not a disclosure. Generators for fake MRR dashboards are public, and founders have noticed.

“Almost every day a post pops up with a chart from RevenueCat or Stripe going straight up to $5k–$10k MRR.” – r/appledevelopers

“Or are most of these just fake screenshots made with inspect element?” – r/appledevelopers

“I think most are just fake screenshots and people trying to sell you their “full proof” marketing strategy” – r/appledevelopers

“A screenshot is a photo. You're trusting a stranger's Photoshop skills.” – r/SideProject

Even honest screenshots distort. One founder who reviewed 40 of them listed the recurring tricks:

“Revenue and MRR get mixed. Annual plans counted as monthly inflate the figure 12x.” – r/indie_startups

“Rounding runs one direction. $6,200 becomes "nearly $10k."” – r/indie_startups

“No date. A 2023 peak gets reposted in 2026 as current.” – r/indie_startups

Communities are responding. The r/indiehackers moderators now say posts discussing MRR get auto-reported and that “if we do not see any form of confirmation for the claim, the post will be removed.” Buyers learned the same lesson the expensive way:

“Lesson: Revenue screenshots are easy to fake or manipulate.” – r/saasforsale

“Everyone posts fake screenshots, fake founder stories, fake success metrics.” – r/micro_saas

Method 2: paying customers x realized price

This is the workhorse. Estimate paying customers as a low and high count, multiply each by a realistic revenue per customer, and you have a recurring-revenue band. Use the entry plan and your category’s P25 to P75 revenue per subscription from the table above.

“If you know their average revenue per user you can work out the MRR.” – r/SaaS

True, and our test shows the catch: knowing the average is the hard part. Correlation between subscription count and MRR is strong in our data (0.83 on a log scale), so customer count is the right input. It just cannot tell you where on the price curve a company sits.

Where competitor customer counts leak

Companies publish customer counts more often than revenue. Look in these places:

• “Trusted by” claims. “Used by 2,000+ teams” usually counts signups, including free. Treat it as a ceiling on paying customers.
• Case studies and logos. Count them over time on archived versions of the page. Growth in logos tracks growth in paying accounts.
• Marketplace installs. App marketplaces (Shopify, Chrome, WordPress) show install or review counts. Paid conversion is a fraction of installs.
• Review counts. Useful for rank order. See Method 7 before you convert them.
• Founder interviews and podcasts. Founders often state customer counts when they would never state revenue.

For mining review sites properly, the guides on G2 analysis and Capterra analysis show what the data can and cannot tell you.

Method 3: headcount x revenue per employee

The classic shortcut comes from SaaStr. Jason Lemkin’s rule of thumb takes LinkedIn headcount and multiplies by roughly $150,000 to $200,000 per employee, as low as $100,000 for heavily funded companies and as high as $300,000 for self-funded or freemium ones.

“Check the number of employees that the SaaS company has. Multiply that with $150,000 if well funded and $200,000 if moderately funded” – r/SaaS

For a venture-backed company with 50 or more people, it is a reasonable cross-check. The rule was built for that world. The problem starts below it. Our revenue per employee by industry study covers the customer side of this ratio.

Why headcount breaks for small SaaS

Solo-run SaaS and AI listings on acquisition marketplaces show a median trailing revenue of $40,000 a year, with the middle half between $26,000 and $89,750 (130 listings). Listings with a 2 to 20 person team show a median $175,500, middle half $62,500 to $484,750 (270+ listings). Apply $150,000 per head to a solo founder and you overshoot the median by almost 4x.

Worse, LinkedIn headcount for small companies includes contractors, advisers and people who left. Data vendors inherit the same errors, and reviewers say so:

“I found some of the data to be inaccurate - regarding the company size and revenue.” – Capterra review of InsideView

“I have found that the employee count, Annual revenue, LinkedIn profiles, and other crucial titles are inaccurate” – Capterra review of DiscoverOrg

“When I look up a company to get information on them before making a call, the revenues are always way off” – G2 review of D&B Connect

“Some problems with inaccurate revenue data” – G2 review of TAMI

“For EMEA the data is often inaccurate or missing” – Capterra review of ZoomInfo Sales

“I also find that the data seems around 65% accurate.” – Capterra review of Apollo.io

A sales-ops thread put the mechanism plainly:

“Headcount in, revenue guess out.” – r/gtmengineering

“It becomes a problem when a whole revenue org treats "the model said 50 employees" as gospel and then acts personally betrayed when the account has 6 people and a Shopify store” – r/gtmengineering

Use headcount as a ceiling check for funded teams and ignore it for anything under about ten people. Solo and tiny SaaS businesses are covered better by acquisition data; see using SellSide as market validation.

Method 4: read the pricing page, carefully

Only 41.1% of 30,000+ companies in the Stripe Index publish a price we can scrape, and 32.5% have a dedicated pricing page. For most competitors, the pricing-page method is not even available. When it is, use it for three things: the entry price (your anchor), the plan structure (seats, usage, flat), and the gating (free trial or freemium).

15.4% of Stripe Index companies offer a free trial and 5.1% a free tier. A freemium competitor with a big user count may have a small paying base, so apply your conversion assumption to users, not to signups. A “contact sales” plan signals larger contracts, which pushes the realized price up. For a structured read of any site, run the free site teardown.

Method 5: treat ad activity as a floor

The Meta Ad Library and the Google Ads Transparency Center show which ads a company runs and how long they have run. They do not show spend for ordinary commercial ads; Meta shows spend for ads about social issues, elections or politics.

So the signal is duration, not dollars. A competitor running the same ads for many months is almost certainly getting paid back, because nobody funds a losing campaign forever. That tells you the business clears some floor. It does not tell you the size. Converting guessed ad spend into revenue with a guessed return on ad spend stacks two unknowns.

Method 6: traffic x conversion, the weakest link

Traffic tools model visits from panels and rankings. They are least accurate on small sites, which is where most SaaS competitors live. Reviewers of the tools say it directly:

“The data is not 100% accurate compared to actual traffic, especially for smaller or low-traffic websites.” – Capterra review of Similarweb

“Sometimes the data is not 100% accurate but the trends are accurate.” – Capterra review of Similarweb

“It hadn't caught properly our site traffic lately. we have had increase in traffic over the past 4-5 months, and Similarweb measures a drop.” – Capterra review of Similarweb

Site owners who compared tool readings with their own analytics found big misses. One reported a site with 60,000 organic visitors a month read as 13,100, 18,100 and 32,500 by three tools. Another saw one domain estimated at 700 clicks by one tool and 500,000 by another.

“There's no third party tool that can give you reliable traffic estimates.” – r/SEO

“Sometimes they get quite close to the real thing, most of the time, they're completely off the mark.” – r/SEO

“you'll never get accurate. You'll get best guess.” – r/SEO

“Shows 4x more visits, 3x higher bounce rate, and 12x lower visit duration.” – r/SEO

Then you multiply that visit estimate by a guessed conversion rate and a guessed price. If each input is off by 2x, the product can be off by 8x. Use traffic for direction (growing or shrinking) and ignore its absolute level. The best advice from that thread was not a tool:

“Best option is to triangulate: Similarweb for rough total traffic, Ahrefs/Semrush for organic direction, then mark each domain with confidence (high/medium/low) in your sheet.” – r/SEO

Method 7: review volume as a size proxy

Review counts are as skewed as revenue. Across 43,000+ Capterra-listed companies with reviews, the median has 6 reviews, the 90th percentile has 72, and the top 10% hold 82.8% of all reviews. That makes review volume good at one job: telling a category leader from the long tail.

It is bad at converting to dollars. Review counts track how hard a vendor asks for reviews, how old the product is, and whether its buyers are the reviewing type. A 20-person team with a review-incentive campaign can out-review a quiet company ten times its size. We tried to link review counts to verified revenue directly and could not build a clean enough match between the datasets to publish a ratio (see coverage honesty). Use reviews for rank order and for what they are best at, pain. The G2 review analysis guide covers that side.

Method 8: funding presence, not funding size

Funding tells you a competitor can outspend you, hire faster and survive longer at a loss. It does not tell you revenue. Our Funded DB tracks 17,000+ funded companies with AI scoring for category and momentum. We deliberately do not publish round sizes from it, and funding amounts would not be a revenue measure anyway.

Use funding to adjust other methods. A heavily funded team runs lower revenue per head, so use the low end of the headcount rule. And remember that an existing competitor is validation, not a verdict; SaaS market saturation in 2026 explains how to read a crowded field. The Funded DB MCP tools let you pull this from your AI assistant.

Method 9: public filings, where they exist

Public companies state revenue in their annual reports, searchable on SEC EDGAR. In the UK, every company files accounts at Companies House, though small companies can file accounts that leave out the profit and loss account, so turnover is often missing. Balance-sheet movement (cash, deferred revenue) can still hint at scale.

Filings rarely cover the small SaaS you are actually competing with. They matter most when a large incumbent is in your category, because they set the ceiling for the whole market. Pair them with how to calculate market size or the market size calculator.

Method 10: acquisition listings and multiples

Acquisition marketplaces publish trailing revenue, profit, team size and asking price for thousands of small software businesses. Our SellSide data holds 800+ listings. They will not contain your exact competitor, but they give you comparables: what a solo-run tool in your space tends to earn, and what it sells for.

SaaS and AI listings carry a median revenue multiple of 2.7x for solo teams and 2.3x for 2 to 20 person teams. On TrustMRR, 1,100+ startups listed for sale carry a median multiple of 3.3x. If a competitor is rumoured to have sold, those multiples turn a price back into a revenue band. The SaaS sellers report, the SaaS valuation guide and finding SaaS acquisition opportunities go further. Listings are chosen by sellers, so they skew toward businesses worth selling.

Method 11: ask, honestly

The most underused method is a conversation. Sales reps will explain pricing structure, typical contract size and discounting. Former customers will tell you what they paid. A pricing consultant on YouTube put the first step bluntly:

“if you have a SAS business and you want to know your competitors pricing here's what you do you call them” – YouTube, SaaS pricing consultant

The same video suggests posing as a consultant for an anonymous client. We would not. Be honest about who you are, or talk to people who have bought the product. Ex-customers, partners and agencies that implement the tool are fair sources, and they know realized prices better than any pricing page. Customer interviews double as validation; see validating a SaaS idea with real reviews.

How to triangulate into one range

Triangulation is not averaging. Averages hide disagreement. Instead:

1. Write each method as low and high. Customers x price, headcount x revenue per head, category P25 to P75.
2. Discard methods that do not apply. Headcount for a solo founder, traffic for a site under a few thousand visits.
3. Keep the overlap. The zone where at least two methods agree is your likely band.
4. Flag the spread. If methods disagree by 10x, say so. It usually means your customer count is wrong.
5. Date it. An estimate is a snapshot. Note the month and the inputs.

This is the same evidence-first approach we use for demand in how founders research markets and the SaaS market research guide.

Worked example: a marketing-automation competitor

Take a hypothetical marketing-automation tool, not a real company. Its site says “used by 600+ teams” and shows 20 logos. It has a free trial, no free plan, and an entry plan of $19 a month. LinkedIn shows 4 people.

• Paying customers. “600+ teams” likely counts trial signups. Assume 100 to 250 paying accounts.
• Revenue per customer. Marketing tools collect $15.00 (P25) to $100.50 (P75) per subscription, median $30.90. The entry plan at $19 sits inside that band. Stripe Index marketing-automation companies list a median entry of $18 and a top plan of $199.
• Customers x price. 150 accounts x $15.00 = $2,250. x $30.90 = $4,635. x $100.50 = $15,075. Likely band: roughly $2,000 to $15,000 MRR.
• Headcount. Four people fails the headcount rule; $150,000 x 4 would imply $50,000 MRR. Discard it for a team this small.
• Category distribution. Median Marketing startup MRR is $276, P90 $10,495. A company with 150 paying accounts is already well above the median.

Result: a likely band of $2,000 to $15,000 MRR, with $4,000 to $5,000 as the centre. Stated as a range, dated, with the headcount method flagged as not applicable.

Checking the example against real startups

We can check that band. Among revenue-verified Marketing startups with 100 to 250 paying subscriptions, MRR runs $2,678 at the 25th percentile, $4,974 at the median and $15,927 at the 75th. That is a small group, 8 startups, so read it as a sanity check, not a benchmark. It lines up with the $2,250 to $15,075 band almost exactly.

Across all categories, startups in the same 100 to 250 subscription range sit between $929 and $4,821 (middle half). Marketing sits higher because it charges more per customer. That is the whole lesson: the category price band moves the answer more than anything else.

Sanity-check with market density

Revenue estimates live inside a market. The Stripe Index counts 250+ marketing-automation companies (30+ flagged micro SaaS), 180+ social media management companies, 160+ SEO and web analytics companies and 50 email marketing companies. Our Funded DB has 270+ funded companies whose one-liner mentions marketing.

A crowded category with a fat revenue band says buyers pay, and you need a wedge. A thin category where your estimate for the leader is under $1,000 MRR says the market may be small. We track this density logic in the state of micro SaaS competition and low-competition SaaS ideas. The Stripe Index MCP tools give you the counts per category.

MRR is not the whole revenue

960+ revenue-verified startups show 30-day revenue with zero MRR. They sell lifetime deals, credit packs or one-off purchases. Among startups with both, the median ratio of 30-day revenue to MRR is 1.01, but 35.7% earned 30-day revenue at least 20% above their MRR.

So estimate two layers: the recurring core (customers x price) and a one-time layer if the competitor sells lifetime deals or top-ups. AI tools are the most common case; our AI SaaS pricing models study shows how credits change the mix.

Direction beats size

40.8% of 3,600+ revenue-verified startups showed negative 30-day growth in our snapshot, and the median was flat at 0.0%. A competitor at $8,000 MRR and shrinking is a different threat from one at $3,000 and doubling.

Direction is also easier to read than size. Traffic trends, new logos, hiring posts, pricing changes and review velocity all move before revenue does. Track them monthly. The best MRR tracking tools cover your own numbers; for competitors, a simple dated sheet beats any dashboard. If a funded rival arrives, the playbook in SaaS moats in the AI era applies.

What to do with the range

For the complaint side of that decision, use the complaint analysis platform or browse pain points by category.

Common mistakes

• Multiplying signups by list price. Signups include free and churned users. List price ignores the entry tier.
• Anchoring on an average. The mean MRR is 30x the median in our data.
• Using the headcount rule on tiny teams. It overshoots solo businesses by roughly 4x.
• Trusting a screenshot. Undated, uncropped and unverified is three problems.
• Treating traffic as truth. Tools disagree with each other by large multiples on small sites.
• Reporting one number. A point estimate implies precision nobody has.

“AI with no actual proof.” – r/SaaS

“How were you able to estimate the MRR?” – r/SaaS

“How reliable do you think those revenue numbers from Sensortower and similar are?” – r/SaaS

Those three replies landed under a viral post claiming eight copycat apps each made $100,000+ a month. The post admitted its combined figure was a “rough estimation.” The comments asked the right question. Your estimate should be able to answer it.

What you must never do with an estimate

An estimate is a research input, not a fact about a company. Three rules:

• Never publish an estimate about a named private company as fact. Say “we estimate a range of”, show the method, or keep it internal.
• Never misrepresent yourself to extract confidential numbers from a competitor’s staff.
• Never use non-public data you should not have, such as leaked dashboards or former-employee confidential information.

This is not legal advice. It is how you keep an estimate useful and your reputation intact. We follow the same rule on this page: every revenue figure here is an aggregate, and the worked example is hypothetical.

Methodology

All figures come from read-only SQL on BigIdeasDB’s warehouse, run on September 25, 2026. TrustMRR revenue reflects the latest sync, dated July 2026.

• Estimation test. Startups with MRR above zero and active subscriptions above zero (3,600+). Revenue per subscription = MRR / active subscriptions. Estimated MRR = active subscriptions x the startup’s category median revenue per subscription (all-startup median where category is missing). We counted estimates within 0.5x to 2x and within 0.75x to 1.25x of reported MRR. Repeated with the all-startup median and a flat $29.
• Skew. Startups ranked by MRR into deciles; share of total MRR per decile.
• Price points. Stripe Index price points parsed to numbers; entry price = lowest value between $5 and $2,000, top plan = highest value up to $5,000, per company, grouped by AI-classified business model.
• Headcount. SellSide listings in SaaS and AI categories with trailing revenue, grouped by the listing’s team-size band.
• Reviews. Capterra-listed companies with at least one review, total review counts, decile shares.
• Quotes. Reddit via live search, Capterra and G2 review text from our corpus, one YouTube transcript. Quoted verbatim, attributed to platform only.

Data sources and limits

Coverage honesty

Our ground truth is indie-heavy. The median TrustMRR startup earns $146 MRR, so the error rates describe small SaaS best. For a 200-person company, the headcount rule and public filings carry more weight than they do here.

We compared TrustMRR revenue per subscription with Stripe Index list prices. These are different companies, so the “entry plan” conclusion is directional. We tried to link review counts to verified revenue by exact company name and found only about a hundred matches, many likely false, so we publish no review-to-revenue ratio. Domain matching produced no usable overlap. Traffic accuracy figures are reports from site owners, not our own measurement. Google Trends did not respond during research, so we make no claim about search demand direction.

Where BigIdeasDB fits

Estimating a competitor needs a price band, a revenue distribution and a sense of market density. Those normally live in three tools and a pile of guesses. Here is the order we would use:

To go further: get started with TrustMRR, read the revenue intelligence tool overview, follow how to research competitors with BigIdeasDB and competitor analysis for SaaS, or explore acquisition comps with SellSide. You can query all of it from your own assistant with the BigIdeasDB MCP server (see the setup guide, the revenue intelligence tools and the SellSide tools). To find a competitor worth sizing, try the free competitor finder, then Discover for the problem behind it. Related reading: market research tools for startups, best SaaS research tools, micro SaaS examples with revenue and the SaaS valuation calculator.

Frequently asked questions

BigIdeasDB Research. (2026). How to Estimate a SaaS Competitor's Revenue (We Tested the Methods). BigIdeasDB. Retrieved from https://bigideasdb.com/how-to-estimate-saas-competitor-revenue