TrustMRR overview

What TrustMRR is for

Demand evidence tells you a problem is real. Revenue evidence tells you people pay to solve it. TrustMRR closes that loop with verified numbers, so you validate a niche against what comparable products actually earn rather than what you hope they might. Read the broader philosophy on Revenue Intelligence.

Every metric traces back to a real startup row. TrustMRR does not fabricate averages. The benchmarks, clusters, and category summaries are all computed from the underlying verified companies, so when a number moves, it moves because the companies behind it moved, not because a model guessed.

That distinction matters when you are deciding where to spend months of your life. A trend report can tell you a space is hot; only revenue data tells you whether the hot space is full of businesses that actually collect money. TrustMRR is built to answer the second question, which is the one that pays your bills.

It is also deliberately conservative. Where the Discover databases quantify pain across 1M+ complaints, TrustMRR quantifies outcomes: how much comparable products earn, how fast they grow, and what they sell for. You use the two together. Complaints point you at a problem worth solving, and TrustMRR tells you whether solving it has ever turned into a real business.

The dataset and its scale

TrustMRR tracks 6,040+ verified SaaS startups across 31 categories, carrying 50+ data points per company and more than $30M in aggregate monthly revenue. Average MRR across the set is $4,682, and 1,636 of those startups are currently listed for sale. The word verified is the important one: these are real companies with real revenue attached, not projections.

• 6,040+ verified SaaS startups, each with 50+ data points.
• 31 categories spanning developer tools, education, mobile micro-SaaS, AI, and more.
• Average MRR $4,682, with $30M+ in aggregate monthly revenue tracked.
• 1,636 startups currently listed for sale in deal flow.
• 26 AI-generated clusters organized across four analytical lenses.

The six ways to use it

TrustMRR is one dataset seen through six surfaces. Each answers a different question, and you rarely need all six for a single decision. Knowing what each one is best at keeps you from over-researching.

• Startup research: query 6,040+ startups by MRR, growth, profit multiple, and 50+ data points per company. See Searching startups.
• Revenue benchmarks: average, median, p25, and p75 MRR by category, plus growth, churn, and valuation comparisons. See Revenue benchmarks.
• Clusters: 26 AI-generated clusters across four analytical lenses so you study a pattern instead of a single company. See Understanding clusters.
• Category analysis: a market summary, opportunity thesis, top performers, rising stars, and best-deal picks for each of the 31 categories. See Category analysis.
• Deal flow: the 1,636 startups currently listed for sale, sortable by lowest profit multiple with buy-box filters. See Deal flow.
• AI Research Chat: ask questions in plain language and get answers that cite the underlying rows. See AI Research Chat.

The AI Research Chat is the connective tissue across the other five. It runs 7 specialized tools, covering startup search, startup detail, clusters, category analysis, deals, benchmarks, and wider market data, and it picks the right tool for whatever you ask. Because every answer cites the startup rows behind it, you can always verify a figure rather than take it on faith.

A validation workflow that ties it together

The six surfaces are strongest when you move through them in order rather than treating each as a standalone tool. Most decisions follow the same arc: read the market, then read the middle of the market, then read the individual companies, and only then commit.

1. 1

   Start with a category

   Open the category analysis for the niche you are considering and read the market summary and opportunity thesis before touching any numbers.

2. 2

   Sanity-check with benchmarks

   Pull the revenue benchmarks for that category and look at the median, not just the average, to see what a typical company earns. Read p25 and p75 to bracket the realistic band.

3. 3

   Study a cluster

   Find a cluster that matches the pattern you want, whether a growth pattern or an acquisition profile, and study the whole group instead of one company.

4. 4

   Drill into startups

   Use startup search to open individual profiles and confirm the pattern holds at the company level, across the 50+ data points.

5. 5

   Ask the chat to close gaps

   Send the leftover questions to AI Research Chat, which cites the rows it used so you can verify every claim before you act on it.

What the data reveals

A few patterns show up over and over once you read TrustMRR at scale, and they reshape how you should think about a launch.

• Most SaaS earns almost nothing. The median tracked SaaS makes roughly $7 MRR across 3,478 revenue-positive startups, usually because the product skipped validation. A small number of winners lift the $4,682 average.
• Software margins are unusually kind. TrustMRR category data shows average profit margins of 60-80%, with Developer Tools around 76.8%, mobile micro-SaaS around 79.5%, and Education around 72.9%, so a modest top line can still fund a real, sellable business.
• AI is the biggest and fastest-moving category but also the most crowded. TrustMRR tracks 1,213 AI startups growing roughly 99.9% year over year on average, yet the median AI startup still earns about $7 MRR because most are thin wrappers.

How the data reads

The other habit worth building early is to read distributions, not headlines. The $4,682 average is real, but the median tracked SaaS earns roughly $7 MRR. Both numbers are true; they describe different companies. Knowing which one applies to your situation is the difference between a realistic plan and a fantasy.

// WRONG - TrustMRR values are already dollars
const mrr = row.revenue_mrr / 100; // 100x too small

// RIGHT - use the value as-is
const mrr = row.revenue_mrr; // dollars

How TrustMRR connects to the rest of BigIdeasDB

TrustMRR is the revenue layer of a larger loop. The Discover databases read 1M+ complaints to tell you which problems are real and painful. TrustMRR then tells you whether solving that class of problem has ever produced a business that collects money, and at what scale. Used together, you move from documented demand to proven revenue without leaving the platform.

• Validate a niche end to end: confirm the pain in the complaint data, then confirm the revenue in startup search and revenue benchmarks.
• Decide between build and buy: study winners through clusters and category analysis, then scan deal flow if acquiring beats building from scratch.
• Answer whatever is left in plain language through the AI Research Chat, which cites the rows behind every figure.
• When you handle raw fields in an export or query, keep the units straight with Reading MRR data correctly.