Category benchmarks
AI SaaS valuation benchmarks by category
Compare observed sale prices, recurring-revenue multiples and time-to-exit across AI software segments. Every aggregate is calculated from sourced transactions.
- Categories represented
- 13
- Exits tracked
- 136
- Overall median ARR ×
- 2.89×
AI SaaS exit benchmark table
Categories are ordered by median disclosed sale price. A dash means the dataset does not have enough disclosed inputs to calculate that metric without guessing.
| Category | Deals | Median price | Median ARR × | Median months |
|---|---|---|---|---|
| Audio / voice | 1 | $200,000 | 2.22× | 11 |
| Analytics / scoring | 6 | $128,000 | 1× | — |
| GEO / AEO | 1 | $85,000 | 14.17× | — |
| Image gen | 7 | $47,500 | — | 3 |
| Dev tools | 13 | $35,000 | 3.83× | 6.5 |
| Chatbot / agent | 7 | $27,500 | 0.85× | 12 |
| Other | 37 | $18,250 | 6.25× | 5.5 |
| Health / fitness | 2 | $10,000 | — | 12 |
| Directory / database | 23 | $7,000 | — | — |
| Productivity | 19 | $6,000 | 2.22× | 12 |
| SEO / content | 5 | $5,000 | 2.89× | — |
| Text gen | 14 | $1,450 | 9.32× | 9 |
| Video gen | 1 | — | — | — |
Why category changes the comparison
Two products with the same MRR can have very different cost structures and risks. Image and video generation products may carry significant inference costs. Developer tools can benefit from sticky workflows but depend on technical users. Content and SEO products can scale quickly while facing intense competition and platform changes.
Category is therefore a starting point, not a valuation model. Revenue retention, margins, growth, customer concentration and founder workload still need to be considered at the individual deal level.
How to read small-sample benchmarks
Use the count beside every category
A median based on a handful of public transactions is sensitive to each new deal. Treat it as observed evidence, not a precise market price.
Different metrics can use different subsets
The deal count covers tracked exits in the category. Median price, multiple and time use only records with the inputs required for that calculation.
Private-market disclosure is uneven
Marketplace exits are more likely to expose prices than private acquisitions, so public benchmarks can over-represent certain deal sizes and seller profiles.
From category benchmark to deal analysis
Start with the closest product category, then compare revenue quality, margin, growth and transferability. The valuation multiple guide explains the ARR calculation, while the micro-SaaS exit overview covers acquisition channels and time-to-exit.
Methodology
Benchmarks are calculated from the current WrapperExits dataset. Sources include acquisition marketplaces, public announcements, founder interviews and other cited records. Missing financial figures remain null and are excluded from the corresponding median. Categories describe the product's primary use case and may be revised as the dataset improves.