TokenAtlasThe problem
Startups consistently report the same pain: AI costs are growing faster than revenue, the engineering team can't explain the bill to finance, and there's no clear answer when leadership asks "what does AI cost us per active user?"
What changes with TokenAtlas
For a startup the question is usually singular: at what point does the AI feature stop being affordable? TokenAtlas answers it by modelling your workload against published provider rates — cost per call, per active user and per month — so a seed-stage team can see the burn implied by 1,000 users versus 50,000 before either number arrives.
The typical workflow
Start from what you already know: average prompt size, average response length, and calls per user per day. Model that on your current model, then re-run it on a cheaper tier and on next quarter's user target. The output is three numbers a founder can act on — cost per user today, cost at plan, and the delta if you route the long tail to a smaller model.
What you can model
The exercise is scenario analysis, not measurement: TokenAtlas works from token volumes you supply rather than ingesting live provider usage. What that gives a startup is a defensible range — best case, expected, and the growth case that breaks unit economics — plus a ranked view of which workload carries the most modelled spend and is therefore worth optimising first.
Why it matters for startups
Fundraising and pricing decisions both need an AI cost number that survives scrutiny. Modelled per-user and per-feature costs let a founder answer "what does this feature cost to serve" with the arithmetic attached, and re-run it the moment provider pricing or the product plan changes.
Frequently asked questions
- Is TokenAtlas appropriate for our team size?
- Yes — pricing scales from solo founders on the free tier to enterprise teams on custom contracts.
- How long does setup take?
- There is nothing to connect. You model a workload from token volumes you already know — prompt size, response size and call volume — so a first estimate takes a few minutes.
- Do you support our LLM provider?
- Published rate cards for OpenAI, Anthropic, Google, Mistral, Groq, DeepSeek, Cohere and Perplexity are modelled in the catalogue, so you can price the same workload on any of them.