TokenAtlasThe problem
SaaS teams 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 SaaS product the AI line item lands directly on gross margin, and it moves with usage rather than with headcount. TokenAtlas models cost per call, per seat and per month for each AI feature from published rates, so you can see which parts of the product are margin-accretive at the current price point and which are not.
The typical workflow
Model each AI feature separately — summarisation, chat, search — since their token profiles differ sharply. Enter typical prompt and completion sizes and expected calls per seat per month, then compare the modelled cost against the revenue that seat produces. Repeat the same scenario on a cheaper model to see how much margin a routing change would return.
What you can model
This is scenario modelling from token volumes you supply, not live usage ingestion. The useful outputs for a SaaS team are a modelled cost-to-serve per plan tier, the usage level at which a flat-rate tier stops covering its own inference cost, and a side-by-side of what the same feature would cost on a smaller model.
Why it matters for saas teams
Pricing changes, plan design and margin reporting all depend on a cost-to-serve figure you can defend. Modelling it per feature and per tier means the number can be recalculated when a provider changes rates, rather than rediscovered at the end of the quarter.
Worked example: a summarisation feature
A SaaS summarisation feature at 1,200 input / 300 output tokens per call, run 200,000 times a month, is 240M input and 60M output tokens. On GPT-4o that models to 0.7x$2.50x240 + 0.3x$10x60... concretely: 240M/1M x $2.50 = $600, plus 60M/1M x $10 = $600, for $1,200/month. On Claude 3.5 Haiku the same volume is 240M/1M x $0.80 = $192 plus 60M/1M x $4 = $240, for $432/month.
Cost drivers ranked
For a per-seat SaaS feature, calls per seat per month ranks first since it multiplies every other input. Output length ranks second on generation-heavy features (drafting, summarising) and input length ranks second on retrieval-heavy features (search, Q&A over documents). Model choice is the lever you control without touching the product; volume and prompt shape are largely set by how the feature is used.
A cheaper model can be a false economy
Swapping a feature from GPT-4o to a cheaper model lowers the modelled per-call cost, but if it increases retries, requires a second call to fix a bad answer, or forces a longer prompt to compensate for weaker instruction-following, the net monthly figure can end up higher than the number the model picker suggests. Model the retry-adjusted scenario, not just the headline per-token rate.
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.