TokenAtlasThe problem
Enterprise 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
In an enterprise the difficulty is rarely a single bill — it is a dozen teams shipping LLM features on different models with no shared basis for comparison. TokenAtlas gives them one modelling method: published per-token rates, the same scenario inputs, and cost per call, per user and per month expressed the same way for every workload.
The typical workflow
Model workload by workload, in the same format each time: prompt size, completion size, volume, model. Run the approved-model scenario next to the alternatives so an architecture review can see the cost of the choice, and keep the scenarios so the numbers can be refreshed when a provider updates its rate card.
What you can model
TokenAtlas models scenarios from volumes you supply — it does not ingest provider usage or require API keys, which keeps the exercise outside the data-access review that usually delays this kind of tooling. The output is a comparable cost estimate per workload, a modelled forecast for the planning cycle, and the arithmetic behind both.
Why it matters for enterprise teams
Budget approvals and architecture reviews both need an LLM cost figure that is consistent across teams and reproducible six months later. Shared modelling assumptions make two proposals genuinely comparable, and make it obvious which workload is driving the forecast.
Choosing a model consistently across teams
Blended at 70/30: GPT-4.1 = $3.80/1M, Claude 3.5 Sonnet = $6.60/1M, Mistral Large = 0.7x$2+0.3x$6 = $3.20/1M, Llama 3.1 70B = 0.7x$0.59+0.3x$0.79 = $0.653/1M. Publishing this table as the shared reference for architecture reviews means two teams proposing different models can be compared on the same basis before either one ships.
What moves an enterprise-wide estimate most
With many teams shipping workloads independently, the aggregate estimate is driven less by any single team's volume and more by which model each team defaults to. Two teams with identical call volumes on GPT-4o mini ($0.285/1M) versus GPT-4o ($4.75/1M) produce a roughly 17x difference in modelled cost for equivalent usage — the model-selection policy matters more than any one team's optimisation.
Sensitivity scenarios for planning
Model each approved workload at current volume, then at a headroom multiple (2x, 5x) tied to the rollout plan, so a budget approval reflects the scaling case rather than only the pilot case. Re-run the same set of scenarios whenever a provider revises its rate card, since a governance process built on a stale table understates or overstates every subsequent proposal.
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.