Inference cost calculator
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The dominant cost of an AI app is the LLM calls — and almost nobody can estimate it. Pick what you're building, tune the assumptions, and compare 29 models across OpenAI, Anthropic, Google, xAI, Mistral, OpenRouter and Workers AI.
What are you building?
Daily active users
Usage assumptions (all editable)
Agent steps count as calls, not runs.
System prompt + history + retrieved context. ~4 chars ≈ 1 token.
Output tokens cost 4–6× more than input on most models.
Share of input tokens billed at the provider's cached rate (~90% cheaper).
publishes no cached-input discount, so the cache rate is ignored for it.
Estimated monthly inference bill ·
/mo
per user / month
per LLM call
calls · input tokens · output tokens per month
Suggested for these assumptions
at scale
| Daily active users | Monthly bill | Per user |
|---|---|---|
| ← you |
Every model, same assumptions
Sorted by monthly bill. Click a row to select that model.
| Model | Provider | Tier | $/mo | $/user | vs selected |
|---|---|---|---|---|---|
| open |
About this tool
This calculator turns your product assumptions into a monthly inference bill: daily active users, LLM calls per user, input and output tokens per call, and optional prompt-cache hit rate. Pick a workload preset (chat, RAG, agent, and others) to get sensible defaults, then tweak every slider.
Use it when you are planning an AI feature or comparing models before you wire up billing. The table ranks every model on the same assumptions so you can see cheap vs capable trade-offs, not just list prices.
Figures come from published API rates (29 models today). They ignore batch pricing, enterprise discounts, image or tool surcharges, and your own caching layer — treat the output as a planning estimate, not a vendor quote.
Everything runs in your browser. A share link only encodes your inputs in the URL; no usage data is sent to a server.