How to Estimate OpenAI & Claude API Cost Before You Ship

Cost is tokens × price × volume

Text LLM APIs usually charge separately for input and output tokens. Your monthly bill is roughly: (avg input tokens × input price + avg output tokens × output price) × requests. Miss any one of those inputs and the forecast is fiction.

Start from real prompts. Paste a production-like system prompt and user message into AI Lab, note the family token estimate, then set an output ratio (replies are often 20–100%+ of input for chat; agents can be higher).

Budget buffers teams forget

Retries, tool/function calls, evaluation runs, and long multi-turn threads multiply tokens. If you plan launch-day traffic, add a buffer — many teams use 1.5× to 3× on early forecasts.

Also compare model tiers. A smaller model with a slightly longer prompt can still be cheaper than a frontier model with a short prompt. AI Lab’s side-by-side tokens plus price chips make that tradeoff visible quickly.

• Price lists change — re-check vendor pages before locking a budget.
• Cache static system prompts server-side when the vendor supports it.
• Measure with sample traffic, then revisit the calculator.

Ship a cheaper prompt, not just a cheaper model

The highest-leverage cut is often deleting unused instructions, examples, and retrieved context. After trimming, re-run the token compare. If cost is still high, step down a model tier for classification-like tasks and keep the larger model for hard reasoning.

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