How to estimate AI API cost
A practical guide for turning token pricing tables into a monthly API budget before a prompt workflow reaches production.
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1. Estimate requests per month
Start with real product usage: active users, sessions per user, and model calls per session. For early planning, test low, expected, and high scenarios.
2. Split input and output tokens
Input tokens include prompts, system instructions, retrieved context, and chat history. Output tokens include the model response. Treat them separately because most providers price them differently.
3. Compare blended monthly cost
Per-token pricing is hard to reason about. Convert every model to the same usage profile, then compare total monthly cost, benchmark fit, context window, latency, and source status.
4. Verify provider-specific details
Caching, batch APIs, image/audio tokens, fine-tuning, regional pricing, and discounts can change the result. Use ModelMeter for planning, then verify final terms with provider sources.
API cost FAQ
What is the quickest way to estimate AI API cost?
Start with monthly requests, input tokens per request, output tokens per request, and the model's input/output price per 1M tokens.
Why do input and output tokens have different prices?
Most providers price generated output higher than input because output consumes inference time and capacity differently.