Learn how to use AI API Cost, define the inputs correctly, and interpret LLM API cost results with scenario testing.
AI API Cost is designed for one job: Compare GPT-4o, Claude, & Gemini inference costs and RAG pipeline scaling. The best way to use it is to model scenarios, not to hunt for a single “perfect” number.
If you understand how inputs like input tokens, output tokens, and requests per user shape LLM API cost, you can spot mistakes quickly and keep outputs decision-ready.
This tool models LLM API cost for common cases so you can compare tradeoffs quickly.
If your scenario is unusual, treat the output as a starting point and validate assumptions before committing.
Key inputs: input tokens, output tokens, requests per user, and active users.
Be consistent about units (monthly vs annual) and scope (include fees/taxes if they exist in real life).
Compare outputs like monthly cost and cost per user across scenarios instead of trusting one number.
If the decision changes under downside assumptions, build a buffer or revise the plan.