How to Calculate AI API Costs
AI API pricing usually depends on how many input and output tokens you use. Input tokens are the prompt, instructions, system messages, and any context you send to the model. Output tokens are the model’s response. Most providers price each side separately, so accurate estimates require both numbers.
To estimate cost per request, multiply your average input tokens by the provider’s input price per million tokens, then do the same for output tokens and add them together. Once you know cost per request, multiply by your daily request count to project daily, monthly, and yearly spend.
Most teams can reduce spend quickly by matching model size to task complexity. Simple classification, summarization, tagging, extraction, and routing usually work well on cheaper models, while advanced reasoning and long-form generation can justify more expensive ones. That is why comparing multiple models before launching a workflow matters.
Other proven cost controls include batching requests, caching repeated prompts, shortening system instructions, limiting maximum output tokens, and trimming unnecessary context. You can also see the full LLM Pricing Table or use our AI Subscription Optimizer to find savings across your stack.