Reduce AI Token Usage: Practical Guides

Estimate the cost before changing your workflow.

Estimated cost per request

Input (M tokens) × cache-hit rate × cached-input price

+Input (M tokens) × (1 − cache-hit rate) × uncached-input price

+Output (M tokens) × output price

Token usage is in millions; prices are per 1M tokens. Cache-hit rate is a fraction from 0 to 1.

Use your provider's actual rates and measured cache hits. Cache writes, tools, and other charges may add cost. Then check whether the change still produces an acceptable answer.

Estimate your token costs →

6 guides

Caching6 min read

Prompt caching, explained

Learn what is reused and check cache usage.

Context5 min read

Keep context useful, not endless

Trim repeated history and oversized tool output.

Structured output4 min read

Ask for the output you actually need

Reduce extra generation and parsing failures.

Coding plans4 min read

Coding plans vs. API billing

Choose a billing approach by checking compatibility, usage limits, and your actual workload.

Model routing5 min read

When do decision models actually save money?

Measure complete-task cost, fallback frequency and accepted quality before claiming token savings.

Caching8 min read

Repeated document QA: does prompt order reduce cost?

In a 24-request pilot, document-first prompts reused 12,800 input tokens. See initial versus later requests, estimated costs, answer failures and the reproducible data.