How can I reduce my AI API token costs?

Measure where tokens go, then cut in this order: send easy requests to small, cheap models; use prompt caching for repeated context; trim what you send; batch work that can wait; and move steady, high-volume work to open models on hardware you own, where each extra token costs only electricity.

1. Measure. Break spend down by feature, team and model. A few workloads usually drive most of the bill.
2. Route by difficulty. Classification, extraction and short summaries rarely need a frontier model. Small models cost a fraction as much per token.
3. Cache. Agents and chat apps resend the same system prompt, files and history. Prompt caching bills repeated input at a steep discount; DeepSeek, for example, charges $0.006 per million cached input tokens against $0.30 uncached.
4. Trim context. Retrieve only the relevant documents and summarize long histories instead of resending them.
5. Batch what can wait. Overnight jobs can use discounted batch APIs or off-peak pricing.
6. Own the baseload. Steady, predictable work, such as coding agents running all day or bulk document processing, is where fixed-cost hardware beats per-token billing.

Updated Oct 26, 2026