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LLM API Cost Optimization Techniques
Practical, measurable ways to reduce LLM API costs without degrading the outcomes that actually matter for your use case.
Direct answer
The highest-leverage LLM cost optimizations are prompt caching (reusing unchanged context across calls), routing simpler tasks to smaller/cheaper models rather than a single frontier model for everything, and trimming unnecessary context (tool schemas, chat history) rather than including everything by default — measure the actual cost impact of each before assuming it matters at your volume.
Last reviewed 2026-08-29
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Prompt caching is often the single biggest lever
Many providers offer reduced pricing for context that repeats across calls (a long system prompt, a stable document). If your application re-sends the same large context repeatedly, checking whether your provider supports prompt caching and structuring calls to take advantage of it is frequently the highest-return optimization available, often larger than model selection.
Model routing: not every call needs the frontier model
Classification, simple extraction, and other low-complexity tasks often perform nearly as well on a smaller, cheaper model as on the largest available one — reserving the most expensive model for the specific steps that actually need its reasoning capability, and routing simpler steps to a cheaper model, can meaningfully reduce cost without a proportional quality loss.
Trim context, don't pad it by default
See how to fix MCP token bloat for the specific mechanics — every connected tool's schema, every turn of chat history, and every piece of retrieved context adds to the token bill on every call. Include only what a given step actually needs, rather than a maximal default context 'just in case.'