Reusing the cached prefix of a prompt across calls to dramatically reduce input token cost when the same context is sent repeatedly.
Prompt caching is a feature offered by major LLM providers (Anthropic, OpenAI, Google) that lets you reuse a stable prefix of your prompt across multiple calls. Cached input tokens are billed at a substantial discount — typically 10–25% of standard input pricing for cached portions. To take advantage of it, the prompt must be structured so the stable content (system prompt, tool definitions, long context) sits at the beginning, and per-request content sits at the end.
For high-volume workloads where the same system prompt and tool definitions are sent on every call (most production AI agents), prompt caching can deliver 30–60% reduction in input token cost with no architectural change — purely a configuration win. It is one of the lowest-effort, highest-ROI optimisations in the AI cost stack.
Prompt caching is a tactic, not a strategy. Across Skopa audits, it typically captures about 12% of total available cost savings — significant, but not where the order-of-magnitude wins live. We always enable it where the workload supports it, but we never start a cost optimisation engagement with it. The bigger wins are in orchestration cleanup and prompt and context discipline. Caching becomes valuable only after those layers are clean.