Prompt Caching: Add a Caching Layer to Cut LLM Costs Without Cutting Quality
Prompt caching explained: how API caching layers work, when a caching layer saves money, and how to design prompts so you cache more and pay less.
Prompt caching lets providers reuse previously processed prefix tokens at a big discount. If your system prompt and context are stable across requests, caching can cut input-token cost dramatically — often 50-90% on the cached portion.
How prompt caching works
- The provider caches processed tokens for a prefix of your prompt.
- On the next request, the same prefix is matched from cache.
- Cached tokens bill at a reduced rate (often 1/10th or less).
- Different providers have different cache sizes, TTLs, and pricing.
When caching saves money
- Chat apps: system prompt + conversation history are stable.
- Agents: repeated instructions and tool schemas.
- Batch jobs: same prefix, different inputs.
- RAG: a large fixed context plus changing question.
Designing prompts for caching
- Put stable content first: system prompt, tool schemas, fixed context.
- Keep variable content (the actual question) at the end.
- Avoid changing the prefix between calls.
- Reuse the exact same system prompt string across requests.
Caveats
Cache misses cost the same as normal, cache TTLs expire, and caching may not apply to streaming on every provider. Verify your provider's cache rules and monitor cache-hit rates.
FAQ
Does prompt caching reduce quality?+
No. It is a billing and compute optimization for identical prefix tokens — output quality is unchanged.
How much does prompt caching save?+
Providers typically discount cached input tokens by 50-90% versus uncached. Savings depend on your prefix hit rate.
Which providers support prompt caching?+
Most major providers now offer some form of prompt caching. Check each provider's docs, cache sizes, TTL, and pricing.
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