Why does caching help lower LLM API bills?
How caching reduces costs
When you cache a response, you avoid calling the API for identical queries. For example, if many users ask 'What are your hours?', you can serve a cached answer without paying for tokens. This is especially effective for FAQs or common intents.
Prompt caching, offered by some providers, caches the processed input tokens for repeated prompts. If you use the same system prompt or context across requests, you pay a reduced rate for those tokens after the first time.
- Response caching: store and reuse full answers for identical queries.
- Prompt caching: discount on repeated input tokens.
- Semantic caching: match similar queries to cached responses.
- Cache invalidation: update when information changes.
- TTL: set expiration to avoid stale answers.
Implementation tips
Use a key-value store like Redis to cache responses, with a hash of the query as the key. For prompt caching, structure prompts so static parts come first, as some providers cache prefixes. Monitor cache hit rates to ensure effectiveness.
Be mindful of privacy: don't cache sensitive user data. Also, caching works best when queries are repetitive; for unique queries, it offers little benefit.
Common mistakes
- Caching everything without considering that unique queries won't benefit.
- Forgetting to invalidate cache when underlying data changes, leading to wrong answers.
- Assuming all providers offer prompt caching; it's not universal.
