LLM Caching Strategies: Cache Design for Lower Token Bills
LLM caching strategies beyond prompt caching: semantic caching, cache key design, TTLs and invalidation, and how to measure hit rates and real savings.
Most LLM spend is repeat work. The same system prompt, the same document, the same question pattern hit the API over and over. Caching is how you turn that repeat traffic into a discount — but prompt caching alone leaves money on the table. The full playbook has layers.
Layer one: provider prompt caching
Your first layer is provider-side prompt caching, which discounts re-sent prefix tokens. It is automatic on most platforms once the same prefix appears repeatedly. Design for it: put stable instructions and fixed context first, the variable question last, and keep the prefix byte-identical between calls. Check each provider's cache TTL and size limits, and watch your cache-hit rate in the dashboard.
Layer two: semantic caching at the answer level
The second layer caches at the answer level: before calling the model, check whether a near-identical question has been answered. Embed the question, compare against a cache of recent questions with a similarity threshold, and return the stored answer when it matches. This works brilliantly for support, FAQ-style assistants, and lookup-heavy chatbots where users ask the same thing with slightly different words.
Designing cache keys that actually hit
- Include the model, temperature, and response format in the key.
- Exclude timestamps and request IDs that change every call.
- Normalize whitespace and casing before hashing.
- For semantic cache, key on question embeddings, not raw strings.
- Bump the version field when you change a system prompt or policy.
- Purge by policy version, not just by age.
TTLs and invalidation
LLM answers go stale — pricing pages change, policies get updated, products ship. Give every cache entry a TTL that matches the volatility of its data, and support forced invalidation when you edit the underlying content. A support chatbot answering yesterday's policy is worse than no answer at all. Store the source document version alongside the cached answer so you can evict on change.
Measuring the actual gain
- Track cache hit rate per provider and per endpoint.
- Log tokens saved per day, not just requests saved.
- Compare spend before and after enabling each layer.
- Watch for quality regressions from stale semantic-cache hits.
- Recompute the breakeven whenever a provider changes cache pricing.
FAQ
What is semantic caching for LLMs?+
Caching entire answers for semantically similar questions by comparing question embeddings, so repeat questions skip the model call entirely and return a stored response.
Does caching hurt answer quality?+
Prompt caching does not change output at all. Semantic caching can serve stale answers, which is why cache entries need TTLs and invalidation tied to content changes.
How much can caching save?+
Prompt caching discounts cached input tokens by up to 90%; semantic caching removes whole calls. Combined, teams often cut token spend by half or more on repetitive workloads.
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