Provider-side prompt caching, covered in depth elsewhere in this series, discounts the cost of reprocessing a repeated prompt prefix. Application-level caching is a complementary strategy that can eliminate an API call entirely for a repeated or near-duplicate request, and it is worth layering on top of provider-side caching rather than treating the two as alternatives.
Exact-Match Response Caching
For any workload where the exact same input is likely to recur, such as a common FAQ question, a frequently requested document summary, or a popular product description generation, caching the full response keyed on the exact input avoids an API call entirely on a cache hit, which is strictly cheaper than even a heavily discounted cached-prompt call, since no tokens are billed at all.
Set an appropriate cache expiration policy based on how often the correct answer might change; a static FAQ answer can be cached for a long period, while a response depending on frequently changing underlying data needs a much shorter cache lifetime or explicit invalidation when the underlying data changes.
Semantic Caching for Near-Duplicate Queries
Exact-match caching misses the common case where users phrase a similar question differently, such as two different wordings that are asking essentially the same thing. Semantic caching addresses this by embedding incoming queries and checking for a sufficiently similar cached query, using a vector similarity search rather than exact string matching, and returning the cached response if a close enough match is found.
This approach introduces its own cost, an embedding call on every query even on a cache hit, and a similarity threshold that needs careful tuning: too loose a threshold risks returning an incorrect cached answer for a genuinely different question, while too strict a threshold reduces the hit rate enough that the caching layer offers little benefit.
Where Response Caching Is a Poor Fit
Highly personalized or context-dependent responses, where the correct answer genuinely depends on user-specific state, conversation history, or real-time data, are poor candidates for response caching, since a cached response from a different user or a different point in the conversation would likely be incorrect regardless of surface-level query similarity.
Creative or intentionally varied generation tasks, where users expect a fresh, non-repeated response even for a similar prompt, also work against caching's core assumption, and forcing a cache in this context would degrade the product experience even if it reduced cost.
Combining Caching Layers
A well-designed system can layer exact-match caching, semantic caching, and provider-side prompt caching together, checking exact-match first as the cheapest and most precise option, falling back to semantic caching for near-duplicates, and finally falling through to a live API call that still benefits from provider-side prompt caching on any stable prefix.
Monitor hit rate separately for each caching layer, since a low semantic cache hit rate might indicate a similarity threshold that is too strict or a query distribution too varied for this technique to add much value, information that is only visible if the layers are instrumented and measured independently rather than as a single combined caching metric.
Key takeaways
- Use exact-match response caching for workloads with genuinely recurring identical inputs.
- Consider semantic caching for near-duplicate queries, but tune the similarity threshold carefully.
- Avoid response caching for highly personalized, context-dependent, or intentionally varied generation tasks.
- Set cache expiration based on how frequently the correct answer might change.
- Instrument each caching layer separately to see which is actually contributing hit rate and savings.
Bottom line
Application-level caching, layered underneath provider-side prompt caching, can eliminate API calls entirely rather than merely discounting them, making it one of the most impactful optimizations available for workloads with genuine query repetition. The key discipline is matching the caching technique, exact-match or semantic, to whether your workload actually has the kind of repetition each technique is built to exploit.