LLM gateway caching: how it works and when to use it

LLM gateway caching can reduce model costs and latency by serving reusable responses or prompt computations before a full generation is required, with savings determined by cache hit rates, bypassed token volume, and expiration settings. Exact-match caching returns a complete stored response only when request content, model, and generation parameters match, making it suited to deterministic prompts, evaluations, CI runs, and repeated API requests; Braintrust Gateway provides this approach with configurable modes, TTLs, cache-status headers, encryption, and optional sharing controls. Semantic caching uses embeddings and similarity thresholds to reuse answers for paraphrased requests, potentially increasing hit rates but introducing a risk of incorrect matches and requiring supporting embedding and vector-store infrastructure. Provider prompt caching instead reuses computation for repeated prompt prefixes while still processing variable input and generating new output, reducing input costs without eliminating output-token charges. Selecting a method depends on whether requests, meanings, or prompt prefixes repeat, while volatile information such as prices, inventory, permissions, and account state requires short TTLs or cache bypasses. Effective production use also depends on including current context in requests, evaluating cache behavior before deployment, monitoring stale or inaccurate responses after launch, and distinguishing cached responses from fresh model generations.

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