Response Caching Strategy Cuts LLM Costs by Over Half—Here's How

Billing happens per token in most LLM deployments, which means identical or near-identical requests trigger duplicate charges. A straightforward but often-overlooked solution lies in response caching—storing answers already computed and reusing them when similar questions arrive—borrowed from techniques refined in data engineering over decades.

The economic incentive is substantial. For a hypothetical workload processing 1 million monthly API calls at $0.006 per call, a hybrid caching approach delivering a 60% hit rate could cut monthly spend from $6,000 to roughly $2,550 while also slashing response latency.

Two-Tier Caching Architecture#

The simplest layer is exact-match caching. Normalize the request (model settings, context, documents, user permissions), hash it with SHA-256, then look it up in a fast store like Redis. An exact match means returning the cached answer instantly, skipping model inference entirely. This works best for bounded, predictable requests: batch jobs, CI pipeline runs, boilerplate summarization.

The second layer addresses cases where queries aren't identical but are semantically similar. Embed the incoming query, search a vector database for close neighbors, and return a match if it clears a similarity threshold. The cosine similarity bar matters enormously: code-like queries typically need thresholds around 0.95+ (small wording shifts change meaning), while conversational queries tolerate looser thresholds (0.85–0.90). These numbers are starting points; real tuning depends on your embedding model and actual traffic.

A hybrid approach checks exact-match first, falls back to semantic search, and promotes semantic hits back into the exact store so future identical paraphrases hit instantly.

Freshness, Scope, and When to Skip#

Both cache tiers must key on more than query text alone: context and documents in the prompt, model version, caller permissions, and any versioned sources. Two identical questions about different documents or by users with different access must never share a cache entry.

Time-to-live (TTL) should track how quickly answers go stale. Internal HR policies may cache for weeks; market data for minutes or seconds. Live inventory and stock prices during active trading may be unsafe to cache at all. Shadow-mode testing (logging what you'd return without changing behavior) helps validate cached answers before trusting them in production.

Skip caching entirely for requests with personal or account-specific data (leakage risk), creative tasks (where variation is desired), and genuinely real-time data where staleness of even a minute matters.