Key Takeaway
Semantic caching using embedding similarity can achieve cache hit rates of 30-50% for typical LLM applications while maintaining output quality above user-noticeable thresholds. This guide covers five caching patterns from exact match to predictive prefetching, with implementation examples, cache key design, invalidation strategies, and hit rate benchmarks.
Prerequisites
- An AI inference endpoint in production with observable request patterns
- Redis, Memcached, or equivalent in-memory cache infrastructure
- Access to an embedding model for semantic caching (any text embedding API)
- Understanding of your application's freshness requirements (how stale can cached responses be?)
- Cost tracking to measure the ROI of caching implementation
Why AI Caching Is Different
Traditional web caching matches exact request keys: the same URL with the same parameters returns the same cached response. AI caching faces two challenges that make exact matching insufficient. First, semantically equivalent queries may have different surface forms ('What is the capital of France?' and 'France capital city?' should return the same cached answer). Second, AI outputs are often non-deterministic, meaning identical inputs may produce different (but equally valid) outputs, making cache validation more nuanced.
The reward for solving these challenges is significant. LLM API calls are expensive (dollars per thousand requests at frontier model pricing) and slow (seconds of latency). A cache hit eliminates both the cost and the latency, providing a cached response in milliseconds for zero API cost. Even modest cache hit rates of 20-30% translate to meaningful cost reduction and latency improvement.
Pattern 1: Exact Match Cache
Exact match caching is the simplest pattern: hash the normalized input and look up the hash in a key-value store. If found, return the cached response. If not, call the model and cache the result. This works well for deterministic queries with structured inputs (e.g., classification of product descriptions, extraction from invoices) but poorly for free-form queries where users phrase the same question differently.
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