Caching

The fastest request is one you don’t make. Caching trades storage for speed, serving precomputed results instead of recalculating them. But caching done wrong is worse than no caching—stale data, inconsistencies, and debugging nightmares. When to Cache Cache when: Data is read more often than written Computing the result is expensive Slight staleness is acceptable The same data is requested repeatedly Don’t cache when: Data changes constantly Every request needs fresh data Storage cost exceeds compute savings Cache invalidation is harder than recomputation Cache Placement Client-Side Cache Browser cache, mobile app cache, CDN edge cache: ...

Every LLM API call costs money and takes time. When users ask variations of the same question, you’re paying for the same computation repeatedly. Semantic caching solves this by recognizing that “What’s the weather in NYC?” and “How’s the weather in New York City?” are functionally identical. The Problem with Traditional Caching Standard key-value caching uses exact string matching: 1 2 3 cache_key = hash(prompt) if cache_key in cache: return cache[cache_key] This fails for LLM applications because: ...

A practical guide to caching — when to cache, what to cache, and how to avoid the gotchas that make caching the second hardest problem in computer science.