Semantic caching stores LLM responses indexed by the semantic meaning of queries rather than exact string matches, enabling cache hits for paraphrased questions that would miss traditional caches.
Semantic caching stores LLM responses indexed by the semantic meaning of queries rather than exact string matches, enabling cache hits for paraphrased questions that would miss traditional caches. It uses embedding similarity to determine whether a new query is close enough to a cached query to reuse the stored response.
Traditional caching requires exact input matches. For LLM applications, this is ineffective because users phrase the same question differently every time. "What's the weather in NYC?" and "Tell me NYC weather today" are semantically identical but would miss an exact-match cache. Semantic caching embeds incoming queries and searches for cached responses whose query embeddings are within a configurable similarity threshold (typically 0.95+ cosine similarity).
The implementation requires an embedding model to encode queries, a vector store for similarity search, a similarity threshold that balances hit rate against accuracy, and TTL (time-to-live) policies for cache expiration. The threshold is critical: too low and the cache returns irrelevant responses; too high and the hit rate drops to near zero. Production systems typically start conservative (0.98) and tune downward based on user feedback.
Semantic caching can reduce LLM API costs by 30-60% for applications with repetitive query patterns (customer support, FAQ-style interactions, search). Beyond cost savings, cached responses have near-zero latency compared to 1-3 seconds for model inference, dramatically improving user experience for common queries.
A customer support chatbot handles 50,000 queries per day, with 40% being variations of the same 200 questions. Semantic caching with a 0.96 similarity threshold serves these repeated queries in 50ms instead of 2 seconds, reducing daily API costs from $800 to $350 while improving median response time by 60%.
Aaron is an engineering leader, software architect, and founder with 18 years building distributed systems and cloud infrastructure. Now focused on LLM-powered platforms, agent orchestration, and production AI. He shares hands-on technical guides and framework comparisons at fp8.co.