A caching strategy that stores and retrieves AI responses based on semantic similarity rather than exact query matching.
Semantic caching is an optimization technique for AI systems that caches responses based on the semantic meaning of queries rather than their exact text. When a new query arrives, the system checks whether a semantically similar query has been answered before — if the similarity score exceeds a threshold, the cached response is returned instead of invoking the expensive LLM or retrieval pipeline. This dramatically reduces latency and cost for AI applications with repetitive query patterns.
As enterprise AI deployments scale to millions of daily queries, the cost and latency of LLM inference has become a critical operational concern. Semantic caching can reduce LLM API costs by 40-70% in production systems with repetitive query patterns. It is now a standard component of enterprise AI infrastructure, implemented by platforms like Redis, Momento, and GPTCache.
Semantic caching works by embedding incoming queries and comparing them against a cache of previously embedded queries stored in a vector database. If the cosine similarity between the new query and a cached query exceeds a configurable threshold (typically 0.92-0.95), the cached response is returned. Cache entries include the original query embedding, the response, metadata about the source, and a TTL for freshness management.
A customer service AI handles 10,000 daily queries. Semantic caching identifies that 'How do I reset my password?', 'I forgot my password', 'Can't log in, need to reset password', and 'Password reset instructions' are all semantically equivalent (similarity > 0.93). After the first query is answered, the next 9,999 similar queries return the cached response in under 5ms instead of waiting 2 seconds for LLM inference.