Semantic Caching Can Cut LLM API Costs by Up to 70% Using Embeddings

Semantic caching is an optimization technique for LLM-powered applications that uses vector embeddings to detect meaningfully similar queries, rather than relying on exact string matches like traditional caching. By recognizing that questions such as 'What is your refund policy?' and 'Can I get my money back?' are functionally identical, the system can serve cached responses instead of making redundant API calls. This approach can reduce LLM API costs by 30–70% and cut response latency from seconds to milliseconds without requiring changes to prompts or model configuration. In production environments, vector databases such as Redis with vector search, Pinecone, Qdrant, or pgvector are recommended to ensure the cache scales reliably. The technique is considered especially effective for applications where users frequently ask the same questions in different ways.