Semantic Caching Cuts Enterprise AI Costs by Reusing Answers to Similar Queries

Enterprise RAG systems increasingly struggle with high latency and costs as repeated LLM inference for semantically identical but differently worded queries drives up infrastructure expenses. Semantic caching addresses this by using vector embeddings and similarity search to detect questions with the same intent, allowing previously generated responses to be reused without triggering fresh retrieval and inference cycles. An AWS evaluation of nearly 64,000 real chatbot queries found that semantic caching can reduce inference costs by up to 86% and improve latency by up to 88% while maintaining response quality above 91%. Unlike traditional string-matching caches, semantic caching recognises that natural language users constantly paraphrase the same underlying question, meaning production AI systems contain far more repetition than most teams anticipate. The core architectural insight is that eliminating redundant computation across semantically equivalent queries can fundamentally improve the economics of large-scale AI deployments.