Semantic Caching is an advanced optimization technique that stores and reuses responses from Large Language Models (LLMs) based on the underlying meaning of a query rather than an exact string match.

Unlike traditional keyword-based caching, Semantic Caching utilizes Embedding and similarity thresholds to identify if a new prompt is semantically equivalent to a previously processed request. When a high degree of similarity is detected, the AI Gateway serves the cached response instantly, significantly reducing computational costs, token consumption, and inference latency. This approach ensures a faster, more cost-effective user experience while maintaining the contextual accuracy of the AI-generated output.