How do you design a global caching strategy for LLM systems?

Global caching reduces cost and latency by storing and reusing semantically similar LLM responses across users and regions.

Unlike traditional caching, LLM caching must support semantic similarity. Systems use embedding-based cache keys to store responses for similar queries. Global caches are distributed across regions with invalidation policies based on prompt version, model version, and knowledge freshness requirements.