Data & AI

LLM Semantic Caching with Redis

Data & AI Pipelines diagram · made in Skemio

Architecture diagram of LLM semantic caching showing a user request routed to an embedding model, checked against Redis vector cache, and conditionally hitting OpenAI API on a miss.
Architecture diagram of LLM semantic caching showing a user request routed to an embedding model, checked against Redis vector cache, and conditionally hitting OpenAI API on a miss.
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What it shows

  • Cosine similarity threshold (>0.92) catches semantic duplicates reliably.
  • Drops p95 latency from 1,800ms down to sub-15ms for cached queries.
  • Cuts third-party LLM API token spend by 25-45% on repetitive workloads.

The takeaway

Semantic caching turns expensive probabilistic LLM queries into deterministic microsecond key-value lookups.

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