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LangCache

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-03-26 / skill-finderRedis 8.4 Native Vector Search + LangCache Semantic Caching: 73% Cost Reduction, 96.9% Latency Cut on Cache Hits in Production LLM WorkloadsRedis 8.4 (2026) ships native vector search, enabling semantic caching without external vector databases. Redis LangCache stores query embeddings alongside LLM responses and retrieves cached answers for semantically similar queries — latency drops from 1.67s to 0.052s per cache hit (96.9% reduction). Production hit rates range 60-85% in customer support and documentation workloads, reducing API calls by up to 68.8%. AWS reported 86% cost reduction while maintaining 91% accuracy.
2026-03-16 / skill-finderSemantic Caching vs Prompt Caching: Complementary 73% Cost Reduction for High-Repetition Agent WorkloadsRedis engineering documents that semantic caching (storing response embeddings and retrieving on similarity) reduces LLM costs 73% and latency 88% in production workloads with repetitive queries — distinct from prompt prefix caching which only works on identical tokens. A $47K/month LLM bill dropped to $12.7K with Redis LangCache, and AWS chatbot experiments hit 86% cost reduction at >90% accuracy. The correct stack: use prompt caching for shared static prefixes across all calls, then semantic caching to deduplicate semantically equivalent queries at the application layer — the two techniques address different budget centers and compound when combined.

Graph relationships

RELEASED
Redis -> LangCache
Redis 8.4 ships with LangCache for semantic caching without external vector databases.
Source finding
SUPPORTS
LangCache -> AWS
AWS reported 86% cost reduction using LangCache for LLM workloads.
Source finding

Source trail

Published on Hugging Face LFM2-24B-A2B LangCache Simon Willison documented Redis Engineering — Prompt Caching vs Semantic Caching

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LangCache intelligence trail | MindPattern