LLM Semantic Caching: The 95% Hit Rate Myth (and What Production Data Actually Shows)
This article debunks the myth of 95% cache hit rates for semantic caching of large language models (LLMs). It explains the differences between exact caching and semantic caching, and shares real-world production data showing hit rates in the 20-45% range.
Why it matters
Accurate understanding of semantic caching performance is critical for teams looking to optimize their LLM costs and latency.
Key Points
- 1Published production hit rates for semantic caching range from 20-45%, not 90-95%
- 2Even a 20% hit rate can save significant costs and latency on LLM requests
- 3Teams should start with exact caching and only add semantic caching if the marginal improvement justifies the complexity
Details
The article explains that the '95% cache hit rate' claim refers to the accuracy of cache matches, not the actual frequency of hits. In reality, production data shows semantic caching hit rates in the 20-45% range. Even a 20% hit rate can save $1,000/month on a $5K LLM bill while cutting latency from 2-5 seconds to under 5 milliseconds on cached requests. The article outlines the differences between exact caching (hashing the full prompt) and semantic caching (using vector embeddings to find similar prompts). It recommends starting with exact caching and only adding semantic caching if the marginal improvement justifies the added complexity.
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