Most LLM Calls Are Waste. Here's the Math.

Semantic caching for Large Language Models (LLM) APIs is a promising approach to reduce redundant API calls by caching semantically similar responses rather than exact text matches, potentially saving costs in high-repetition environments like customer support. Research indicates that semantic caching can eliminate a significant portion of API calls, with cache hit rates varying from 20% to 70% depending on the workload's nature. However, studies often overlook real-world complexities, such as the dynamic nature of cache hit rates that improve over time, the role of deterministic routing in bypassing unnecessary LLM calls altogether, and the impact of retrieval quality on reducing iterative LLM calls. The combination of these mechanisms can yield substantial reductions in API usage, though the exact savings depend on workload characteristics and the integration of these mechanisms into an efficient infrastructure. While some workloads, such as customer support, can achieve up to 85% reduction in LLM calls, novel and creative tasks see minimal reductions due to their unique and non-repetitive nature.

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