Multi-Layer Semantic Caching for Production LLM Systems
Introduction
In a previous article, I described building an agentic search framework in Go. While that architecture handled the functional requirements well, operating it at scale revealed significant cost and latency challenges. At millions of queries per month, LLM API costs, and P95 latency approached 5 seconds.
This article presents the semantic caching architecture we implemented to address these issues. The system reduced LLM costs by 45-50% and improved P95 latency to under 2 seconds, while maintaining response freshness guarantees.
The key insight: caching at multiple granularities within the agentic pipeline provides better results than end-to-end response caching alone. Specifically, caching the agent's planning decisions—which are deterministic and independent of result freshness—achieved a 50% hit rate even with conservative similarity thresholds.
Problem Analysis
Cost Structure
A single query in an agentic search system involves multiple LLM calls:
- Planning/Tool Selection(~8,500 input tokens): Agent reads tool definitions and decides which...
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