Build a two-tier LLM cache to cut API costs

This tutorial details how to build a two-tier caching layer for LLM API calls to reduce costs. The first tier uses Redis for exact-match caching based on SHA-256 hashes of prompts and models. The second tier employs cosine similarity on embeddings to detect and cache semantically similar queries, preventing redundant LLM API calls. Implementing this can save significant costs, with a potential 40% cache hit rate reducing daily expenses by $200 in the example provided. AI

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IMPACT Reduces operational costs for applications leveraging LLM APIs by caching responses.

RANK_REASON The article describes a technical implementation for optimizing LLM API usage, which falls under tooling.

Read on dev.to — LLM tag →

• LLM
• OpenAI
• Redis

• infra
• product