Tutorialembeddingsvector databasevalkeysentence transformers
Developers Build Semantic Cache To Reduce Costs
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A technical post explains how to implement semantic caching using vector embeddings and a vector database to reduce LLM API costs. For a 10,000-queries-per-day customer support chatbot, a 60% hit rate reduced monthly API spend from $1,230 to $492 in the author's test. The post provides Python code using sentence-transformers and Valkey/Redis, and reports a 250x latency improvement (7s vs 27ms).
