Caching RAG Retrieval Queries at the HTTP Edge Can Cut Vector DB Costs

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A team managing a customer support chatbot over two million documents found that just 38% of vector lookups were driven by repetitive queries like 'reset password' and its variations. Analysis revealed the retrieval pipeline was regenerating embeddings and re-traversing the same index thousands of times daily, inflating Pinecone costs unnecessarily. Common fixes like in-process LRU caches or shared Redis clusters introduce problems such as low hit rates across pods, thundering herd restarts, and multi-tenant data leakage risks. A more effective approach involves placing a dedicated HTTP caching gateway in front of the retrieval service and switching from POST to idempotent GET endpoints, enabling standard proxy-level caching. This allows repeated identical retrieval queries to be served from cache without touching the vector database, reducing both latency and API costs.

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