How to Cut Vector Database Costs by Up to 80%: A Practical Milvus Optimization Guide

This post provides a practical guide to reducing vector database costs, focusing on Milvus. It details how to select cost-effective indexes (IVF_SQ8, IVF_PQ, DiskANN) over more expensive ones like HNSW, and explains how to leverage MMap (Milvus 2.3+) and tiered storage (Milvus 2.6+) to drastically reduce memory and local disk usage. The guide also emphasizes the cost implications of embedding dimensionality and provides configuration examples for MMap and tiered storage.