Choosing Vector Stores for Retrieval Workloads

This article provides a comprehensive guide to choosing vector stores for retrieval workloads in IT/technology architectures. It covers key index types including HNSW, IVFFlat, and DiskANN, and compares popular vector databases such as pgvector (PostgreSQL integration), Milvus (distributed scale), Weaviate (hybrid search), and LanceDB (disk-native ML workflows). The guide emphasizes practical tradeoffs in recall, latency, memory usage, and scalability for production deployment, making it relevant for developers, data engineers, and tech professionals working with RAG pipelines, recommendation systems, and semantic search.