pgvector, Qdrant, or Milvus: How to choose a vector database for RAG

This article addresses the challenge of selecting the right vector database for RAG systems. The author notes that while prototyping often seems straightforward, scaling frequently leads to issues with performance, data filtering, and infrastructure maintenance. The material provides a comparative analysis of three popular solutions: PostgreSQL with the pgvector extension, the specialized vector database Qdrant, and the high-performance Milvus system. The author explains when PostgreSQL's capabilities are sufficient, when it is worth switching to Qdrant for search optimization, and under what conditions the complex architecture of Milvus is justified. The article helps developers avoid over-engineering their systems and choose a tool that aligns with the project's actual needs, considering data volume and query latency requirements.