Qdrant

Vector Databases in Production: Architecture, Filtering Strategies, and Scale Ceilings for pgvector, Qdrant, Milvus, and Pinecone

The rapid deployment of retrieval-augmented generation (RAG) and semantic search has turned vector databases from specialized academic tooling into core production infrastructure. However, engineering teams face conflicting architectural paradigms. On one side, the relational database ecosystem argues that vector extensions inside existing databases eliminate operational overhead. On the other side, dedicated vector database vendors argue that relational engines cannot handle high-dimensional ge