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Vector databases have gone from an experimental curiosity to the central component of most LLM-based products. This comparison covers Qdrant, Pinecone, and Weaviate: architecture, strengths, limitations, and a decision tree for choosing the right option based on your operational priorities and budget.
pgvector turns PostgreSQL into a fully functional vector database without adding a separate service to the stack. It extends Postgres with the vector type, IVFFlat indexes for approximate nearest-neighbour search (ANN), and the ability to combine relational SQL filters with vector ranking in a single query. For most RAG projects and internal chatbots, those limits never become a problem.
Chroma is the easiest vector database to get started with embeddings and semantic search: install it with pip install chromadb, no extra infrastructure required, and it exposes a minimal API (add, query, delete). It suits prototypes and mid-sized RAG systems well; past a few million vectors, Qdrant or Milvus scale better.
