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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 vector database without adding another service to the stack. With version 0.8.6 and PostgreSQL 18, HNSW reached 95.1% recall@10 at 1.15 ms on 99,000 real embeddings. SQL filters share the query with vector ranking, and iterative index scans stop a filter from returning fewer rows than requested.
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.
