VectorDatabasesExplained

Traditional databases search by exact match: SQL queries, keyword lookup, filter conditions. Vector databases search by meaning. They store high-dimensional embeddings and find the most semantically similar results using approximate nearest neighbor (ANN) algorithms.

This is what powers RAG systems, semantic search, recommendation engines, anomaly detection, and any application where you need to find 'things like this' rather than 'things exactly matching this query'.

For prototyping: ChromaDB. For production with existing Postgres: pgvector. For managed simplicity: Pinecone. For self-hosted with hybrid search: Weaviate. For high-performance filtered search: Qdrant.

The embedding model matters more than the database. A great embedding model with pgvector will outperform a mediocre model with the most sophisticated vector database. Optimize your embeddings first, then scale your infrastructure.