Vector Databases
A curated collection of the best vector databases for storing embeddings and enabling semantic search over scraped content.
Quick answer
Vector database choice depends on deployment scale and retrieval shape. Qdrant fits production vector search with filtering, Milvus fits distributed deployments, Weaviate fits AI-native schemas and hybrid search, Chroma fits prototypes and embedding apps, and pgvector fits teams keeping retrieval inside PostgreSQL.
Top picks in Vector Databases
Qdrant
Vector filteringFits production vector search where payload filtering, hybrid retrieval, and straightforward deployment are important.
Frequently asked questions
What are examples of vector databases?
Qdrant is a vector database for similarity search with filtering and hybrid retrieval. pgvector is a PostgreSQL extension that adds vector storage and nearest-neighbor search to an existing relational database. It is an extension, rather than a separate database service.
Sources (checked ): Qdrant documentation; pgvector documentation
When to avoid vector databases?
For exact identifiers, ordinary filters or relational joins, start with a database that already handles those queries. Use vector retrieval when similarity between useful embeddings serves your application. An extension such as pgvector can also combine vector retrieval with existing SQL data.
Sources (checked ): pgvector: vectors alongside SQL; Qdrant retrieval documentation