Vector databases store high-dimensional numerical representations of data – text, images, audio, or code – and retrieve the items most similar to a query vector through approximate nearest-neighbour (ANN) search. They are the foundational data layer for RAG systems, semantic search, recommendation engines, and multimodal AI applications. Without efficient vector retrieval, most production AI applications could not run at the scale or speed users expect.

The core operation is embedding-based search: your AI model converts a query into a vector, the database searches its index for the closest stored vectors, and returns the matching items. Specialised ANN algorithms like HNSW (Hierarchical Navigable Small World graphs) and IVF-PQ (Inverted File Index with Product Quantisation) make this search fast enough to query millions of vectors in milliseconds. Pinecone is the leading managed vector database for production RAG; it handles index management, scaling, and metadata filtering without requiring database administration. Weaviate and Qdrant are open-source alternatives with active communities and strong multi-tenancy support. Chroma is popular for local development. For teams that want to avoid adding a new database to their stack, pgvector adds vector search to PostgreSQL, and Redis Stack adds it to Redis – allowing vector retrieval alongside existing relational or key-value data.

The data layer around vector search is evolving beyond pure ANN retrieval. Hybrid search – combining dense vector similarity with sparse keyword (BM25) scores – consistently outperforms pure vector search on enterprise document retrieval benchmarks and is now a standard feature in all major platforms. Multi-vector representations, where a document is indexed as multiple segment-level vectors rather than one whole-document embedding, improve recall for long documents. Metadata filtering lets you restrict searches to subsets of the index (only documents from a specific customer, only content dated after a certain time) which is essential for multi-tenant RAG systems. As organisations build larger AI data estates, the ability to handle billions of vectors with sub-100ms latency at reasonable cost is becoming a competitive differentiator for vector database platforms.

Frequently Asked Questions

What is a vector embedding?

A vector embedding is a numerical array (typically 384 to 3072 numbers) that represents the semantic meaning of a piece of data – text, an image, audio, or code. Similar items end up with similar embeddings (close together in vector space), which is what makes it possible to find “semantically related” content through mathematical distance calculations rather than exact keyword matches.

Do you need a specialised vector database or can PostgreSQL handle it?

For small to medium workloads (up to a few million vectors with moderate query rates), pgvector in PostgreSQL is often good enough and avoids the operational complexity of a separate database. For large-scale production RAG with tens of millions of vectors and tight latency requirements, a specialised database like Pinecone, Weaviate, or Qdrant will deliver better performance and manageability.

What is hybrid search and why does it perform better than pure vector search?

Hybrid search combines dense vector similarity scores with sparse keyword matching (BM25) and merges the results using a reciprocal rank fusion algorithm. Pure vector search sometimes misses exact keyword matches when the embedding model does not capture a specific technical term well. Hybrid search catches both semantic similarity and exact matches, which is why it consistently outperforms either approach alone on enterprise document retrieval tasks.

How do you choose the right vector database for a production RAG system?

The key criteria are: scale (how many vectors do you need to store and query?), latency requirements (what is your acceptable p95 query time?), filtering needs (do you need metadata-based access control?), and operational preference (managed service vs. self-hosted). Start with pgvector if you already run PostgreSQL and your scale is modest. Move to Pinecone, Weaviate, or Qdrant when you need dedicated scaling, advanced filtering, or better support for billion-scale indexes.