Vector Database
A specialised database that stores embeddings and efficiently finds nearest neighbours.
What is Vector Database?
A vector database is where you store and search embeddings. The key operation is "find the K most similar vectors to this query vector". Doing this efficiently across millions or billions of embeddings is computationally hard — vector databases use specialised indexes (HNSW, IVF) to make it fast.
Popular vector databases in 2026: **Pinecone** (managed, easy), **Weaviate** (open-source), **pgvector** (Postgres extension — popular when you already have Postgres), **Qdrant**, **Milvus**, **Chroma** (lightweight).
For most Indian startups, pgvector is the pragmatic choice — it adds vector search to your existing Postgres without operating a separate service. Pinecone is the easiest managed option. The other names matter mostly at very large scale.
Vector databases are core RAG infrastructure. The choice affects cost, latency, and operational complexity.
A Chennai-based EdTech indexed 100,000 educational PDFs into pgvector, costing only the Postgres they were already running. Their semantic-search-over-textbooks feature drove a 40% increase in study-time per student.
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