Do I need a dedicated vector database or can I use PostgreSQL?

PostgreSQL with the pgvector extension handles small-to-medium workloads well (up to a few million vectors). Dedicated vector databases (Pinecone, Weaviate, Qdrant) win at large scale, high query rates, and when you need advanced features like multi-tenancy and hybrid search. Most production AI agent deployments start with pgvector and migrate only if performance requires it.

Vector Database

Written by Max Zeshut

Founder at Agentmelt · Last updated May 26, 2026

A database optimized for storing and searching high-dimensional embeddings (numerical representations of text, images, or audio). AI agents use vector databases to find semantically similar content—e.g., matching a support question to the most relevant KB article even if the exact words differ. Examples include Pinecone, Weaviate, and Chroma.

Example

A legal AI agent indexes 50,000 past contracts in Pinecone. When a new vendor sends an MSA for review, the agent retrieves the 10 most similar contracts in milliseconds, identifies unusual terms by comparison, and surfaces precedent decisions on each—turning the 'have we ever seen a clause like this?' question into a 2-second lookup.

Frequently asked questions

Do I need a dedicated vector database or can I use PostgreSQL?: PostgreSQL with the pgvector extension handles small-to-medium workloads well (up to a few million vectors). Dedicated vector databases (Pinecone, Weaviate, Qdrant) win at large scale, high query rates, and when you need advanced features like multi-tenancy and hybrid search. Most production AI agent deployments start with pgvector and migrate only if performance requires it.

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