pgvector vs Qdrant — Choosing a Vector Store for Production

A comparison of two vector storage approaches — pgvector for tight Postgres integration versus Qdrant for dedicated vector performance.

Choosing a vector store is a tradeoff between operational simplicity and raw search performance. Here's how I think about pgvector vs Qdrant.

pgvector pros

No extra service to manage. Transactions across relational and vector data. Good enough performance for most workloads with IVFFlat indexes.

Qdrant pros

Dedicated vector engine with better recall and lower latency at scale. Built-in filtering, payload indexing, and multi-tenancy. Worth the operational overhead when vector search is your primary access pattern.

My rule of thumb

Start with pgvector. Move to Qdrant only when you need sub-10ms recall at 1M+ vectors or complex filtering during search.