Cloud Databases & Vector Stores · 2 min read

Postgres (pgvector) vs Dedicated Vector Stores: Pinecone, Qdrant, and Milvus Compared at Scale

A technical evaluation of vector retrieval latency, HNSW indexing costs, and operational complexity across 10 million high-dimensional embeddings.

Choosing between pgvector on your existing relational database and a dedicated specialized vector database is one of the most consequential architecture decisions for AI product teams.

### When pgvector is the Right Choice For 90% of production applications holding fewer than 5 million vectors, pgvector (especially with HNSW indexing and halfvec quantization) offers compelling advantages: - **Zero Data Sync Latency:** Relational metadata (user_id, organization_id, permissions) lives in the exact same table as embedding vectors, eliminating brittle dual-write pipelines. - **Transactional Consistency:** Inserting, updating, or deleting a document atomically deletes its embedding in the same ACID transaction. - **Reduced Infrastructure Overhead:** Teams already operating Postgres (via Supabase, RDS, or Neon) do not need to onboard, monitor, and secure a second database service.

### When Dedicated Vector Stores Win At 10M+ to 100M+ vector scales, dedicated engines like Pinecone, Qdrant, and Milvus demonstrate clear performance advantages: - **Cold Storage Tiering:** Storing raw vectors on disk while keeping compressed scalar quantizations in RAM reduces memory bills by up to 75%. - **Sub-10ms P99 Latency:** Specialized SIMD hardware vector kernels deliver consistent sub-10ms query times even under thousands of concurrent search requests. - **Distributed Sharding:** Effortless horizontal partitioning across multi-node clusters without complex manual Postgres tablespaces.