PostgreSQL as a Vector Database: When to Use pgvector vs Pinecone vs Weaviate

"Should we use PostgreSQL as our vector database?" I've heard this question a lot in 2026. pgvector is everywhere. Every Postgres instance now has vector search capabilities. But is it actually better than Pinecone or Weaviate? I tested all three with 10 million vectors (1536 dimensions, OpenAI embeddings). Here's what I found. The Vector Database Landscape in 2026 Quick summary: Pinecone : Fully managed, serverless, 70% market share Weaviate : Hybrid search (vectors + BM25), open-source pgvector : PostgreSQL extension, ACID compliance The big shift in 2026: pgvector is no longer "the slow option." With pgvectorscale (Timescale's addition), PostgreSQL now delivers 471 QPS at 99% recall on 50M vectors. That's 11.4x better than Qdrant and competitive with Pinecone. Let's break this down. What Even Is a Vector Database? Vectors are just arrays of numbers that represent meaning: # Text → Vector embedding " machine learning " → [ 0.23 , - 0.41 , 0.88 , ..., 0.15 ] # 1536 dimensions " deep l