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Vector database comparison: pgvector, Pinecone, Qdrant, Weaviate
How to choose a vector database in 2026 — pgvector vs Pinecone vs Qdrant vs Weaviate, with honest trade-offs for production AI apps.
vector databaseRAGAI infrastructure
The default answer in 2026: pgvector
If you already have Postgres, start with pgvector. One database, transactions, joins to your business data, and great enough recall up to tens of millions of vectors.
When to leave Postgres
- Recall and latency start to suffer past your scale threshold
- You need advanced filtering across high-cardinality metadata at speed
- Hybrid (keyword + vector) search is a first-class need
Pinecone
- Managed, fast, easy to operate, expensive at scale
- Pick it when you want zero ops and your data is happy outside your DB
Qdrant
- Open-source, self-hostable, strong filtering and payload search
- Pick it when you want control and good ergonomics without paying for managed
Weaviate
- Open-source, modular, great hybrid search and built-in modules
- Pick it when hybrid retrieval and a schema-first approach matter
What to evaluate beyond features
- p95 latency under your real query mix, not their benchmark
- Cost at 10x your current vector count
- How easy it is to re-embed when you change models
- Backup, snapshots, and disaster recovery story
