Vector Databases Compared: Pinecone vs Weaviate vs pgvector

The database landscape for 2026 is fragmented. Here is how to choose.

The Three Categories

1. The Native Managed Services (Pinecone, Zilliz)

Best For: Teams who want Serverless and dont care about the underlying infrastructure.
Pros: Instant setup, massive scalability, great SLAs.
Cons: Cost at scale. Data leaves your VPC (usually).
Winner: Pinecone (Serverless mode is a game changer).

2. The Open Source Natives (Weaviate, Qdrant, Chroma)

Best For: Teams who want control, hybrid search, and fancy filtering.
Pros: Docker deployable, rich query languages (Weaviates GraphQL is powerful), runs locally for dev.
Cons: You manage the cluster (or pay them to do it).
Winner: Weaviate (Best hybrid search), Qdrant (Fastest Rust engine).

3. The Just a Feature Extensions (pgvector, Elasticsearch, Redis)

Best For: Startups who already have Postgres and dont want another infrastructure piece.
Pros: ACID transactions, joins with relational data, zero new ops overhead.
Cons: Slower at 10M+ vectors. Missing advanced features (like multi-tenancy optimization).
Winner: pgvector (If you have 1M vectors, just use this).

The Decision Matrix

Constraint	Recommendation
Speed to MVP	pgvector (Supabase/Neon)
Scale (100M+ vectors)	Pinecone or Qdrant
Complex Metadata Filtering	Weaviate
Local-First / Privacy	Chroma (Runs in-memory)

Conclusion

Dont over-engineer. Start with pgvector. Move to a dedicated vector DB only when you hit performance limits.

Next: [Scaling Vector Search](/blog/scaling-vector-search-production)

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