pgvector vs Weaviate
Both are alternatives to Pinecone. Here's how they stack up — verified facts, no spin.
Also searched as Weaviate vs pgvector — same comparison, one verdict.
pgvector
Vector search inside the Postgres you already run — zero new infrastructure.
pgvector is an open-source extension (PostgreSQL license) that adds vector similarity search to PostgreSQL itself. If your app already has Postgres, this is the no-new-moving-parts answer: embeddings live next to your relational data, joins and filters are just SQL, and every major managed Postgres (RDS, Cloud SQL, Azure, Supabase, Neon) supports it. Recent releases added parallel HNSW index builds, iterative scans for filtered queries, and halfvec quantization — it now handles serious workloads, not just prototypes.
Weaviate
Open-source with built-in hybrid search and a module ecosystem.
Weaviate is an open-source (BSD-3-Clause), cloud-native vector database that stores objects and vectors together, pairing similarity search with structured filtering and strong built-in hybrid (keyword + vector) search. Its module system can handle embedding generation for you, and Weaviate Cloud offers the managed path. A polished middle ground between Qdrant's lean engine and Milvus's heavy distribution.
Side by side
| pgvector | Weaviate | |
|---|---|---|
| Sovereignty Score | 94 | 87 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | No |
| License | PostgreSQL | BSD-3-Clause |
| Pricing | Free — an extension of PostgreSQL; runs wherever your Postgres runs | Free self-hosted (BSD-3-Clause); Weaviate Cloud managed tiers |
pgvector edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
pgvector
Strengths
- +No new database to operate — it's your existing Postgres
- +Vectors join directly with relational data in SQL
- +Supported by every major managed Postgres provider
- +HNSW indexes, filtered iterative scans, quantization
Trade-offs
- −Very large collections (hundreds of millions of vectors) favor a dedicated engine
- −Tuning happens in Postgres terms — indexes, memory, vacuum
Weaviate
Strengths
- +Hybrid keyword + vector search built in
- +Modules can generate embeddings server-side
- +GraphQL and REST APIs; solid multi-tenancy
Trade-offs
- −Heavier than Qdrant for simple use cases
- −Module system adds a learning curve
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Facts verified 2026-07-15. Licenses and pricing change — spotted something out of date? That's a correction we want.