pgvector vs Dedicated Vector Databases: When to Use What
Compare pgvector with Milvus, Weaviate, and Qdrant. Learn when to use PostgreSQL-based vector search vs a dedicated vector database for your AI stack.
Choosing the Right Vector Database for Your AI Stack
Vector databases have become a cornerstone of modern AI applications — powering semantic search, RAG pipelines, recommendation engines, and agent memory. But with so many options available (pgvector, Milvus, Weaviate, Qdrant, Chroma), choosing the right one can be overwhelming. This article breaks down pgvector against dedicated vector databases so you can make an informed decision.
What Makes pgvector Different?
pgvector is not a separate database — it is an extension for PostgreSQL. This fundamental difference shapes everything about how you use it:
| Aspect | pgvector | Dedicated Vector DB (Milvus, Weaviate) |
|---|---|---|
| Architecture | Extension on PostgreSQL | Standalone service |
| Setup complexity | CREATE EXTENSION vector; | Deploy cluster, configure APIs |
| Data consistency | ACID transactions, foreign keys | Eventual consistency (typically) |
| Query language | SQL with vector operators | Custom REST/gRPC APIs |
| Ecosystem integration | Any PostgreSQL client/tool | Vendor-specific SDKs |
| Index types | IVFFlat, HNSW | IVF, HNSW, DiskANN, SCANN |
| Vector dimensions | Up to 16,000 | Up to 65,535 |
When to Choose pgvector
pgvector shines when your application already uses PostgreSQL. If you are building a RAG pipeline on top of an existing Postgres database, adding pgvector means zero new infrastructure. You can join vectors with user data, orders, and content in a single query. This simplicity is why over 22,000 developers on GitHub choose pgvector — it reduces operational complexity dramatically.
Best Use Cases for pgvector
- RAG on existing Postgres data: Your documents, metadata, and embeddings live in the same database with referential integrity.
- Small-to-medium scale: Up to tens of millions of vectors, pgvector performs well with proper indexing.
- Startups and MVPs: Skip the operational overhead of running a separate vector database service.
- Applications that need ACID: When vector operations must be transactional alongside relational updates.
When to Choose a Dedicated Vector Database
Dedicated vector databases like Milvus and Weaviate pull ahead at very large scale or when you need specialized vector operations they excel at:
- 100M+ vectors: Dedicated services are built for billion-scale with distributed sharding.
- Hybrid search with dense + sparse vectors: Weaviate and Qdrant offer built-in hybrid search.
- Multi-modal data: Some vector DBs handle images, audio, and text embeddings natively.
- Filtered search performance: Pre-filtering with metadata can be faster in purpose-built systems.
Quick Comparison Table
| Feature | pgvector | Milvus | Weaviate | Qdrant |
|---|---|---|---|---|
| License | PostgreSQL | Apache-2.0 | BSD-3-Clause | Apache-2.0 |
| Docker image | pgvector/pgvector:pg16 | milvusdb/milvus | semitechnologies/weaviate | qdrant/qdrant |
| Setup time | 5 minutes | 30 minutes | 15 minutes | 10 minutes |
| SQL support | Full SQL | Limited | GraphQL | REST only |
| ACID transactions | Yes | No | No | No |
The Verdict
For most AI applications — especially RAG systems, semantic search on PostgreSQL-backed apps, and projects where simplicity matters — pgvector is the right choice. It eliminates an entire service from your architecture while giving you production-grade vector search. Only reach for a dedicated vector database when you outgrow pgvector’s scale or need specialized features like distributed hybrid search.
Start with pgvector. Scale up when you need to.