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PG Vector

PG Vector is a popular Postgres extension that adds vector search capabilities directly inside a traditional relational database. It is ideal for teams that want to keep embeddings, metadata, and application data in one system without operating a separate vector database.

Rank: #5License: PostgreSQLCost: low

Deployment

Self-Hosted, Managed Cloud

Cost

Free (extension), infra cost only

Index Types

Flat, IVFFlat, HNSW

Deployment

Infrastructure Options

Deployment Types

  • Self-Hosted
  • Managed Cloud

Cloud Providers

  • AWS RDS
  • Azure Postgres
  • GCP AlloyDB
  • Supabase
  • Timescale

Strengths

What PG Vector Does Well

  • No separate vector database to manage
  • Unified data layer (vectors + metadata + relational data)
  • Leverage existing Postgres infrastructure and expertise
  • ACID transactions with vectors and relational data
  • Works with all Postgres tooling and ecosystem
  • Free and open-source extension
  • Excellent for hybrid SQL + vector queries
  • Mature backup, replication, and monitoring tools
  • Easy integration with existing Postgres apps

Weaknesses

Potential Drawbacks

  • Performance limited by Postgres constraints
  • Not optimized for billion+ vector scale
  • HNSW index can be memory-intensive
  • Slower than specialized vector databases at scale
  • Limited to 2,000 dimensions (vector), 4,000 (halfvec)
  • Vector operations can impact transactional workload
  • Index building can be slow for large datasets

Use Cases

When to Choose PG Vector

Ideal For

  • Applications already using PostgreSQL
  • Teams wanting unified data layer
  • Projects needing transactional consistency with vectors
  • Small to medium vector datasets (millions)
  • Applications requiring complex SQL joins with vectors
  • Cost-conscious projects with existing Postgres
  • Rapid prototyping with familiar tools

Not Ideal For

  • Applications needing billions of vectors
  • Very high-performance vector search requirements
  • Greenfield projects not tied to Postgres
  • Applications needing specialized vector features
  • High-throughput vector-only workloads

Compare Databases

See how it stacks up

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