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. If you want to compare the best vector databases for your data, try Agentset.
Vector Databases Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.
Trusted by teams building production RAG applications
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
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Databases
See how it stacks up
Compare PG Vector with other vector databases to understand the differences in deployment options, cost, and features.
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