PG Vector vs MongoDB Atlas Vector Search
Compare deployment options, cost efficiency, and features to choose the right vector database for your application. If you want to compare these models on your data, try Agentset.
Database Comparison
PG Vector takes the lead.
Both PG Vector and MongoDB Atlas Vector Search are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why PG Vector:
- MongoDB Atlas Vector Search offers more deployment options
- PG Vector is more cost-effective
- PG Vector has more permissive licensing
- MongoDB Atlas Vector Search has 4 more strengths
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
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.
MongoDB Atlas Vector Search
MongoDB Atlas Vector Search is a native vector database capability built directly into MongoDB Atlas, eliminating data synchronization between operational and vector databases. It enables hybrid queries combining vector similarity with MongoDB's powerful document model, metadata filtering, aggregation pipelines, and geospatial search—all within a single unified platform.
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);
}Feature Comparison
Infrastructure & Technical Details
| Feature | PG Vector | MongoDB Atlas Vector Search |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Managed Cloud, Self-Hosted (Enterprise), Community Edition |
| Cost | Free (extension), infra cost only | Free tier: M0 (512MB); Flex: $8-$30/mo; Dedicated: starts $57/mo (M10); Community: Free |
| License | PostgreSQL | SSPL (self-managed) / Proprietary (Atlas Cloud) |
| Index Types | Flat, IVFFlat, HNSW | Hierarchical Navigable Small World (HNSW-like), Quantized indexes |
| Cloud Providers | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 9 | 13 |
| Weaknesses | 7 | 10 |