MongoDB Atlas Vector Search vs Qdrant
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
Qdrant takes the lead.
Both MongoDB Atlas Vector Search and Qdrant are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Qdrant:
- Qdrant ranks higher overall
- Qdrant has more permissive licensing
- MongoDB Atlas Vector Search has 3 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
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.
Qdrant
Qdrant is an open-source vector database available as both a managed cloud service and a self-hosted solution. It offers strong HNSW performance, flexible deployment, and predictable cost structures, making it suitable for both startups and large-scale RAG 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);
}Feature Comparison
Infrastructure & Technical Details
| Feature | MongoDB Atlas Vector Search | Qdrant |
|---|---|---|
| Deployment | Managed Cloud, Self-Hosted (Enterprise), Community Edition | Self-Hosted, Managed Cloud |
| Cost | Free tier: M0 (512MB); Flex: $8-$30/mo; Dedicated: starts $57/mo (M10); Community: Free | Starts ~$0.014/hour for smallest node |
| License | SSPL (self-managed) / Proprietary (Atlas Cloud) | Apache 2.0 |
| Index Types | Hierarchical Navigable Small World (HNSW-like), Quantized indexes | HNSW, Sparse (dot similarity) |
| Cloud Providers | AWS, Azure, GCP | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 13 | 10 |
| Weaknesses | 10 | 5 |