Qdrant vs MongoDB Atlas Vector Search
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Qdrant takes the lead.
Both Qdrant 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 Qdrant:
- Qdrant ranks higher overall
- Qdrant has more permissive licensing
- MongoDB Atlas Vector Search has 3 more strengths
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.
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.
Feature Comparison
Infrastructure & Technical Details
| Feature | Qdrant | MongoDB Atlas Vector Search |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Managed Cloud, Self-Hosted (Enterprise), Community Edition |
| Cost | Starts ~$0.014/hour for smallest node | Free tier: M0 (512MB); Flex: $8-$30/mo; Dedicated: starts $57/mo (M10); Community: Free |
| License | Apache 2.0 | SSPL (self-managed) / Proprietary (Atlas Cloud) |
| Index Types | HNSW, Sparse (dot similarity) | Hierarchical Navigable Small World (HNSW-like), Quantized indexes |
| Cloud Providers | AWS, Azure, GCP | AWS, Azure, GCP |
| Regional Flexibility | high | high |
| Strengths | 10 | 13 |
| Weaknesses | 5 | 10 |