MongoDB Atlas Vector Search vs Weaviate
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
MongoDB Atlas Vector Search takes the lead.
Both MongoDB Atlas Vector Search and Weaviate are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why MongoDB Atlas Vector Search:
- MongoDB Atlas Vector Search ranks higher overall
- MongoDB Atlas Vector Search offers more deployment options
- Weaviate has more permissive licensing
- MongoDB Atlas Vector Search has 5 more strengths
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.
Weaviate
Weaviate is a cloud and self-hosted vector database offering hybrid dense+sparse search, strong metadata filtering, and a modular storage layer. It is designed for enterprise and production RAG workloads that require flexibility and scalable cloud hosting.
Feature Comparison
Infrastructure & Technical Details
| Feature | MongoDB Atlas Vector Search | Weaviate |
|---|---|---|
| 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 | Free (self-host), cloud starts ~$25/mo |
| License | SSPL (self-managed) / Proprietary (Atlas Cloud) | BSD |
| Index Types | Hierarchical Navigable Small World (HNSW-like), Quantized indexes | HNSW, Hybrid dense+sparse |
| Cloud Providers | AWS, Azure, GCP | AWS, GCP |
| Regional Flexibility | high | low |
| Strengths | 13 | 8 |
| Weaknesses | 10 | 6 |