MongoDB Atlas Vector Search vs Weaviate

Side-by-side comparison of MongoDB Atlas Vector Search and Weaviate: pricing, features, API access, and community ratings.

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	MongoDB Atlas Vector Search Build intelligent applications powered by semantic search and generative AI with a native, full-featured vector database. Visit	Weaviate The open-source AI-native vector database built for production-scale search, RAG, and agents Visit
Category	Vector databases	Vector databases
Pricing	Freemium	Freemium
Starting price
Free tier
Audience	All audiences	All audiences
API available
Open source
Self-hostable
Model provider	Voyage AI (Automated Embeddings); any LLM via RAG
Founded	2007	2019
Headquarters	New York, USA	Amsterdam, Netherlands
Key strengths	·Unified operational and vector data in a single platform — no sync overhead ·Hybrid search combining vector, lexical, geospatial, and metadata filtering ·Automated Embeddings powered by Voyage AI — no ML expertise required ·Independent scaling of vector search via dedicated Search Nodes	·Billion-scale vector search with multi-tenancy support ·Built-in embeddings — no external pipeline required ·Deployment-agnostic: cloud, self-hosted, or on-prem ·Unified platform for search, RAG, agents, and memory (Engram)