Chroma vs MongoDB Atlas Vector Search

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

AI comparison summary

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	Chroma Open-source search infrastructure for AI — vector, full-text, regex, and metadata search at scale Visit	MongoDB Atlas Vector Search Build intelligent applications powered by semantic search and generative AI with a native, full-featured vector database. 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
Headquarters		New York, USA
Key strengths	·Multi-modal search: vector, full-text (BM25/SPLADE), regex, and metadata in one system ·Built on object storage (S3/GCS) — up to 10x cheaper than memory-based alternatives ·Serverless and zero-ops — auto-scales with no manual tuning required ·Apache 2.0 open-source with 27k GitHub stars and 15M+ monthly downloads	·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