MongoDB Atlas Vector Search vs turbopuffer

Side-by-side comparison of MongoDB Atlas Vector Search and turbopuffer: 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	turbopuffer Fast vector and full-text search engine built on object storage — 10x cheaper and infinitely scalable Visit
Category	Vector databases	Vector databases
Pricing	Freemium	Freemium
Starting price
Free tier
Audience	All audiences	Technical
API available
Open source
Self-hostable
Model provider	Voyage AI (Automated Embeddings); any LLM via RAG
Founded	2007	2023
Headquarters	New York, USA	San Francisco, USA
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	·10x cheaper than traditional vector databases due to object storage architecture ·Sub-10ms p50 latency with Memory/SSD caching layer ·Massive scale: 4T+ documents, 10M+ writes/s, 25k+ queries/s in production ·Hybrid search combining vector (ANN) and full-text (BM25) in one system