turbopuffer

Vector and full-text search database built on object storage — fast, scalable, and 10x cheaper than alternatives.

Verified API available
Quick facts
What is it Vector and full-text search database built on object storage — fast, scalable, and 10x cheaper than alternatives.
Pricing Paid — from $16/mo
Free tier No
Platform API
API Yes
Best for semantic search for AI applications, recommendation systems
Domain registered 2023

Data updated Aug. 1, 2026

What does turbopuffer do?

Turbopuffer is a search database that handles both vector and full-text search, but it doesn't run on the usual expensive infrastructure. Instead, it's built directly on top of object storage like S3. That means it can store and query billions of vectors without the high costs of traditional vector databases. The company claims it's about 10x cheaper than alternatives, and the numbers back it up — they're handling over 4 trillion documents and 10 million writes per second in production for companies like Cursor, Notion, and Anthropic.

How does it work? You upload your data (vectors, text, metadata) into a namespace, then query it using vector similarity, BM25 full-text search, or a hybrid of both. Turbopuffer automatically scales, uses a memory/SSD cache for fast queries, and stores everything durably on object storage. The latency is low — sub-10ms p50 for warm queries — and you can filter by metadata, pin namespaces for speed, and even branch namespaces for experimentation. The pricing starts at $16/month for a launch plan, which is surprisingly affordable for a production-grade search database.

Who is this for? Developers building AI applications that need semantic search, recommendation systems, or retrieval-augmented generation (RAG). It's also great for any app that needs fast, scalable full-text search without the complexity of managing a cluster. If you're tired of paying huge bills for vector databases or dealing with infrastructure headaches, turbopuffer is worth a serious look.

Key features

What makes it stand out
01
Vector search with approximate nearest neighbor (ANN) for AI embeddings
02
Full-text search using BM25, with hybrid search combining both
03
Built on object storage (S3) for cost-effective, unlimited scaling
04
Automatic scaling with sub-10ms p50 latency for warm queries
05
Metadata filtering, namespace branching, and pinned namespaces for performance

Who is turbopuffer for?

Who benefits most from this tool
semantic search for AI applications
recommendation systems
full-text search for docs, issues, and knowledge bases

Pricing

launch

$16.0/month
  • Includes all database features

Trust & presence

Domain Domain registered 2023

Gallery

Click any image to enlarge

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