Turbopuffer - Serverless Vector Database

Basic Information

• Product Name: Turbopuffer
• Company/Organization: Turbopuffer Inc.
• Country/Region: USA
• Official Website: https://turbopuffer.com/
• Type: Commercial serverless vector search engine
• License: Proprietary (not open source)
• Features: Built on object storage (S3, etc.)

Product Description

Turbopuffer is a serverless search engine built on object storage, offering highly cost-effective vector and full-text search services. By storing data on inexpensive object storage and utilizing intelligent caching and indexing techniques, it achieves low latency (p50 < 10ms) and low-cost large-scale vector search. Turbopuffer has been used by renowned products like Cursor and Notion, handling over 2.5 trillion documents.

Core Features/Characteristics

• Object Storage Architecture: Built on S3 and other object storage, extremely low cost
• Serverless: Auto-scaling, pay-as-you-go pricing
• Hybrid Search: BM25 full-text search + vector similarity search + ranking fusion
• Metadata Filtering: Filtering and searching on arbitrary attributes
• Large-Scale Multi-Tenancy: Supports millions of namespaces (Notion runs 10M+ namespaces)
• Low Latency: p50 latency under 10ms
• Massive Scale: Handled 2.5T+ documents, 10M+ writes/sec, 10K+ queries/sec
• Compliance Features: HIPAA BAA, SSO, and other enterprise-grade compliance

Business Model

SaaS with pay-as-you-go pricing.

Pricing (2026)

• Launch: $64/month, includes all database features
• Scale: $256/month, adds HIPAA BAA, SSO, priority support
• Enterprise: $4,096+/month, single-tenant, BYOC, CMEK, 99.95% SLA
• Typical Cost: 1536-dim vectors, 1M reads/writes, 10 namespaces < $10/month
• Query prices reduced by up to 94%

Notable Customers

• Cursor: Scaled code retrieval to 100B+ vectors
• Notion: 10M+ namespaces, 10B+ vectors
• Multiple renowned AI products

Target Users

• AI applications requiring cost-effective vector search
• Large-scale multi-tenant SaaS products
• Cost-sensitive startups
• Product teams needing rapid scaling

Advantages

• Extremely low cost (an order of magnitude lower than most managed solutions)
• Auto-scaling, no capacity planning required
• Low latency, proven at scale
• Supports both vector and full-text search
• Backed by notable customers (Cursor, Notion)

Limitations

• Not open source, vendor lock-in risk
• Cloud-only service, no on-premise deployment
• Data stored in the cloud, limited privacy control
• Relatively new, ecosystem integration not as mature as established solutions
• Object storage architecture may have higher latency than in-memory databases in some scenarios

Relationship with OpenClaw

Turbopuffer can serve as a cloud-based vector storage backend for OpenClaw, especially suitable for cost-sensitive scenarios requiring large-scale search. However, due to the lack of on-premise deployment, it conflicts somewhat with OpenClaw's privacy-first principle. It is more suitable for OpenClaw's cloud/enterprise deployment scenarios.

Competitor Comparison