Turbopuffer is a serverless vector and full-text search engine built on object storage that delivers 10x cheaper similarity search at scale with sub-10ms latency for warm queries.
A serverless vector database built on object storage that's 10x cheaper than alternatives — fast search across billions of documents with no infrastructure to manage.
Turbopuffer is a serverless search engine that takes a fundamentally different architectural approach to vector databases: it's built from the ground up on object storage (like S3) rather than RAM or local SSDs. This design choice enables dramatic cost reduction — up to 10x cheaper than traditional vector databases — while maintaining fast query performance for warm namespaces.
The object storage-first architecture means turbopuffer's costs scale with data stored rather than memory provisioned. Traditional vector databases keep vectors in RAM or across SSD clusters, which becomes prohibitively expensive at scale. Turbopuffer stores data on cheap object storage and uses intelligent caching to serve frequently accessed namespaces with sub-10ms p50 latency. Cold namespaces (data not recently accessed) have higher latency (~343ms p50) but cost almost nothing to store.
Beyond vector search, turbopuffer provides BM25 full-text search and hybrid search that combines vector similarity with keyword matching. The full-text search engine was written from scratch for the object storage architecture, supporting configurable tokenization, language-specific analyzers, and efficient filtering. Hybrid search lets applications combine semantic relevance (vectors) with exact keyword matching (BM25) for more accurate results.
The platform handles massive scale in production: 2.5 trillion+ documents, 10 million+ writes per second, and 10,000+ queries per second. Namespaces can hold up to 500 million documents at 2TB each, with unlimited total namespaces. This makes it suitable for multi-tenant SaaS applications where each customer gets their own namespace.
Turbopuffer uses a namespace-based multi-tenancy model that maps cleanly to application architectures. Each namespace is independently queryable, automatically scaled, and isolated. The serverless model means there's no capacity planning, no cluster management, and no infrastructure to provision — you write data and query it.
The pricing is usage-based with a $64/month minimum commitment. At standard workloads (1536-dimension vectors, 1M reads, 1M writes, 10 namespaces), costs come in under $10/month of actual usage. SOC2 compliance, GDPR-ready DPA, and HIPAA-ready BAA are available across plans, with Enterprise adding single-tenancy, BYOC, private networking, and SSO.
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Built from the ground up on object storage rather than RAM or SSDs, enabling 10x lower costs than traditional vector databases while maintaining fast performance through intelligent caching.
Use Case:
Storing 100 million embeddings for a RAG application at a fraction of the cost of Pinecone or Weaviate by leveraging cheap object storage instead of provisioned memory.
Native BM25 full-text search engine written from scratch for the object storage architecture, supporting configurable tokenization, language-specific analyzers, and efficient metadata filtering.
Use Case:
Searching through product documentation using keyword queries with stemming and stop-word removal, returning results ranked by BM25 relevance scoring.
Combine vector similarity search with BM25 full-text search using multi-queries and client-side result fusion (e.g., reciprocal rank fusion) for more accurate retrieval.
Use Case:
Building a RAG pipeline that combines semantic embedding search with exact keyword matching to catch both conceptually relevant and terminologically precise results.
Unlimited namespaces that are independently queryable, automatically scaled, and isolated. Each namespace can hold up to 500M documents at 2TB, with no global document limit.
Use Case:
Running a multi-tenant SaaS application where each of 100,000 customers has their own isolated search namespace with automatic scaling.
Proven in production at 2.5T+ documents, 10M+ writes/s, and 10k+ queries/s globally. Sub-10ms p50 latency for warm namespaces with automatic cache management.
Use Case:
Powering real-time semantic search for a consumer application serving millions of concurrent users across billions of documents.
Filter vector and full-text search results by metadata attributes with support for complex filter expressions, enabling precise result narrowing without separate database queries.
Use Case:
Searching for semantically similar documents but filtering to only return results from the last 30 days and a specific content category.
$64.00/month
month
Higher minimum commitment with enhanced support
Custom pricing with SLA guarantees
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In 2025-2026, turbopuffer reduced query prices by up to 94%, dramatically lowering costs for high-query workloads. The platform surpassed 2.5 trillion stored documents in production. New features include customer-managed encryption keys (CMEK) per namespace, private networking for enterprise deployments, and configurable tokenization for full-text search. The pricing calculator on turbopuffer.com now shows transparent per-operation costs for storage, reads, and writes.
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