Master Turbopuffer with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Turbopuffer powerful for ai memory & search workflows.
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.
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.
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.
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.
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.
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.
Searching for semantically similar documents but filtering to only return results from the last 30 days and a specific content category.
Turbopuffer stores all data on object storage (like S3) instead of keeping vectors in RAM or on SSDs. Object storage costs ~$0.02/GB/month vs $3-10/GB/month for memory. Intelligent caching keeps frequently accessed data fast (sub-10ms), while rarely accessed data stays on cheap storage. You pay for actual storage and queries rather than provisioned capacity.
Warm namespaces (recently accessed) benefit from caching and serve queries at sub-10ms p50 latency. Cold namespaces (not recently accessed) need to load data from object storage first, resulting in ~343ms p50 latency. After the first query, a cold namespace becomes warm. The system automatically manages caching — no manual warm-up needed.
Turbopuffer is dramatically cheaper at scale (10x+) due to its object storage architecture. Pinecone keeps vectors in memory, delivering consistently low latency but at much higher cost. Turbopuffer matches Pinecone's latency for warm queries but has higher latency for cold data. Turbopuffer also includes native full-text search, which Pinecone doesn't offer. Choose Pinecone for consistent low-latency at any scale; turbopuffer for cost efficiency at scale.
Yes, turbopuffer is well-suited for RAG pipelines. It supports vector search, BM25 full-text search, and hybrid search — all important for retrieval quality. The main consideration is cold namespace latency: if your RAG application accesses many different data sources infrequently, cold start latency (~343ms) adds to response time. For applications with consistent data access patterns, warm namespace latency is excellent.
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Tutorial updated March 2026