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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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