For developers building AI search and agent memory, Turbopuffer is a vector database built directly on object storage, delivering the low cost per query needed to run retrieval at scale.
Presence & Market Position
#35 of 103
GTM Developer Tools
▲ 94 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Turbopuffer is positioning itself as the object-storage-native retrieval layer for AI search and agent memory, prioritizing low storage cost and elastic scale. Its market presence is rising through database architecture discussion, AI-infrastructure partnerships, and technical customer evidence rather than broad GTM software coverage. Conversation touches on scaling search systems, retrieval quality, native embeddings, and the economics of serving very large document collections. Recent customer examples across legal search and cross-product enterprise search give the architecture concrete production context, while its visibility alongside other agent-infrastructure vendors reinforces its place in the emerging retrieval stack.
Deepline
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Mem0Anthropic, Clay, Cursor, Notion, Atlassian, Ramp, Linear, Cognition, Harvey, Grammarly
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Business Profile
Founded in 2023, Turbopuffer is an independent, 36-person database company with $100M in reported 2026 ARR. It built its business around separating durable storage from stateless compute, using object storage as the source of truth and caching frequently queried data on SSD and memory. That architecture supports a commercial service aimed at large AI-search workloads, including multi-tenant, single-tenant, and customer-cloud deployments. The company remains focused on first-stage retrieval while expanding into full-text search, hybrid search, branching, encryption, and native embeddings.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Turbopuffer exposes its retrieval layer through a documented HTTP API and client SDKs, giving agents programmatic access to vector, full-text, hybrid, filtered, and sparse search. Namespaces provide the durable memory boundary, while copy-on-write branching and customer-managed encryption support isolated agent workloads. Native embeddings reduce a separate model call during reads and writes. No public MCP or CLI surface is on file.
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