Turbopuffer
Object-store-backed serverless vector DB. Pay-per-query, cheap at idle, fast at scale.
VISIT TURBOPUFFERQuick facts
- CategoryManaged SaaS
- EngineRust
- PricingPaid
- LicenseProprietary
- Created2024
- GitHub starsclosed
- Hybrid searchNative
- Edge-readyYes
- Multi-tenantNative
- Max dimensions20,000
What it is
Turbopuffer is a newer serverless vector database that stores vectors in object storage (S3-class). Pay-per-query rather than always-on cluster cost — kindest pricing model for sparse-traffic workloads. Used by some serious AI products in production despite being ~2 years old.
Best for
- Sparse-traffic RAG workloads where always-on cluster cost is the constraint
- Apps with bursty query patterns
- Teams that want managed vector without Pinecone-tier pricing
When not to pick it
Skip Turbopuffer for very steady high-throughput workloads — at sustained traffic the per-query model loses to a dedicated cluster. Skip if you want the breadth of Pinecone's feature surface.
My take
Turbopuffer is the most interesting newer entrant. Object-store-backed is the right architecture for sparse-traffic AI; the bet is whether the team can stay independent or get acquired.
Links
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If Turbopuffer is your pick — the next conversation is short
The 30-min call is where your vector-DB choice becomes a real RAG architecture, a chunking + reranking strategy that actually works for your corpus, and a price range you can take to your stakeholders. Describe your data shape, your query patterns, your latency budget. I tell you whether Turbopuffer is genuinely your fit.