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turbopuffer

by turbopuffer

Serverless vector database storing vectors in object storage with sub-second query latency — cost-efficient for APAC teams with large vector collections that need infrequent search without managing dedicated vector database infrastructure.

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AIMenta verdict
Decent fit
4/5

"Serverless vector database — APAC teams use turbopuffer for cost-efficient large-scale vector search that stores APAC vectors in object storage and queries them with sub-second latency without managing vector database infrastructure."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Object storage backend: APAC vectors stored in S3 at storage cost, not compute cost
  • Pay-per-query: no idle APAC infrastructure cost between query bursts
  • Sub-second latency: on-demand partition prefetch for APAC approximate search
  • Namespace isolation: per-APAC-tenant vector collections with individual filtering
  • Simple REST API: upsert, query, delete for APAC vector operations
  • Billion-scale: cost-efficient for large APAC collections with moderate QPS
When to reach for it

Best for

  • APAC teams building large-scale vector search applications with moderate or bursty query patterns — where the cost of always-on dedicated vector database infrastructure exceeds the actual APAC query compute cost.
Don't get burned

Limitations to know

  • ! Not suitable for APAC applications requiring sustained thousands-of-QPS — object storage I/O has latency floor
  • ! Relatively new service — APAC teams should verify SLA and enterprise support maturity
  • ! No self-hosted option — APAC data sovereignty requirements may conflict with cloud-only model
Context

About turbopuffer

turbopuffer is a serverless vector database that stores vectors in cloud object storage (S3-compatible) rather than on dedicated compute — charging only for queries made, not for storage time. APAC teams use turbopuffer for large-scale vector collections (millions to billions of APAC embeddings) where dedicated vector database infrastructure would be cost-prohibitive for the actual query volume.

The turbopuffer architecture separates storage from compute: APAC vectors are stored in compressed columnar format in object storage at near-S3 pricing ($0.023/GB/month), and query compute spins up on-demand — achieving sub-second approximate nearest-neighbor search latency by prefetching relevant APAC index partitions into memory based on the query. APAC teams with intermittent search patterns (batch queries, not continuous high-QPS) benefit most from this architecture.

For APAC teams building large-scale RAG applications where the vector collection is large but query rates are moderate (hundreds of QPS rather than thousands), turbopuffer's per-query pricing avoids the cost of always-on dedicated Pinecone pods or self-hosted vector database clusters that must be sized for peak APAC load regardless of actual usage.

turbopuffer's namespace model allows APAC teams to maintain separate vector collections per customer or per document corpus — multi-tenant APAC applications can isolate tenant vectors in separate namespaces with individual filtering without cross-tenant data access. The REST API is minimal: upsert vectors, query by nearest neighbors with optional APAC metadata filter, delete by ID.

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