Turbopuffer

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Turbopuffer is a fully managed cloud vector database built around a centroid-optimized SPFresh index. It is designed for extremely low-cost, large-scale storage, leveraging object storage engines like S3, GCS, or Azure Blob. If you want to compare the best vector databases for your data, try Agentset.

Rank: #1License: ProprietaryCost: low

Vector Databases Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.

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Trusted by teams building production RAG applications

5M+

Documents

1,500+

Teams

99.9%

Uptime

Deployment

BYOC, Managed Cloud

Cost

Minimum commitment $64/month

Index Types

SPFresh

Deployment

Infrastructure Options

Deployment Types

BYOC
Managed Cloud

Cloud Providers

AWS
GCP
Azure

Strengths

What Turbopuffer Does Well

Ultra-low storage costs leveraging object storage
Excellent for massive-scale deployments
BYOC option for data sovereignty
Can deploy to any region with object storage
Innovative SPFresh index design
Good for cold/warm data access patterns
Cost-effective for billions of vectors

Weaknesses

Potential Drawbacks

Cold queries can be slow (p99: 554ms)
Proprietary and vendor lock-in
Minimum $64/month commitment
Limited client library ecosystem
No self-hosted option
Documentation less mature
Newer player with smaller user base

Use Cases

When to Choose Turbopuffer

Ideal For

Very large-scale vector storage (billions+)
Cost-sensitive applications at scale
Cold storage and archival use cases
BYOC deployments requiring data sovereignty
Applications tolerant to higher latency

Not Ideal For

Applications requiring consistently low latency
Small-scale prototypes (due to $64/mo minimum)
Local development workflows
Use cases with predominantly cold queries

Build RAG in Minutes, Not Months

Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.

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import { Agentset } from "agentset";

const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");

const results = await ns.search(
  "What is multi-head attention?"
);

for (const result of results) {
  console.log(result.text);
}