Turbopuffer vs Qdrant

Compare deployment options, cost efficiency, and features to choose the right vector database for your application. If you want to compare these models on your data, try Agentset.

Database Comparison

Turbopuffer takes the lead.

Both Turbopuffer and Qdrant are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.

Why Turbopuffer:

  • Turbopuffer ranks higher overall
  • Qdrant offers more deployment options
  • Turbopuffer is more cost-effective
  • Qdrant has more permissive licensing
  • Qdrant has 3 more strengths

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Turbopuffer

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.

Deployment: BYOC, Managed Cloud
Cost: Minimum commitment $64/month
License: Proprietary
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Qdrant

Qdrant is an open-source vector database available as both a managed cloud service and a self-hosted solution. It offers strong HNSW performance, flexible deployment, and predictable cost structures, making it suitable for both startups and large-scale RAG workloads.

Deployment: Self-Hosted, Managed Cloud
Cost: Starts ~$0.014/hour for smallest node
License: Apache 2.0
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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.

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);
}

Feature Comparison

Infrastructure & Technical Details

FeatureTurbopufferQdrant
DeploymentBYOC, Managed CloudSelf-Hosted, Managed Cloud
CostMinimum commitment $64/monthStarts ~$0.014/hour for smallest node
LicenseProprietaryApache 2.0
Index TypesSPFreshHNSW, Sparse (dot similarity)
Cloud ProvidersAWS, GCP, AzureAWS, Azure, GCP
Regional Flexibilityhighhigh
Strengths710
Weaknesses75