Qdrant
by Qdrant · Open-source vector search engine written in Rust
BenchRank score
Screenshots of Qdrant
Homepage
Overview
Qdrant is an open-source vector search engine written in Rust that stores vectors alongside JSON metadata and serves similarity queries over REST, gRPC or official clients. It supports dense and sparse vectors in one query, multiple vectors per object, filtering during HNSW traversal, and reranking. It runs self-hosted, on Qdrant Cloud, hybrid or edge.
- Best for
- Teams building AI retrieval or RAG that need self-hosted or managed vector search at scale
- Pricing
- No figures are published: Qdrant Cloud has a free forever tier (0.5 vCPU / 1GB RAM / 4GB disk), a usage-based Standard tier billed hourly on compute, memory, storage and inference tokens, and a Premium tier requiring a minimum spend.
Strengths and trade-offs
Strengths
- Open source, or managed on AWS, Azure or GCP
- Hybrid dense-sparse search with BM25, SPLADE++ and miniCOIL
- Filters applied during HNSW traversal, not pre- or post-filtering
- REST, gRPC and official Python/JavaScript clients, plus a web UI
Trade-offs
- Free tier is one node capped at 0.5 vCPU, 1GB RAM and 4GB disk
- SSO and private VPC links need the minimum-spend Premium tier
- No prices published; Standard tier billed on actual resource usage
- Free and OSS support is community-only, best-effort response
- Qdrant Edge is still in beta
Pricing
Published plans and prices from Qdrant’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Qdrant pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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