#12 in DatabasesUpdated 2026-08

Qdrant

by Qdrant · Open-source vector search engine written in Rust

66.3 — BenchRank score out of 100

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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 from Qdrant’s own pricing page, in USD. Usage charges and add-ons may apply on top.

Qdrant pricing tiers, monthly rates in USD
Plan	Monthly	Includes
Free Tier	—	Single node cluster, 0.5 vCPU / 1GB RAM / 4GB disk Free cloud inference with selected models Community support, best-effort response For testing and prototypes
Standard Tier	—usage-based	Dedicated resources, vertical and horizontal scaling Backup and disaster recovery, highly available setups 99.5% uptime SLA, standard support 10x5 Free tokens for paid inference models
Premium Tier	Contact sales	Minimum spend required SSO and private VPC links 99.9% uptime SLA (99.95% multi-AZ) Premium support, 24x7

How this score is made up

Each dimension is scored out of 100 and combined into the headline score using fixed weights.

MCP support: 92 out of 100
API quality: 20 out of 100
Documentation: 90 out of 100
Agent friendliness: 83 out of 100
Pricing transparency: 35 out of 100
Changelog: 100 out of 100
Marketing site structure: 75 out of 100
Operational trust: 35 out of 100

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: 85 out of 100
Maintenance: 100 out of 100

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