Qdrant
Qdrant: A flexible vector database for teams building hybrid search and AI retrieval. Ranked #7 of 27 in Vector Databases by our editors (8.7/10); pricing: Free plan; best for modern teams wanting open hybrid search.
At a glance
- Editor score8.7 / 10
- PricingFree plan
- Best forModern teams wanting open hybrid search
- Free planYes
- DeploymentBoth
- Founded2021 · Berlin, Germany
- Facts checked22 Sep 2026
Where it wins
- Hybrid dense and sparse search with metadata filtering
- Open-source self-hosting plus managed cloud deployment
- Multivectors, named vectors, REST, and gRPC APIs
Where it doesn't
- The Free Tier is limited to a single node
- Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
- Paid cloud tiers use usage-based pricing rather than fixed plan amounts
Our verdict on Qdrant
Qdrant is an open-source vector search engine and vector database for developers building semantic search, recommendations, retrieval-augmented generation, and other AI applications. It supports self-hosted deployment and Qdrant Cloud, giving teams a choice between managing the software themselves and using a managed service. Its fit is strongest for teams that need dense, sparse, and multivector retrieval alongside payload metadata and hybrid semantic-and-lexical search.
Pricing separates development access from production capabilities. The Free Tier costs $0 and is available free forever, with a single-node cluster and 0.5 vCPU, 1 GB RAM, and 4 GB disk. The Standard Tier adds dedicated resources, vertical and horizontal scaling, backup and disaster recovery, and a 99.5% uptime SLA. Premium adds SSO, private VPC links, a 99.9% uptime SLA, and extra support. The paid cloud tiers use usage-based pricing, while the free tier provides a practical starting point for development and evaluation.
Qdrant’s feature depth is a major reason to consider it for retrieval systems that need more than basic nearest-neighbor search. HNSW supports dense similarity search; sparse-vector indexing supports exact sparse retrieval; and hybrid search combines dense and sparse representations. Payload indexes, full-text indexing, named vectors, multivectors, late-interaction retrieval, REST and gRPC APIs, and cloud-hosted embedding inference broaden its implementation options. Teams seeking a single-node free environment or open-source self-hosting should find a clear fit. Teams requiring higher availability, private networking, or additional support should look at the paid tiers.
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Qdrant pricing
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Qdrant fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Not verified |
| Deployment | Both |
| Hybrid search | Yes |
| Metadata filtering | Yes |
| Sparse vectors | Yes |
| Maximum dimensions | 65535 dimensions |
| Index types | HNSW; sparse vector index; payload index; filterable HNSW |
| SDK languages | Python, JavaScript/TypeScript, Rust, Go, .NET, Java |
| Deployment | Cloud, Self-hosted |
| Platforms | Web, Windows, macOS, Linux |
| Compliance & security | SOC 2, HIPAA, GDPR |
| Support | Email, Tickets, Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan (source) |
| Website | qdrant.tech |
| Facts checked | 22 Sep 2026 |
Alternatives to Qdrant
- ElasticsearchA broad hybrid-search platform for teams adding vectors to mature analytics stacks.9.3
- Vertex AI Vector SearchA managed Google Cloud option for teams building dense, sparse, and hybrid search.9.2
- VikingDBA cloud vector database with dense, sparse, hybrid, filtered, and multimodal retrieval.9.1
Qdrant vs the competition
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Also Ranked In
Qdrant is ranked in 7 of our directories.
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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