Qdrant
The open source vector database in Rust for semantic search at scale.
Qdrant is an open source vector database written in Rust: it indexes embeddings and serves low-latency similarity search, even across millions of vectors. Payload filtering combines semantics and business criteria in one query, and hybrid search (dense + sparse) sharpens relevance. Managed cloud or self-hosted, it's our choice when a RAG pipeline has to handle real load or stay on your own infrastructure.
What Qdrant brings to your project.
Typical use cases: Production RAG, semantic search, recommendation, deduplication.
- 01
Written in Rust: performance and low memory footprint at scale.
- 02
Payload filtering: semantics + business criteria in one query.
- 03
Hybrid dense + sparse search, quantization and multi-tenancy.
- 04
Managed cloud or self-hosted (Docker, Kubernetes): sovereignty possible.
Entrust your project
to our experts.
Entrust your project to our
experts.
Entrust your project
to our experts.
Our experts build your project, delivering superior technical and functional quality within shorter timeframes.







They trust us.
They trust
us.
Startups, mid-caps, large enterprises, public sector: Kosmos supports organisations of every size in building their web, mobile and AI applications.















A project with Qdrant?
Describe your project. Our team replies within 24 hours with free technical scoping, along with a clear estimate of costs and timelines. No commitment.
- Reply within 24 hours from a project manager
or engineer.Reply within 24 hours from a project manager or engineer. - Technical scoping and quote, with no fees.
- No commitment, your data stays
confidential.No commitment, your data stays confidential.
Monday to Saturday · 9am to 6:30pm
hello@kosmos-digital.com
Paris · Lyon · Marseille · Nice · Geneva
Free scoping & estimate in less than 24h
Describe your project and we'll get back to you with a costed estimate and a roadmap.



