Powerful vector search engine
Deploy Qdrant instantly for semantic search, RAG and recommendation systems.
Key features
What engineering teams need for reliable deployments.
Instant deployment
The Qdrant database is up and running in seconds with high-speed NVMe disks.
Semantic search
Fast vector similarity search for RAG and AI applications.
Simple management
Manage your cloud cluster without complex Linux configuration.
Built with Rust
Rust delivers fast performance with efficient resource usage.
Frequently asked questions
Is Qdrant suitable for Persian text processing?
Yes. Qdrant is language-independent. Supply embeddings from multilingual or Persian models such as BGE-M3 for semantic search in Persian text.
How do I connect my application code to Qdrant?
Use Qdrant SDKs for Python, Node.js, Go and Rust, or its REST API.
What is the difference with traditional databases?
Traditional databases such as MySQL handle exact data queries. Qdrant searches semantic relationships in text and images using vectors from AI models.
Qdrant for AI applications
Qdrant is an advanced open-source vector search engine. As AI and large language models (LLMs) become more widely used, semantic storage and search are increasingly important. Deploy Qdrant on the Cloud Noce platform to quickly set up search for your AI projects.
Qdrant stores vectors representing text, images or audio, allowing searches by meaning, including across Persian text.
Fast vector search with Rust
Qdrant's Rust core uses resources efficiently and handles thousands of queries per second. Cloud Noce NVMe storage supports fast, reliable vector data processing.
Passing sanctions and free access to global services
Cloud Noce PaaS uses integrated network tools to connect applications and containers to global services affected by access restrictions.
Connect your applications to development packages, external APIs and the tools your workflow requires.