Qdrant is an open-source vector database and vector search engine, available at qdrant.tech, designed to provide fast and scalable vector similarity search for high-dimensional data. Built in Rust, Qdrant delivers cloud-native scalability, high availability, and cost-efficient storage options, making it a popular infrastructure choice for AI-driven and large-scale machine learning applications.
Qdrant's primary offering is a high-performance vector database that enables organizations to store, index, and search billions of vector embeddings efficiently. Its API and architecture are designed for ease of use, simple deployment, and robust integration with modern AI pipelines. By leveraging Rust, Qdrant achieves reliability and performance crucial for demanding workloads, such as semantic search, recommendation systems, and generative AI retrieval tasks.
Qdrant distinguishes itself through:
- Native high-dimensional vector support optimized for performance and scalability
- Open-source accessibility with enterprise-ready features
- Flexible deployment options (cloud, on-premise, hybrid)
- Cost-efficient storage management for large datasets
Who Uses Qdrant?
Qdrant serves a wide range of organizations looking to operationalize AI and advanced search capabilities. Its users include technology companies, enterprises, and AI startups. Notable customers include:
- Hubspot
- Bayer
- CB Insights
- Bosch
- Cognizant
- Tripadvisor
- Deutsche Telekom
- Sprinklr
- QA.Tech
- Kairoswealth
- Kern AI
- Voiceflow
- Nyris
- Dailymotion
- Visua
- Dust
- IrisAgent
- Talkmap
- ML6
- Balderton
These clients use Qdrant to power recommendation engines, semantic search, analytics, and generative AI use cases at scale.
How Was Qdrant Started?
Qdrant was founded by André Zayarni (CEO & Co-Founder) and Andrey Vasnetsov (CTO & Co-Founder), bringing together expertise in engineering scalable data systems. The leadership team includes professionals in product, enterprise solutions, finance, growth, and sales, supporting the company’s mission to deliver robust vector search infrastructure. You can read more about their vision and team on the About page.
Who Are Qdrant's Competitors?
Qdrant operates in the vector database and AI infrastructure space, where its main competitors are other vector search platforms and databases, such as Pinecone, Weaviate, Milvus, and Vespa. These platforms also focus on enabling scalable similarity search and powering AI applications, though Qdrant differentiates with its Rust foundation, open-source approach, and deployment flexibility.
Latest News on Qdrant
Recently, Qdrant has been highlighted in operational use cases such as Lettria’s scalable GraphRAG architecture with Neo4j and Qdrant, and in GoodData’s turbocharged AI analytics solution. For more details, see their latest announcements and blog posts on their website.
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