Qdrant Solutions GmbH
Qdrant provides a high-performance open-source vector database and search engine built in Rust, designed to handle production-grade AI workloads with high scalability and efficiency.
At a Glance
- Enterprise AI
- E-commerce
- Recruiting
- Financial Services
- +1 more
AI Tools by Qdrant Solutions GmbH
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Qdrant
Open Source Vector Database Engine
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Latest News
Qdrant Skills for AI Agents: Tooling for agent-driven retrieval.
Qdrant Raises $50M Series B to Build Composable Vector Search.
Qdrant Meets Google Gemini Embedding 2: Native support announced.
GlassDollar Improves High-Recall Sourcing by Migrating from Elasticsearch to Qdrant.
Products & Services
High-performance vector search engine and database built in Rust.
Fully managed vector search service with auto-scaling, HA, and specialized tools.
Managed Qdrant clusters running on customer-owned infrastructure for data residency.
Isolated deployments for strict security and compliance requirements.
Market Position
Positions itself as a faster, more memory-efficient, and production-ready alternative to general-purpose databases with vector extensions (like pgvector or Elasticsearch) and competing vector databases like Pinecone or Milvus, emphasizing its Rust-based performance and 'composable' architecture.
Leadership
Founders
André Zayarni
Co-founder and CEO of Qdrant. Previously served as CPO and CTO of MoBerries GmbH and founded JobBot.me.
Andrey Vasnetsov
Co-founder and CTO of Qdrant. Creator of the Qdrant vector search engine. Expert in machine learning, metric learning, and high-performance Rust development.
Executive Team
André Zayarni
CEO & Co-Founder
Former CPO/CTO at MoBerries.
Andrey Vasnetsov
CTO & Co-Founder
Creator of Qdrant, Rust and ML expert.
Board of Directors
Founding Story
Started in 2021 by André Zayarni and Andrey Vasnetsov because existing vector search libraries like FAISS lacked the scalability and production features required for modern AI applications. Andrey Vasnetsov built the engine from scratch in Rust to prioritize performance and reliability.
Business Model
Revenue Model
Open-core model with revenue from managed cloud subscriptions (SaaS), hybrid/private cloud deployments, and enterprise support.
Pricing Tiers
0.5 vCPU, 1 GB RAM, 4 GB Disk, single-node cluster, free cloud inference with selected models.
Dedicated resources, vertical/horizontal scaling, HA, backup & DR, 99.5% SLA.
Enterprise security, SSO, private VPC links, 99.9% SLA, premium support.
Target Markets
- Enterprise AI
- E-commerce
- Recruiting
- Financial Services
- Cybersecurity
- Semantic Search
- Recommendation Systems
- Retrieval-Augmented Generation (RAG)
- Anomaly Detection
- AI Agents
- Video Analysis
- GlassDollar
- Bazaarvoice
- My AskAI
- Twelve Labs