Qdrant

The Deutsche Telekom LMOS case describes Qdrant as a backend for an AI customer-service platform supporting more than two million conversations across three countries.

Qdrant reports enterprise production use at companies including Canva, HubSpot, Roche, Bosch and OpenTable, more than 250M package downloads, and deployments ranging from cloud to edge and supercomputers.

Qdrant reports enterprise production use at companies including Canva, HubSpot, Roche, Bosch and OpenTable, more than 250M package downloads, and deployments ranging from cloud to edge and supercomputers.

Qdrant documents binary quantization with oversampling and full-vector rescoring to control retrieval speed, memory and accuracy tradeoffs. This concrete retrieval mechanism adds technical evidence beyond the existing Rust engine and general hybrid-search description.

Qdrant builds a Rust-based vector search engine focused on predictable low tail latency, billion-scale retrieval and composable retrieval infrastructure, including edge deployments and hybrid search.

Qdrant builds a Rust-based vector search engine focused on predictable low tail latency, billion-scale retrieval and composable retrieval infrastructure, including edge deployments and hybrid search.

Qdrant's organization-maintained GitHub Sponsors page describes a Berlin core team with geographically distributed remote members and identifies named machine-learning and Rust software engineers alongside its founders.

Qdrant offers managed, hybrid and private-cloud enterprise deployments across AWS, Google Cloud and Azure, and raised a $50M Series B in 2026 to scale its core retrieval infrastructure.

Qdrant offers managed, hybrid and private-cloud enterprise deployments across AWS, Google Cloud and Azure, and raised a $50M Series B in 2026 to scale its core retrieval infrastructure.

Qdrant documents collection snapshots containing data and pre-built indexes, restoration across environments without reindexing, version compatibility requirements and temporary disk capacity requirements. These explicit backup and recovery practices support transferable database operations skills.

Qdrant expertise spans vector databases, retrieval, Rust systems engineering, distributed infrastructure, RAG, multimodal search and enterprise deployment, all highly transferable across AI systems employers.

Qdrant expertise spans vector databases, retrieval, Rust systems engineering, distributed infrastructure, RAG, multimodal search and enterprise deployment, all highly transferable across AI systems employers.