Qdrant builds a high-performance, composable vector search engine that lets engineers combine dense and sparse vectors, metadata filters, multi-vector representations, and custom scoring functions at query time. Written entirely in Rust, the platform gives teams granular control over indexing, scoring, filtering, ranking, and latency-versus-precision trade-offs, making it suitable for retrieval-augmented generation, semantic search, and agentic reasoning. The engine deploys flexibly across cloud, hybrid, on-premises, and edge environments and is already used in production by enterprises such as Tripadvisor, HubSpot, OpenTable, Bazaarvoice, and Bosch. Its open-source project has surpassed 250 million downloads and accumulated 29,000 GitHub stars, highlighting strong developer adoption. Qdrant has been recognized in The Forrester Wave: Vector Databases (Q3 2024), GigaOm’s Radar for Vector Databases v3 (2025), and Sifted’s 2025 B2B SaaS Rising 100. The company’s recent Series B financing strengthens its ability to scale these offerings and cement composable vector search as core AI infrastructure.

Total raised

$86M

Funding rounds

3

Latest round

Series B

Latest activity

Mar 2026