Qdrant, an open-source vector search engine company, announced it has raised $50 million in Series B funding to advance its technology for powering large-scale artificial intelligence systems. The round was led by AVP and included participation from Bosch Ventures, Unusual Ventures, Spark Capital, and 42CAP.
The company develops a vector search engine in Rust designed to support production-grade AI workloads. Qdrant’s platform enables organizations to retrieve and process data stored as vector embeddings, a foundational capability for applications such as retrieval-augmented generation (RAG), semantic search, and agent-based AI systems.
As enterprise AI deployments move from experimentation to operational infrastructure, Qdrant said retrieval systems must handle thousands of queries within automated workflows while processing continuously changing datasets. Traditional approaches that rely on single-vector similarity or bolt vector search onto legacy indexing architectures often struggle under these conditions.
Qdrant’s system is designed around what the company calls “composable vector search.” Engineers can combine multiple retrieval methods at query time, including dense and sparse vectors, metadata filtering, multi-vector representations, and custom scoring functions. This approach gives teams control over how indexing, ranking, and filtering interact with performance metrics such as relevance, latency, and cost.
The architecture allows organizations to optimize their search systems for different priorities without redesigning infrastructure as requirements evolve. Qdrant said the platform can be deployed across cloud, hybrid, on-premises, and edge environments, allowing companies to run vector search wherever AI-driven decisions occur.
Enterprises including Tripadvisor, HubSpot, OpenTable, Bazaarvoice, and Bosch currently use Qdrant for AI applications where vector search operates continuously under production workloads. The open-source project has surpassed 250 million downloads and has accumulated more than 29,000 stars on GitHub.
Qdrant has also received industry recognition in several market analyses, including The Forrester Wave for vector databases, GigaOm’s Radar for vector databases in 2025, and Sifted’s 2025 B2B SaaS Rising 100 list.
The company said the new funding will accelerate development of its retrieval infrastructure as organizations increasingly rely on vector-based search to power advanced AI applications.
KEY QUOTES:
“Many vector databases were built to only store dense embeddings and return nearest neighbors. That’s table stakes. Production AI systems need a search engine where every aspect of retrieval — how you index, how you score, how you filter, how you balance latency against precision — is a composable decision. That’s what we’ve built, that’s what developers and the most sophisticated enterprises are looking for as they scale internal and external AI workloads, and this funding accelerates our ability to make it the standard.”
André Zayarni, CEO And Co-Founder Of Qdrant
“With every infrastructure shift, we’ve seen purpose-built systems emerge and rapidly scale in fast-growing new markets, and we’re seeing this pattern again with Qdrant. As an AI-native vector search engine designed for the latency, throughput, and reliability demands of production AI workloads, they’re at the forefront of building the retrieval layer of the future that all advanced AI applications will depend on.”
Warda Shaheen, AVP
“In production AI applications, retrieving context-relevant information in real-time has become business-critical infrastructure. Qdrant’s Rust-based architecture is exemplary of the deep tech innovations that will shape the next generation of powerful and trustworthy AI systems.”
Ingo Ramesohl, Managing Director Of Bosch Ventures
