Qdrant, an open-source vector search engine designed for production environments, has raised $50 million in Series B funding. The round was led by AVP, with participation from Bosch Ventures, Unusual Ventures, Spark Capital, and 42CAP.
The new funding will help Qdrant expand its engineering and product teams while accelerating development of its search infrastructure. The company also plans to strengthen its enterprise offerings, scale global operations and support wider adoption of its open-source platform among developers and large organisations. Part of the capital will go toward improving performance, deployment flexibility and reliability for high-volume production workloads.
Why modern systems demand a new search infrastructure
Vector search initially emerged to address a relatively narrow challenge: identifying the closest matches within dense datasets. But the requirements of modern systems have evolved far beyond that starting point.
Today, retrieval processes often operate inside automated workflows that execute thousands of queries during a single task. These processes interact with constantly changing datasets and multiple types of information simultaneously. Systems built for static datasets or single-vector similarity searches struggle under these conditions.
Applications such as retrieval-augmented generation pipelines, semantic search systems, and reasoning-driven workflows all rely on search infrastructure that can maintain speed and accuracy under sustained load. As a result, companies increasingly require search engines built specifically for these new demands.
Rebuilding search from the ground up
Qdrant was developed with this challenge in mind. In 2021, André Zayarni and Andrey Vasnetsov collaborated on a project to leverage vector similarity search to build a matching engine for unstructured data objects. Written in Rust, the system was designed from the lowest architectural level to support complex search operations at scale.
Instead of relying on a fixed indexing model, Qdrant treats the core components of retrieval, such as indexing, scoring, filtering and ranking, as modular building blocks. Engineers can combine these elements directly when constructing queries, allowing them to tailor search behaviour to specific workloads.
This approach allows teams to combine dense vectors, sparse vectors, metadata filters, multi-vector representations and custom scoring rules within a single query. Developers gain direct control over how each factor influences relevance, response time and computational cost.
Rather than forcing organisations to adapt their applications around rigid search tools, Qdrant’s design allows the infrastructure to adapt to the problem itself.
Built for production, wherever it runs
As organisations shift from experimentation to mission-critical deployment, where search infrastructure operates has become just as important as how it performs.
Qdrant was built to run across cloud environments, hybrid infrastructure, private on-premise systems and edge deployments. This flexibility allows companies to keep search capabilities close to where data is generated or decisions are made.
Because the engine was designed as modular infrastructure rather than a tightly managed service, organisations can deploy it in ways that match their operational and regulatory requirements.
Enterprises including Tripadvisor, HubSpot, OpenTable, Bazaarvoice, and Bosch rely on Qdrant where vector search runs continuously under real-world load. The open-source project has surpassed 250 million downloads and 29,000 GitHub stars, with a global community driving improvements based on production requirements.
Real-world adoption across global companies
Demand for Qdrant’s technology has grown as businesses integrate advanced search capabilities into everyday workflows. Major organisations, including Tripadvisor, HubSpot, OpenTable, Bazaarvoice and Bosch, already rely on the platform to manage high-volume search processes that run continuously under production workloads.
The open-source project has also built a large global developer community. To date, Qdrant has recorded more than 250 million downloads and over 29,000 GitHub stars, reflecting strong adoption among engineering teams experimenting with advanced search infrastructure.
“Many vector databases were built to only store dense embeddings and return nearest neighbours. That’s table stakes,” said André Zayarni, CEO and Co-Founder of Qdrant. “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.”
“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,” said Warda Shaheen of AVP.
“In production AI applications, retrieving context-relevant information in real-time has become business-critical infrastructure,” said Ingo Ramesohl, Managing Director of Bosch Ventures. “Qdrant’s Rust-based architecture is exemplary of the deep tech innovations that will shape the next generation of powerful and trustworthy AI systems.”
