Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the ...

When designing search systems, the decision to use keyword-based search, vector-based search, or a hybrid approach can significantly impact performance, relevance, and user satisfaction. Each method ...

Open-source vector database startup Qdrant Solutions GmbH today announced it has raised $50 million in early-stage funding to pave the way for smarter and more reactive artificial intelligence apps.

Companies across every industry increasingly understand that making data-driven decisions is a necessity to compete now, in the next five years, in the next 20 and beyond. Data growth — unstructured ...

The emergence of vector databases and vector search for handling massive quantities of complex data have radically transformed the way AI is implemented and managed. As a specialized approach for ...

MongoDB enables millions of developers to securely build AI applications on any infrastructure, from local machines to on-premises data centers "According to a 2025 IDC survey, more than 74% of ...

Qdrant, the open-source vector search engine built in Rust for production workloads, today announced $50 million in Series B funding led by AVP, with participation from Bosch Ventures, Unusual ...

Qdrant develops a vector search engine designed for production AI systems, enabling teams to configure retrieval, ranking, ...

Hosted search and discovery platform for enterprise Algolia Inc. today launched NeuralSearch, a vector and keyword search engine using a single application programming interface that provides ...

Qdrant, the open-source vector search engine built in Rust for production workloads, announced it has secured $50 million in Series B funding will enable composable vector search as core ...