By releasing the code behind its search and vector engine under the SSPL, MongoDB is giving self‑managed users new visibility ...

Zilliz Cloud introduces a cloud-native multi-layer storage architecture that automatically places data across memory, loal SSD, and object storage based on access patterns. Hot data stays fast, cold ...

Qdrant, a leading provider of high-performance, open-source vector search, is offering a private beta of Qdrant Edge, a lightweight, embedded vector search engine designed for AI systems on devices ...

Qdrant, the open-source vector search engine used by enterprises and AI-native teams, announced Tiered Multitenancy—as part of the v1.16 release—a new capability that helps organizations isolate heavy ...

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 ...

Have you ever searched for something online, only to feel frustrated when the results didn’t quite match what you had in mind? Maybe you were looking for an image similar to one you had, or trying to ...

Wikidata has built the semantic web backbone supporting knowledge cards in popular engines. Now, it's extending this foundation using a vector database to enhance its existing knowledge graph and ...

“With Elasticsearch vector database at the heart of the Dell AI Data Platform's unstructured data engine, Elastic will bring vector search and hybrid retrieval to a turnkey architecture, enabling ...

Most vector search systems struggle with a basic problem: how to break complex documents into searchable pieces. The typical approach is to split text into fixed size chunks of 200 to 500 tokens, this ...