Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the ...

This expansion is fueled by the rapid adoption of AI, LLMs, and multimodal applications that require high-performance vector search, scalable indexing, and real-time retrieval. By offering, the ...

Despite the aggressive cost claims and dramatic scale improvements, AWS is positioning S3 Vectors as a complementary storage ...

Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...

In the age of generative AI (genAI), vector databases are becoming increasingly important. They provide a critical capability for storing and retrieving high-dimensional vector representations, ...

In the fast-paced digital landscape of the United States, where data is the cornerstone of innovation, traditional databases are facing challenges in keeping up with the demands for efficient and real ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Today’s complex, unstructured data — text, images, audio and video — are ...

First announced early this year, KIOXIA's AiSAQ open-source software technology increases vector scalability by storing all RAG database elements on SSDs. It provides tuning options to prioritize ...

Vector databases explained through speed vs velocity: why AI needs vectors, not rows and columns, to manage context, ...