Teradata announced Teradata Enterprise Vector Store, an in-database solution that delivers a new data management technology that is essential to driving large-scale and reliable AI in organizations: vector store. This brings the speed, power, and multidimensional scale of Teradata’s hybrid cloud AI data integration platform, Teradata Vantage, to vector data management. This capability will be integrated with NVIDIA NeMo Retriever generative AI microservices, a component of NVIDIA AI Enterprise (NVAIE), in the future. This integration connects custom large-scale language models to enterprise data and enables AI applications to respond with high accuracy. The ability to process billions of vectors in milliseconds and integrate with existing enterprise systems enables real-time decision making and delivers cost-effective, advanced processing power to extract real value from complex, multifaceted business challenges that transform business.
Vector stores are the foundation for organizations that want to leverage AI agents. In reality, most vector stores are either difficult or require huge investments to solve the toughest, but potentially most profitable, business problems. This is because they can only rapidly manage vector volumes for small datasets, and cannot handle the large scale and high speed required for AI agent use cases. To efficiently develop and train AI models and leverage AI agents, you need both the processing power to combine unstructured datasets with mission-critical structured data at lightning speed, and massive computing power.
Teradata Enterprise Vector Store is designed to realize use cases that require vector store functionality and RAG (Search Augmentation Generation) applications. With cost-effective scaling and near-seamless integration, it is expected to help companies maximize value and insights from unstructured data while reducing expenses. And because Teradata provides a hybrid cloud AI data integration platform, it can flexibly expand in both cloud and on-premise environments, maximizing the use of current infrastructure and helping companies build AI agents in any environment with excellent investment efficiency.
SOURCE: PRTimes
