Teradata, a provider of data analytics solutions, today announced a new database offering for managing vector data.

Teradata Enterprise Vector Store manages unstructured data in multi-modal formats like text, video, images, and PDFs. It can process billions of vectors and integrate them into pre-existing systems, and offers response times in the tens of milliseconds. 

According to the company, vector stores are an important foundation for agentic AI, but many vector stores require organizations to make tradeoffs, such as getting fast results, but only for small data sets, or being able to handle large vector volumes, but not at the speed required by agentic AI use cases. 

“Vector stores are at the root of how we bind truth to generative AI models and agentic AI. They are essential to any data management practice, but their impact is limited when they are slow or siloed,” said Louis Landry, CTO of Teradata. “Teradata’s long-standing expertise in high concurrency and linear scale, as well as the critical ability to harmonize data and support RAG, means Teradata Enterprise Vector Store delivers on the dynamic, trusted foundation large organizations need for agentic AI.”

Key features of Teradata Enterprise Vector Store include: 

  • Full life cycle management of vector data, from embedding generation to intelligent search
  • Processing data within Teradata, with flexible deployment options to cloud, on-premises, or hybrid
  • Support for AI frameworks like LangChain
  • Temporal vector embedding capabilities, which tracks changes to data over time to improve trust and explainability. 

The company plans to integrate this offering with NVIDIA NeMo Retriever microservices, which will provide customers with a highly accurate information retrieval solution that preserves data privacy.  

“Data is essential to accurate inference for AI applications,” said Pat Lee, vice president of strategic enterprise partnerships at NVIDIA. “Teradata Enterprise Vector Store, integrated with NVIDIA AI Enterprise and NVIDIA NeMo Retriever, can unlock the institutional knowledge stored in PDFs and other unstructured documents to power intelligent AI agents.”