Teradata has launched its new AI Factory, an advanced solution designed to support private AI initiatives in secure on-premises deployments. The Factory was developed in partnership with NVIDIA. By combining data pipelines, algorithm execution, and infrastructure tools, it expedites the development of enterprise AI. This action tackles the growing security, financial, and regulatory issues with cloud-based AI systems. Additionally, Teradata’s product is positioned to revolutionize the way AI is used in various industries as demand for more compliance rises.
Why Are Enterprises Turning to Private AI Now?
Teradata’s AI Factory is a response to the growing pressure on companies to control data sovereignty and lower cloud costs. By combining NVIDIA’s technology with Teradata’s software stack, this all-in-one platform enables quicker and more secure enterprise AI deployments. Additionally, it makes it possible for company-owned infrastructure to directly support agentic, generative, and predictive AI models.
The AI Factory is ideal for industries where regulatory compliance and data control are crucial. These sectors include government, healthcare, and finance. Additionally, the design minimizes risks like data leaks and enables businesses to make effective use of their current GPU hardware. This ensures consistent performance and operational predictability while avoiding the rising costs associated with cloud systems.
Inside the Architecture of Teradata’s AI Factory
Teradata’s platform incorporates several core technologies into a seamless on-premises deployment solution. It includes Teradata’s Enterprise Vector Store and AI Microservices, which work with NVIDIA NeMo to enable Retrieval-Augmented Generation (RAG) pipelines. Additionally, these features maintain strict data boundaries while supporting real-time AI use cases.
The AI Workbench provides tools for managing model lifecycles, self-service analytics, and regulatory compliance. It incorporates frameworks such as JupyterHub, Airflow, and ModelOps, as well as libraries like ClearScape Analytics. Additionally, customers benefit from one-click LLM deployment, which streamlines tasks that would otherwise require a lot of setup.
The factory’s data pipeline and algorithm execution layers are designed to be quick and scalable. It guarantees quick data access by utilizing QueryGrid and Open Table Format. Additionally, it uses NVIDIA tools to support complicated formats like PDFs. Together, these components make private AI development fast, compliant, and manageable.
Private AI Drives Cost Control and Compliance Gains
Teradata’s solution fits the market trend as more businesses invest in on-premises deployment to maintain control. By 2028, more than 20% of businesses will host AI workloads locally, according to a Gartner report. Additionally, Teradata’s solution directly addresses this need by combining all necessary components into a stack that is ready for use.
The AI Factory optimizes performance without depending on fluctuating cloud fees by allowing teams to select between CPUs and customer-owned GPUs. By restricting data movement, its architecture reduces latency. As a result, the AI development process runs more smoothly and efficiently.
The platform’s capacity to combine software and hardware into a scalable solution makes it special. By using this approach, companies can future-proof their enterprise AI strategies while still adhering to international regulatory standards.
The Future of Controlled AI Innovation
As AI ecosystems become more complex, companies are seeking simplified tools that offer performance, security, and flexibility. Teradata’s AI Factory addresses this need with a turnkey solution that makes private AI more accessible. Teradata has developed its most recent product to help clients accelerate AI innovation on their terms. Therefore, on-premises AI deployment is the future for organizations looking to lower risk, save expenses, and take control.
About The Author
