In the AI era, enterprises are reconsidering the “everything in the cloud” approach. The costs and needs of businesses are leading to a more flexible approach, including on-premises data centers for production AI. Decision-making factors include security, data sovereignty, costs, and the AI roadmap for the next five to seven years.

Cloud computing has risks such as data security and lack of control over infrastructure access. International companies face data sovereignty issues with US-based cloud providers. Owning infrastructure gives enterprises more control, legal compliance, and customer data responsibility. Enterprises can also optimize AI infrastructure investments and stretch budgets more effectively than in a cloud setup.

Factors like cloud, hybrid, and on-premises installations, as well as business and IT risk considerations, need examination for AI workload deployments. Enterprises must carefully evaluate where to run their AI production workloads based on various factors to achieve their AI aspirations. A sponsored paper provides in-depth insights on optimizing AI infrastructure deployment.

Read more at Yahoo Finance: Where should enterprises run their AI workloads?