Agentic AI infrastructure shifts enterprise focus from model choice to platform control
As agentic AI infrastructure moves from experimentation into production, enterprises are confronting a more complex question than which model to use: how to control the cost, data exposure and infrastructure supporting production AI applications.
That shift is pushing organizations to rethink how much they should rely on public cloud AI services alone, especially as agentic systems move from simple assistants into persistent enterprise applications that act across business systems. The turning point comes when companies begin treating AI not as a pilot project but as an operating model with enterprise-scale consequences, according to Joe Fernandes, vice president and general manager of the Artificial Intelligence Business Unit at Red Hat Inc.
“The token costs … are exploding rapidly, particularly as you move from experimentation to at-scale production systems and from simple chatbots and assistants into these always-running enterprise agents,” Fernandes said. “I think cost is a huge factor, but then there’s also the data side: Are you willing to let your data go into these public cloud services? Do you have compliance or sovereign requirements that preclude that? I think those are the two things combined … that make them start thinking, ‘Maybe there’s an alternative here to just exclusively relying on these public cloud services.’”
