From private cloud to private AI cloud, software decides who wins
Enterprise experimentation with cloud AI has run into a wall of data-control, sovereignty and token-cost questions, pushing intelligence back toward infrastructure enterprises own. The result is a rebuild of the private cloud as an AI platform, where production inference, not experimentation, sets the requirements.
That shift lands squarely on information technology operations teams, which must serve frontier models, small local models and swarms of agents from the same pool of hardware. Mixing those workloads without driving up server, energy and licensing costs is now the central architectural problem, according to Chris Wolf (pictured), global head of AI and advanced services, VMware Cloud Foundation Division, at Broadcom Inc.
“You have sovereignty considerations. You have tokenomics considerations as well. This doesn’t mean don’t use frontier models. It means be practical,” Wolf said. “Use frontier models where it makes sense, where [you] need deep reasoning. Use specialized models, local SLMs, where they make sense as well. You’re really seeing this breadth of coverage happening in the industry — and now IT operations is caught in the middle of all of this.”
