Photo by Tyler on Unsplash. Source: https://unsplash.com/photos/a-close-up-of-a-server-in-a-server-room-vSprjjDbu60 (Unsplash License).

The short version

Enterprise AI spending is shifting from training to serving, and the bill compounds on whoever runs inference. That shift redraws the line between public cloud and the data center. Vendors now compete over control of the tokens, not the models.

  • One environment, bare metal to inference. An AI Gateway governs models across on-premises and cloud.
  • The pitch is tokenomics. Faster time to first model and predictable cost per unit of AI work.
  • Hardware partners. Certified Dell, Cisco, Lenovo, and Supermicro servers, plus MetalSoft automation.
  • The subtext is licensing. After pushing customers to subscriptions, Broadcom needs VCF to be where AI runs.

What to watch before you believe the private AI cloud promise. Read the full piece.

By Ivan Tarin

Ivan Tarin is a Principal Product Marketing Manager at SUSE, where he owns go-to-market strategy and positioning for a seven-product cloud-native portfolio spanning Kubernetes, virtualization, storage, security, and observability. A former full-stack developer who shipped production code for enterprise and public-sector clients including U.S. national laboratories, Ivan translates complex infrastructure and AI technology into messaging that lands with developers, platform teams, and enterprise buyers. He has presented at KubeCon, SUSECON, and AWS Developer Week, and is currently pursuing an MS in Artificial Intelligence at the University of Colorado Boulder.

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