Most AI pilots never reach production.

The technology works. The use case makes sense. Then the cloud bill arrives. Costs spiral before anyone sees it coming.

It starts small. A few GPUs in the cloud. Reasonable invoices. Then the project scales. Storage costs appear. Data transfer fees stack up. That monthly cloud bill? It can multiply by thirty before finance even flags it.

Meanwhile, GPUs sit idle. Storage and network cannot keep up with compute. Organizations invest in processing power, then watch it wait for data that arrives too slowly. Utilization rates below thirty percent are common.

Pilots get cancelled, budgets freeze, and AI ambitions stall across the organization.

In this 34-minute discussion recorded at the Cisco Studio in Amsterdam, Guy D'Hauwer (Automation Group) and Sander ten Hoedt (Cisco) break down what actually drives AI infrastructure costs and when it makes sense to move from cloud to owned infrastructure.

Key topics include:

• Why "cost per token" should be the metric every AI team tracks, and why most do not.
• How cloud flexibility turns into cloud lock-in through services that stack fees on fees.
• The break-even point where owned infrastructure starts delivering more capacity for the same budget.
• Why GPU underutilization is rarely a GPU problem, and what bottlenecks actually cause it.
• How prefab modular datacenters cut deployment time from months to days.