Cheaper tokens, bigger bills: The new math of AI infrastructure

Presented by NutanixAs enterprises move from AI experimentation into production deployment, the primary cost driver has shifted away from foundation model training and toward the infrastructure required to run thousands of concurrent inference workloads at scale, with agentic AI as the accelerant. Where early enterprise AI projects involved a handful of large, scheduled training jobs, production agentic environments require continuous support for short-lived, unpredictable requests that consume GPU, networking, and storage resources in ways traditional infrastructure was never designed to handle. For enterprise technology leaders, that shift is turning infrastructure efficiency into a make-or-break factor in AI economics. "Every employee with an AI assistant, every automated workflow, ever