AI Costs Shift From Training to Inference Infrastructure

As enterprises move from AI experimentation to production, the main cost driver has shifted from model training to the infrastructure needed to run thousands of concurrent inference workloads. Agentic AI is accelerating this change, demanding continuous support for unpredictable, short-lived requests. Unlike early AI projects with scheduled training jobs, production environments strain GPU, networking, and storage resources in ways traditional infrastructure was never built to handle. For technology leaders, infrastructure efficiency is now a defining factor in AI economics.