Dynamic capacity management for AI infrastructure

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The internet connected billions of people and mobile devices, putting computers in every hand. Now, we’re in the middle of the next big technology shift, deploying millions of autonomous AI agents to work alongside employees and end users. Today, we announced new FinOps controls for Gemini Enterprise to help organizations manage project-level AI spend and eliminate token shock. But the sheer scale of the agentic era is placing new constraints at every layer of the stack, including infrastructure. AI workloads are notoriously difficult to architect, resource-intensive, and bursty, which can also lead to scaling bottlenecks and large pools of underutilized — or misutilized — compute resources.  Organizations need insights to help them extract more value from their infrastructure investments. In this blog, we outline best practices for dynamic capacity management — scheduling and utilization strategies to help you run enterprise and AI applications on a single, flexible foundation with predictable cost and performance. These capabilities are designed to augment our on-demand, Spot and committed use discount (CUD) consumption models, which provide flexible pricing and discounting for your workloads. Let’s jump in. Here's a quick summary Three ways you can implement dynamic capacity management:

Schedule capacity for planned events. Schedule mission-critical resources (GPUs, TPUs and select VM families) ahead of planned events using calendar mode, or optimize costs for batch jobs with flexible start times using flex-start mode in Dynamic Workload Scheduler. Once you obtain the …

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