Google Cloud outlines three practices for dynamic capacity management to handle AI workload bursts without overspending.

First, schedule capacity for predictable events using Dynamic Workload Scheduler, which supports calendar mode for firm reservations and flex-start mode for queued, latency-tolerant jobs—improving access to scarce GPUs/TPUs.

Second, create automated fallback plans so workloads run even when preferred hardware is unavailable; Compute Engine MIGs and GKE custom ComputeClasses let you define priority-ordered alternative machine families, zones, and on-demand/Spot mixes, with active migration returning to higher-priority options when capacity frees.

Third, use dynamic resource allocation in GKE to request exact memory or core slices instead of whole accelerators, reducing waste.

The post also recommends auditing workloads for decoupling from single VM types, adopting committed use discounts (up to 63% off) with flexible resource ties, and engaging account teams for tailored strategies.