Best practices for dynamic capacity management
Google Cloud introduced FinOps controls for Gemini Enterprise to manage AI spending and announced best practices for dynamic capacity management to address scaling challenges in the agentic AI era.
AI agents are being deployed alongside human users, creating new infrastructure demands that are bursty and resource-intensive, often leading to scaling bottlenecks and underutilized compute resources. Google Cloud’s new best practices for dynamic capacity management aim to help organizations optimize infrastructure investments by combining predictable scheduling with automated responses to unpredictable demand. The approach includes tools like Dynamic Workload Scheduler, which offers flex-start and calendar modes to reserve resources for planned events or batch jobs, ensuring cost efficiency and guaranteed capacity when needed.
Organizations can maintain service continuity by defining automated fallback plans using managed instance groups (MIGs) in Google Compute Engine or custom ComputeClasses in Google Kubernetes Engine (GKE). These systems automatically pivot to alternative hardware configurations when preferred options are unavailable, decoupling workloads from single machine types or zones. Instance flexibility in MIGs allows multiple machine types to be specified, improving provisioning success rates, while Spot VMs integrate capacity signals to prioritize longer uptimes and lower pre-emption risks.
GKE extends these capabilities with a unified control plane for automating the entire capacity lifecycle, including multi-dimensional fallback lists, cross-zone scaling, and dynamic shifts between on-demand and Spot VMs. Custom ComputeClasses let platform teams design prioritized hardware alternatives, while dynamic resource allocation enables precise hardware slicing to maximize utilization and reduce waste. These features work together to eliminate all-or-nothing hardware assignments, allowing applications to request only the resources they need.
To implement these strategies, Google recommends auditing workloads for cost-saving opportunities, such as decoupling from single VM families or zones, and committing to minimum spend for discounted prices. Compute flexible committed use discounts offer up to 63% off sustained use, with discounts tied to resource usage rather than specific machine types or locations. The goal is to scale AI workloads without proportionally increasing infrastructure budgets or accruing technical debt.