Cloud Region Capacity Planning

Compare regions for the next cloud capacity expansion using power availability, latency constraints, GPU supply, and timing assumptions before quotas, routing constraints, or utility gates force a poor placement decision.

For cloud infrastructure and capacity planning teams deciding where to expand next.

Which regions can support the next capacity expansion without power, latency, or GPU bottlenecks?

Sample region comparison

Review one region comparison view showing power timing, latency fit, supply tightness, and operating cost tradeoffs across candidate regions.

Illustrative region comparison

Example view for a next-region expansion decision.

Example question

Which region gives the best balance of power timing, GPU access, latency fit, and operating cost for the next expansion move?

The full scorecard expands this comparison with ranking logic, constraint notes, and the assumptions behind each region recommendation.

What we test

• region-level power availability
• latency constraints
• GPU availability and supply constraints
• demand and expansion timing assumptions

What the scorecard includes

• a ranked comparison of which regions are safer or riskier for the next expansion move
• the main capacity constraints behind each region score
• a scorecard that can be circulated to infrastructure and planning teams

Region Capacity Scorecard

Which regions can support the next capacity expansion without power, latency, or GPU bottlenecks?

Preview the variables behind the scorecard

These cards show the outcome measures, conditions, and levers tracked in the region capacity scorecard.

Key outcome measures

Latency SLO Attainment Share

Share of traffic or minutes meeting the latency SLO across the region mix.

percent

Capacity Deficit Index

Normalized shortfall of available capacity vs. demand forecast plus provisioning buffer.

index (0–1)

Effective Capacity Cost per GPU‑hour

Blended $/GPU‑hour from on‑demand, reserved, spot, and unused commitment effects.

USD/gpu hour

Multi‑region Resilience Index

Probability‑scaled index of serving demand under AZ/region failure scenarios and observed failover success.

index (0–1)

Compliance Coverage Share

Portion of demand served from regions meeting data‑residency/regulatory constraints for the workload.

percent

Egress Cost per GB

Effective $/GB for inter‑region/Internet data transfer under the current region plan.

USD/gb

Power Availability Lead Time (months)

Expected months to secure incremental MW capacity in target regions (provider + interconnect milestones).

months

Chip Supply Alignment Index

Fit between GPU supply (deliveries/allocations) and planned demand across regions and priority tiers.

index (0–1)

Key conditions behind the comparison

GPU Allocation Inventory

GPUs available for allocation (delivered and commissioned but not yet assigned).

gpu

Capacity Request Backlog (GPU)

Outstanding GPU capacity requests awaiting provider approval/quota increase.

gpu

Power‑permit Queue Backlog (MW)

MW awaiting permitting/interconnect approval in targeted regions/campuses.

MW

Savings Plan Unused Commitment (USD)

Cumulative unused Savings Plan commitment within the current accrual period.

USD (millions)

RI Unused GPU‑hours

Purchased reserved GPU‑hours not applied to usage in the period.

gpu hour

Workload Placement Backlog (requests)

Pending placement requests waiting for region assignment given constraints.

requests

Levers that can change the scorecard

RI/Savings Plan Coverage Target Share

Target fraction of compute cost/hours to cover with RIs or Savings Plans.

0–1 ratio

Redundant Regions Count

Number of active regions provisioned for failover (N‑way).

count

Traffic Steering Aggressiveness Index

Degree to which routing favors best‑latency/lowest‑cost regions among eligible choices.

index (0–1)

Data Locality Enforcement Index

Strictness of data‑residency enforcement in placement and routing.

index (0–1)

Provisioning Buffer Target Share

Planned headroom over forecast to absorb demand and lead‑time variance.

0–1 ratio