Plan capacity for AI agent workloads
Capacity pricing can make per-call economics more predictable when workloads use the same active capacity efficiently.
Illustrative scenario
Illustrative agent scenario: customer service, code review, and report generation can create many inference calls across a single workflow.
Token-metered costs can be difficult to forecast as request volume, reasoning steps, and tool calls vary.
A capacity plan can help teams choose active capacity around the expected workload.
How QDivZero can fit
QDivZero Compute uses active-capacity pricing for compatible model workloads. When many calls share the same active capacity efficiently, per-call economics can become more predictable.
Teams can scale capacity with demand and monitor utilization. Actual economics depend on workload shape, model choice, and the capacity selected.
How QDivZero fits in
Capacity-based pricing
Select capacity around expected workload and utilization.
Predictable economics
Keep per-call economics more predictable while active capacity stays unchanged.
Capacity-provisioned workflows
Scale within the capacity you provision.
Illustrative workflow
Choose Compute capacity around expected agent workload and utilization
Use active capacity efficiently to make per-call costs more predictable
Run agent workflows within the selected capacity
Plan a capacity line item while monitoring utilization