Experiment with AI within your chosen capacity
Run more experiments within the capacity and budget you choose.
Illustrative scenario
Illustrative developer scenario: a team wants to test more AI features, but token-metered calls make prototypes and experiments harder to budget.
When every test adds variable usage charges, teams may ration inference and delay experiments. A capacity-based workflow can make the cost boundary easier to plan.
A capacity-based workflow can make it easier to plan experiments around a selected Compute instance and schedule.
How QDivZero can fit
QDivZero Compute uses active-capacity pricing for compatible model workloads. Teams choose the capacity and schedule that fit their development needs rather than treating each call as a separate token-metered decision.
Run more experiments within the capacity and budget you choose. Deploy selected models from the catalog through an OpenAI-compatible endpoint, then scale capacity with traffic and development cycles.
How QDivZero fits in
Active-capacity pricing
Choose the active capacity and budget that fit your workload.
Capacity-bounded inference
Run experiments within the capacity and budget you choose.
Production-ready models
Deploy selected models from the catalog through one OpenAI-compatible endpoint.
Illustrative workflow
Choose Compute capacity pricing for model experiments
Run more experiments within the capacity and budget you choose
Deploy selected models from the catalog with an OpenAI-compatible API
Scale capacity with development cycles and traffic needs