Pay for a working agent, not infrastructure setup

Service cost combines launching an isolated agent instance with usage of the selected AI model. The deployment price is visible before confirmation. Requests to paid models are tracked separately based on actual consumption.

This separates infrastructure from variable AI expenses. You can validate a workflow with an accessible model, then move to a stronger option when reasoning quality matters more than speed or cost.

Estimate task frequency, input size, and expected output length before scheduling a workflow. A short daily briefing and a deep report based on dozens of sources create very different model workloads.

Start with a test process and observe actual usage. Review operation history, remove unused schedules, and reserve advanced models for work where they produce a meaningful quality improvement.