Who it is for
AI architects, product teams, and engineering leaders.
What problem it solves
Model economics are difficult to reason about without connecting usage assumptions to the full AI workflow.
How it works
Model token usage, inference, agent workflows, provider alternatives, and architecture-level AI operating cost assumptions. It uses the current project or research context so each step remains connected to the work around it.
Key capabilities
- Token and inference assumptions
- Agent workflow estimates
- Provider and model comparisons
- Architecture-level AI cost planning
Example use case
Estimate the operating range for a multi-step support agent using synthetic request volumes.
Limits and verification
Model pricing and consumption vary; confirm supported providers and current pricing before relying on estimates.