ai-cost-management

Tracking and controlling what AI actually costs to run, from token consumption and per-task pricing to infrastructure and model selection trade-offs. This area covers the gap between rising spend and demonstrable ROI, why agentic workloads inflate costs faster than provider price cuts can offset, and how to attribute expenses to real business outcomes. Expect practical guidance on budgeting, usage forecasting, cost attribution, and deciding when a workload justifies its price.