AI Cost Dashboards & FinOps
Master financial operations for AI infrastructure. Learn to implement cost allocation for GPU workloads, build executive cost dashboards, manage budgets with automated alerts, optimize cloud AI spending, and establish FinOps best practices that align AI investment with business value.
What You'll Learn
Complete FinOps coverage for AI and ML infrastructure costs.
Cost Allocation
Implement tagging strategies, chargeback models, and cost attribution for GPU workloads across teams and projects.
Cost Dashboards
Build Grafana and cloud-native dashboards showing real-time AI spend, trends, and forecasts.
Budget Management
Set budgets with automated alerts, approval workflows, and spend limits for AI projects.
Optimization
Reduce AI costs through right-sizing, spot instances, reserved capacity, and workload scheduling.
Course Lessons
Follow the lessons in order for comprehensive AI FinOps knowledge.
1. Introduction
What is FinOps for AI? The FinOps lifecycle, why AI costs are different, and the FinOps team structure.
2. Cost Allocation
Tagging strategies, chargeback vs showback, GPU cost attribution, and shared resource cost splitting.
3. Dashboards
Build cost dashboards: real-time spend tracking, team breakdowns, trend analysis, and cost anomaly detection.
4. Budgets
Budget creation, automated alerts, approval workflows, and spend forecasting for AI projects.
5. Optimization
Cost optimization: right-sizing GPU instances, spot/preemptible usage, reserved capacity, and scheduling strategies.
6. Best Practices
FinOps maturity model, organizational alignment, continuous optimization, and measuring AI ROI.
Prerequisites
What you need before starting this course.
- Basic understanding of cloud computing pricing models
- Familiarity with GPU instance types (AWS, GCP, or Azure)
- Understanding of ML training and inference workload patterns
- Access to cloud billing data or cost management tools
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