AI-Driven Cloud Cost Optimization

Question

Hi experts!

I want to:

• Implement a system that uses Azure Cost Management API + ML models (Azure ML) to predict future spend.
• Auto-trigger rightsizing (scale sets, reserved instances) based on prediction.
• Deliverable: Self-adjusting cost governance system.

Can you help me with the roadmap to implement this like, to where to start from?

Answer 1

Hello , Welcome to MS Q&A

To implement a self-adjusting cost governance system using Azure Cost Management API and Azure ML, you can follow this roadmap:

1. Understand Cost Management and Azure ML:
   • Familiarize yourself with Azure Cost Management tools and features. This includes setting budgets, monitoring costs, and understanding spending trends. Learn more about managing costs for Azure Machine Learning.
2. Set Up Cost Management Tools:
   • Use Microsoft Cost Management to analyze, monitor, and optimize your spending. Enable cost alerts to notify you about unexpected cloud spend or when you approach predefined limits. Explore cost governance for SaaS workloads.
3. Implement Machine Learning Models:
   • Develop ML models using Azure ML to predict future spending based on historical data. This involves understanding service-specific cost structures and optimizing expenses for each service type. Manage AI costs effectively.
4. Automate Rightsizing and Scaling:
   • Use Azure Policy to enforce cost controls and automate rightsizing of resources like scale sets and reserved instances based on predictions. Consider using virtual machine scale sets to dynamically adjust VM counts based on demand. Governance recommendations for AI workloads.
5. Monitor and Optimize Continuously:
   • Regularly review spending patterns and optimize resource allocation. Use tags to categorize resources for better management and billing. Set up automated cost monitoring and alerts to maintain financial control over AI initiatives. Explore the cost optimization maturity model.

Pls check and let us know

Thanks
Deepanshu

Answer 2

Hello Nidhi Priya,

Thank you for reaching out on the Microsoft Q&A.

To build a self-adjusting cost governance system with Azure Cost Management and Azure Machine Learning, start by laying the data foundation. Use the Azure Cost Management API to gather historical spend and usage data. This gives you the baseline to analyze trends and feed into your forecasting models.

Next, focus on model development. With Azure ML, experiment with forecasting approaches (e.g., ARIMA, Prophet, or deep learning models) to predict future spend. Train the models on your historical data and refine them regularly as new data comes in, so predictions become more accurate over time.

Once your forecasts are ready, move into automation and integration. Connect your ML predictions with Azure automation tools Logic Apps, Functions, or Automation Runbooks—to trigger actions such as rightsizing VMs, scaling sets, or planning Reserved Instances. Include a feedback loop where actual spend data continuously updates the model.

Finally, set up monitoring and governance. Use Cost Management dashboards and alerts to track predicted vs. actual spend and define policies to ensure actions align with organizational governance rules. Periodic reviews of the model and automation logic help keep the system adaptive to workload and pricing changes.

Here’s a simple roadmap flow to visualize the approach:

Data Collection → Model Training & Prediction → Automation & Rightsizing → Monitoring & Governance

By combining predictive insights with automated actions, you shift from reactive cost management to a proactive, self-adjusting system that optimizes Azure spending continuously.

Please find the attached document for your reference:

• Manage and optimize Azure Machine Learning costs
• Analyze, estimate and optimize cloud costs using Microsoft Copilot in Azure

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Thank you!