Kyle Ikeda presents a practical overview of optimizing GPU-driven AI workloads on Azure by leveraging Reserved VM Instances, with a step-by-step scenario focused on reducing costs while maintaining performance.

Streamline Cloud Spend with Azure Reserved VM Instances

Cloud costs, especially for GPU-heavy AI workloads, can rapidly escalate and outpace forecasts. This article demonstrates, through a scenario with the fictitious company Contoso, how organizations can rein in these expenses using Azure Reserved Virtual Machine Instances (RIs) and Azure-native optimization tools.

Why Consider Azure Reserved VM Instances?

Azure Reserved VM Instances provide significant discounts (up to 72%) compared to pay-as-you-go pricing when you commit to specific VMs in a particular region for 1 or 3 years. For predictable, stable workloads, this commitment converts variable cloud costs into a more reliable, budget-friendly model.

Scenario: Contoso’s AI Workloads

  • Training and inference using NC64as T4 v3 VMs for generative AI models
  • Deep learning and batch processing at scale
  • Unpredictable spend on GPU hardware challenged their budgeting

Key Steps to Optimize with RIs

1. Leverage Azure Advisor

Azure Advisor provides recommendations tailored to your actual usage, identifying which VM families to reserve, the quantity, and optimal term. It also flags idle resources for rightsizing before purchasing.

2. Choose the Right Reservation Scope

  • Shared Scope: Applies RI discounts across all subscriptions under the same billing account, maximizing utilization.
  • Other Options: Management group, single subscription, or resource group—each alignment offers differing balances of simplicity and detailed chargeback.

Contoso selected Shared Scope to accommodate team growth across multiple subscriptions while keeping cost reporting streamlined.

3. Purchase and Configure RIs Smartly

  • Reserved NC64as T4 v3 instances in East US for high-performance inference (3-year term, upfront payment)
  • Instance Size Flexibility to ensure RIs covered varying VM sizes during model experiments

4. Monitor Usage and Renewals Proactively

  • Setup utilization alerts via Microsoft Cost Management to detect underuse
  • Enabled auto-renewal to prevent unplanned cost spikes at expiration
  • Real-time dashboard monitoring of RI application and savings

5. Combined Strategies

If workloads are less predictable, supplementing RIs with Azure savings plan for compute can offer additional flexibility.

Results and Learnings

  • Significant savings: Predictable GPU costs versus on-demand pricing
  • Continuous performance: No compromise in computational ability
  • Actionable tips:
    • Start by consulting Azure Advisor
    • Scope reservations for broad coverage, aligning with financial policies
    • Enable instance size flexibility
    • Monitor with alerts and dashboards

Why This Matters

For AI and other intensive compute workloads, Azure RIs transform cloud budgeting and resource planning. With careful planning and Azure’s built-in tools, organizations gain cost control and allocate more budget to experimentation and innovation.

Additional Resources

Author: kyleikeda

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