Azure Advisor identifies over 35% of Azure VMs as underutilized, using a 15% average CPU over 14 days threshold. This underutilization means paying for significantly more resources than are actively used, resulting in substantial waste. For instance, a Standard_D8s_v5 VM costing $277/month at 12% CPU effectively costs $288 per utilized vCPU-month, far exceeding the market rate. Right-sizing this to a Standard_D2s_v5 (2 vCPUs) reduces the cost to $70/month, saving $207 per instance without impacting performance.
Underutilization stems from three main causes: peak provisioning for expected spikes that never materialize, workload decline where services reduce traffic over time, and environment proliferation from uncleaned non-production VMs. Effective right-sizing requires using a 14-day p95 CPU and memory baseline, applying a 60% headroom rule, and validating changes in a staging environment before production. For example, a D8s_v5 with 18% p95 CPU could be right-sized to a D2s_v5, saving $207 monthly.
Further cost reductions can be achieved by leveraging discount stacking. Reserved Instances save 36-63% for stable workloads, Azure Spot VMs offer up to 90% for interruptible tasks, and Azure Hybrid Benefit reduces Windows Server VM costs by up to 49%. Combining a 3-year Reserved Instance with Hybrid Benefit can yield savings of up to 74% for Windows workloads.
Non-production VMs are prime targets for optimization. Scheduling these VMs to run only during business hours can reduce costs by 73%. Downgrading their disks from Premium SSD to Standard SSD further cuts storage expenses by 60%. A comprehensive strategy for non-production environments involves scheduling, right-sizing, and using standard SSDs, which can reduce annual costs by over 75%.
A recommended 90-day cost reduction plan starts with baselining metrics, then focuses on non-production VM scheduling, cleanup, and right-sizing due to their low risk. Subsequently, production VMs are right-sized with validation. Finally, reservations, Spot VMs, and Hybrid Benefit are applied to lock in optimized costs. This structured approach ensures significant savings by systematically addressing and eliminating wasted capacity across the Azure VM fleet.
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