Tips for Reducing Bills with Monitoring and Right-Sizing VMs

Cloud platforms like AWS, Azure, and Google Cloud have revolutionized IT infrastructure, offering unprecedented scalability and flexibility. However, with this freedom comes a hidden challenge: overprovisioned virtual machines (VMs) can silently inflate your monthly bills. Organizations often pay for resources they barely use, while performance issues remain under the radar.

Real-world experience shows that 10–30% of cloud spend in enterprises can be attributed to idle or oversized VMs, unnecessary storage, and poorly configured autoscaling policies. The solution lies in continuous monitoring, proactive right-sizing, and disciplined resource governance. This article dives into practical strategies that IT teams can apply to reduce costs while maintaining performance and reliability.

What is VM Right-Sizing?

VM right-sizing is the process of adjusting a VM’s CPU, memory, and storage allocation to match actual workload demands. The goal is to eliminate waste without impacting application performance.

Benefits of Right-Sizing

• Reduce cloud spend: Pay only for what is needed
• Improve system efficiency: Fewer resources wasted
• Free up capacity: Reallocate underutilized resources to critical workloads
• Prevent performance bottlenecks: Avoid over/underprovisioning

Right-sizing is both data-driven and iterative. Guesswork or blanket downsizing can lead to outages or degraded application experience.

Step 1: Monitor Usage Continuously

You cannot optimize what you do not measure. Continuous monitoring provides visibility into actual resource utilization.

Key Metrics to Track

• CPU Utilization (%): VMs running below 20% consistently may be oversized
• Memory Usage: Low RAM utilization suggests potential downsizing
• Disk IOPS and Throughput: Critical for storage-heavy applications like databases
• Network Traffic: High variability indicates the need for autoscaling
• Uptime Patterns: Some VMs run 24/7 unnecessarily

Best Practices

• Monitor usage over at least 30 days to capture peak and idle trends
• Include weekends and holidays; non-production VMs often sit idle during these times
• Use aggregated averages and maximums to identify safe right-sizing thresholds
• Integrate monitoring with dashboards or cloud-native tools for historical analysis

Expert Insight: Some enterprises underestimate low-impact workloads, such as background jobs or infrequently used test VMs, which can contribute to significant monthly costs if left running.

Step 2: Identify Underutilized VMs

Not every VM is optimized for cost. Look for:

• CPU consistently under 15–20%
• Memory utilization below 30%
• Minimal disk and network activity
• Development/test environments left running outside work hours
• Instances sized for peak demand but operating at small, predictable loads

Real-World Tip: Label and categorize VMs during discovery to separate production, test, dev, and PoC workloads. This segmentation makes right-sizing and automation easier.

Step 3: Select the Right VM Size

Cloud providers offer multiple VM families optimized for compute, memory, storage, or burstable workloads. Matching the right instance to your workload is critical.

Approaches

• Downsize overprovisioned VMs: Example, moving from a D4 to D2 instance on Azure
• Switch VM families: For low-peak workloads, consider burstable instances (AWS T-series, Azure B-series)
• Use autoscaling: Implement scale sets or managed instance groups to grow/shrink resources based on demand
• Target 70–80% of peak usage: Avoid sizing for absolute peaks unless unavoidable

Insight: Enterprises often oversize VMs “just in case.” Data-driven resizing ensures cost savings without sacrificing performance.

Step 4: Schedule Power-Downs for Idle VMs

Non-production workloads often run around the clock unnecessarily.

Strategies

• Automate shutdown during off-hours (nights, weekends)
• Leverage serverless compute or containers for intermittent workloads
• Apply tags like non-production or auto-off for easy management
• Combine with startup scripts for predictable uptime when needed

Expert Tip: For short-lived workloads, ephemeral VMs or containerized deployments can reduce both compute costs and management overhead.

Step 5: Clean Up Unused Disks, IPs, and Snapshots

Idle storage can be as expensive as oversized VMs.

• Detached disks: Costs continue even if the VM is deleted
• Reserved public IPs: Charges accrue when not actively used
• Snapshots and backups: Accumulate over time if not cleaned

Automation Recommendation: Use scripts, cloud-native rules, or policies to identify and delete unused resources, reducing hidden costs.

Step 6: Use Cost and Usage Alerts

Alerts help enforce accountability and prevent overspending.

• Trigger alerts when:
  • VM usage falls below thresholds for a prolonged period
  • Costs for a service or region exceed budgets
  • Unexpected spikes in resource utilization occur
• Integrate with automation tools to:
  • Scale down resources
  • Tag underutilized VMs for review

Pro Tip: Establish a chargeback model for teams using cloud resources to encourage cost-conscious behavior.

Step 7: Implement Tagging for Cost Tracking

Proper tagging is crucial for visibility, governance, and automation.

• Common tags:
  • Environment: Production, Dev, Test
  • Owner: Team name or individual
  • CostCenter: Finance, IT, Marketing
  • Shutdown: Scheduled auto-off policies
• Tags allow:
  • Accurate cost attribution
  • Automated monitoring and cleanup
  • Easier reporting for finance and management

Advanced Strategies for Cost Optimization

• Reserved Instances (RI) or Savings Plans: Only after right-sizing to avoid committing to oversized VMs
• Serverless alternatives: Lambda, Azure Functions, or container-based workloads reduce idle spend
• Monthly optimization reviews: Workloads evolve; your VM sizing should too
• Infrastructure as Code (IaC): Deploy and manage scalable, right-sized workloads programmatically from day one

Expert Insight: Some organizations achieve 20–40% cost reductions simply by combining right-sizing with automated scheduling and cleanup scripts.

Common Mistakes to Avoid

• Sizing for peak usage without autoscaling
• Ignoring hidden costs such as storage, IPs, and snapshots
• Treating all workloads identically—not all VMs require production-level capacity
• Assuming initial sizing is permanent
• Overengineering test/dev environments leading to unnecessary costs

Optimize Continuously, Don’t Set and Forget

Cloud cost optimization is an ongoing practice, not a one-time activity. With monitoring, data-driven right-sizing, automated shutdowns, and resource cleanup, organizations can reduce unnecessary spend while maintaining optimal performance.

Key Takeaways for Enterprises:

• Collect continuous performance data across all VMs
• Right-size instances based on real workload patterns
• Automate shutdowns, cleanup, and alerts
• Implement tagging and governance for visibility
• Periodically review workloads and scaling strategies

The most cost-efficient cloud environments evolve with usage patterns, leveraging automation and continuous optimization to balance performance and expenses. By adopting these strategies, organizations can achieve significant cloud savings without compromising reliability or service quality.

Daniel Lutton

From my early days on the helpdesk through roles as a service desk manager, systems administrator, and network engineer, I’ve spent more than 25 years in the IT world. As I transition into cyber security, my goal is to make tech a little less confusing by sharing what I’ve learned and helping others wherever I can.