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Using Graviton instances in auto-scaling groups for improved AWS cost efficiency

Did you know that switching to AWS Graviton instances can reduce your compute costs by up to 40% while delivering 20% better performance compared to comparable x86 instances? For organizations running auto-scaling workloads, combining Graviton instances with intelligent auto-scaling group configurations creates a powerful cost optimization strategy that can transform your cloud economics.

AWS Graviton processors, built on ARM-based architecture, offer exceptional price-performance benefits for most workloads. When properly integrated into auto-scaling groups, these instances can significantly reduce your cloud spending while maintaining or even improving application performance. Think of it as upgrading your entire fleet to more fuel-efficient vehicles—the savings multiply with every mile driven.

Understanding Graviton instances in auto-scaling contexts

AWS Graviton instances represent a paradigm shift in cloud computing economics. These ARM-based processors excel at handling web servers, containerized applications, microservices, and data processing workloads—exactly the types of applications that benefit most from auto-scaling.

The key advantage lies in their efficiency. Graviton3 instances deliver up to 25% better compute performance and consume up to 60% less energy than comparable x86 instances. When multiplied across an auto-scaling group that might launch dozens or hundreds of instances during peak traffic, these savings compound dramatically.

Consider a real-world scenario: An e-commerce platform that typically runs 20 instances during normal hours but scales to 100 instances during flash sales. By switching the entire auto-scaling group to Graviton instances, the company could save $800-1,200 per day during peak events—money that flows directly to the bottom line.

However, the real magic happens when you configure mixed instance types within your auto-scaling groups. This approach allows you to leverage Graviton instances as your primary choice while maintaining x86 instances as fallbacks, ensuring both cost efficiency and availability even when specific instance types face capacity constraints.

A detailed illustration showing an AWS auto-scaling group composed of server racks, with the majority labeled as ARM-based Graviton instances (depicted in green or with ARM/Graviton logos) and a few fallback x86 instances (depicted in blue). The diagram highlights the cost savings by overlaying clear dollar signs above the Graviton instances and arrows indicating scaling up and down during variable traffic.

Setting up auto-scaling groups with Graviton instances

Creating launch templates for mixed instance types

Your first step involves creating launch templates that support both Graviton and x86 instances. Start by creating a primary launch template for Graviton instances using the AWS CLI or console:

{
  "LaunchTemplateName": "graviton-primary-template",
  "LaunchTemplateData": {
    "ImageId": "ami-0123456789abcdef0",
    "InstanceType": "m7g.large",
    "SecurityGroupIds": ["sg-12345678"],
    "UserData": "base64-encoded-startup-script",
    "IamInstanceProfile": {
      "Name": "EC2InstanceProfile"
    }
  }
}

Create a secondary template for x86 instances as your fallback option. This ensures your auto-scaling group can still launch instances if Graviton capacity becomes limited in your target Availability Zones—a crucial safety net that prevents scaling failures during high-demand periods.

A step-by-step visual workflow illustrating mixed instance policy configuration in AWS: Side-by-side panels showing the creation of a Graviton launch template, an x86 launch template, and then configuring a mixed auto-scaling group that prioritizes Graviton instances. Each panel is labeled, with icons representing each AWS resource, and highlights the seamless fallback mechanism if Graviton capacity is unavailable.

Critical consideration: Ensure your AMI supports ARM64 architecture for Graviton instances. Amazon Linux 2 and most modern distributions provide native ARM64 support, but always validate compatibility before production deployment.

Configuring the auto-scaling group with mixed instance policy

The mixed instances policy is where the optimization magic happens. Configure your auto-scaling group to prioritize Graviton instances while maintaining flexibility:

{
  "AutoScalingGroupName": "cost-optimized-asg",
  "MinSize": 2,
  "MaxSize": 50,
  "DesiredCapacity": 5,
  "VPCZoneIdentifier": ["subnet-12345678", "subnet-87654321"],
  "MixedInstancesPolicy": {
    "LaunchTemplate": {
      "LaunchTemplateSpecification": {
        "LaunchTemplateName": "graviton-primary-template",
        "Version": "$Latest"
      },
      "Overrides": [
        {
          "InstanceType": "m7g.large",
          "WeightedCapacity": "1"
        },
        {
          "InstanceType": "m7g.xlarge",
          "WeightedCapacity": "2"
        },
        {
          "InstanceType": "m6i.large",
          "WeightedCapacity": "1"
        },
        {
          "InstanceType": "m6i.xlarge",
          "WeightedCapacity": "2"
        }
      ]
    },
    "InstancesDistribution": {
      "OnDemandPercentage": 20,
      "SpotAllocationStrategy": "diversified",
      "OnDemandAllocationStrategy": "prioritized"
    }
  }
}

This configuration prioritizes m7g Graviton instances while keeping m6i x86 instances as alternatives. The “prioritized” allocation strategy ensures your auto-scaling group attempts to launch Graviton instances first, falling back to x86 options only when necessary.

Instance lifecycle management and optimization strategies

Understanding the auto-scaling lifecycle with Graviton instances

The auto-scaling lifecycle with Graviton instances follows the same pattern as traditional instances, but with important optimization considerations:

  1. Pending: Auto-scaling attempts to launch your preferred Graviton instance type first
  2. InService: Instances become available to serve traffic after passing health checks
  3. Health checking: Regular health checks ensure instances remain functional
  4. Scaling decisions: Based on CloudWatch metrics and scaling policies
  5. Terminating: Oldest instances typically terminate first during scale-down events

The key difference lies in the launch phase. When Graviton instances aren’t available in a specific Availability Zone, your auto-scaling group automatically falls back to x86 alternatives, maintaining availability without manual intervention. This seamless failover prevents the dreaded scenario where your application can’t scale during peak demand.

Optimizing for cost and performance

Implement these battle-tested strategies to maximize your Graviton auto-scaling efficiency:

Use spot instances strategically: Graviton spot instances often offer even deeper discounts than their x86 counterparts. Configure your instances distribution to use 70-80% spot instances for non-critical workloads. A media processing company reduced their compute costs by 60% by running batch jobs on Graviton spot instances within auto-scaling groups.

Right-size your instances: Graviton instances often require different sizing than x86 equivalents due to their superior performance characteristics. Monitor your CPU and memory utilization patterns using CloudWatch metrics and adjust instance types accordingly. Many organizations find they can use smaller Graviton instances to achieve the same performance as larger x86 instances.

Leverage placement groups: For latency-sensitive applications, use cluster placement groups to ensure your Graviton instances launch close together within the same Availability Zone, reducing network latency by up to 50%.

Best practices for mixed instance type configurations

Balancing cost optimization with availability

The most effective mixed instance policies balance cost savings with operational reliability. Configure your auto-scaling group with 3-4 instance types: 2 Graviton variants and 2 x86 alternatives. This approach ensures you can always scale, even during capacity constraints that affect specific instance families.

Consider using instance families that offer similar performance characteristics. For example, pair m7g instances with m6i instances, or c7g with c6i instances. This consistency simplifies application tuning and performance monitoring—you won’t encounter surprising performance variations that could impact user experience.

Pro tip: Use AWS Instance Advisor to identify optimal instance type combinations based on your actual workload patterns.

Monitoring and alerting for mixed fleets

Implement CloudWatch dashboards that track your instance type distribution. Monitor critical metrics like:

  • Percentage of Graviton vs x86 instances currently running
  • Cost per hour across different instance types
  • Performance metrics broken down by instance family
  • Spot instance interruption rates

Set up CloudWatch alarms when your Graviton instance percentage drops below target thresholds, indicating potential capacity issues or configuration problems. This proactive monitoring prevents cost optimization degradation before it impacts your budget.

Application compatibility considerations

Most modern applications run seamlessly on Graviton instances, but some require minor adjustments. Ensure your AMIs support ARM64 architecture and test your applications thoroughly. Containerized workloads typically transition smoothly since container orchestration platforms like Amazon EKS handle architecture differences transparently.

Legacy applications might need additional validation. Create separate launch templates for applications that aren’t Graviton-compatible, allowing you to optimize compatible workloads while maintaining support for legacy systems without disruption.

Real-world example: A financial services company discovered that 80% of their microservices worked flawlessly on Graviton instances, while their legacy reporting system required x86 instances. They used mixed instance policies to optimize costs where possible while maintaining compatibility across their entire application portfolio.

Cost optimization strategies and real-world savings

Organizations implementing Graviton instances in auto-scaling groups typically see 25-40% cost reductions compared to equivalent x86 configurations. According to AWS case studies, these savings amplify during high-traffic periods when auto-scaling groups launch additional instances.

Consider a web application that scales from 10 instances during low traffic to 50 instances during peak periods. With Graviton instances priced 20% lower than comparable x86 instances, the daily savings during peak traffic can reach hundreds of dollars for medium-sized applications—potentially $50,000-100,000 in annual savings for typical enterprise workloads.

The savings compound when combined with spot instances. Graviton spot instances often trade at even steeper discounts compared to on-demand pricing, making them ideal for fault-tolerant workloads like batch processing, CI/CD pipelines, and development environments.

For organizations serious about maximizing these cost optimizations across their entire AWS infrastructure, automated solutions like Hykell can identify additional optimization opportunities beyond just instance selection, potentially doubling your overall cloud cost savings through comprehensive infrastructure analysis and automated optimization.

Advanced configuration patterns

Time-based scaling with Graviton preference

Implement scheduled scaling policies that heavily favor Graviton instances during predictable traffic patterns. Configure your auto-scaling group to pre-scale with Graviton instances before traffic spikes, ensuring maximum cost efficiency during high-demand periods.

For example, an online learning platform schedules Graviton instance scaling 30 minutes before peak usage hours (6-9 PM), ensuring their cost-optimized infrastructure is ready for the evening rush of students accessing courses.

Multi-AZ distribution strategies

Design your mixed instance policy to distribute Graviton instances across multiple Availability Zones. This approach improves both availability and cost optimization by leveraging Graviton capacity wherever it’s available. Use the VPCZoneIdentifier parameter to specify subnets across multiple AZs, ensuring your auto-scaling group can launch instances in any zone with available Graviton capacity.

Integration with AWS Compute Optimizer

Leverage AWS Compute Optimizer recommendations to fine-tune your Graviton instance selection. The service analyzes your actual usage patterns and recommends optimal instance types, helping you identify when smaller Graviton instances can replace larger x86 instances without performance degradation.

Graviton instances in auto-scaling groups represent one of the most effective AWS cost optimization strategies available today. When properly configured with mixed instance policies, organizations achieve significant cost reductions while maintaining operational flexibility and performance standards. The key lies in thoughtful configuration, continuous monitoring, and gradual migration strategies that prioritize compatibility alongside cost savings.

Start by implementing Graviton instances in your least critical auto-scaling groups, validate performance characteristics, then gradually expand to more critical workloads as confidence builds. The combination of Graviton’s inherent cost advantages with auto-scaling’s demand-driven efficiency creates a powerful foundation for sustainable cloud cost optimization.

Ready to unlock these savings? Begin with a small pilot auto-scaling group using the configurations outlined above, monitor the results for two weeks, then scale your optimization strategy across your entire infrastructure once you’ve validated the performance and cost benefits.