Which scaling policy type should I use?

Target tracking is the simplest and works well for most workloads — you set a target (e.g., 50% CPU) and Auto Scaling handles the math. Step scaling gives you more control with discrete scaling actions at different alarm thresholds, useful when you need asymmetric scale-out vs scale-in behavior. Predictive scaling analyzes 14 days of historical data to forecast future demand and schedule capacity changes in advance. For best results, combine predictive scaling (for known patterns) with target tracking (for unexpected demand).

What is the difference between cooldown and warm-up?

Cooldown is a period after a scaling action during which Auto Scaling ignores further alarm triggers, preventing rapid oscillation. Warm-up (instance warm-up) tells Auto Scaling how long a new instance takes to reach steady state — during warm-up, the instance is not counted toward aggregate metrics. Setting warm-up too low means metrics spike down as unready instances are counted, triggering unnecessary scale-out. Setting it too high delays metric stabilization. Typical warm-up values range from 60 to 300 seconds depending on application startup time.

Auto Scaling Policy Builder

ComputeAWS

Build Auto Scaling target tracking, step scaling, and predictive scaling policies.

Auto Scaling Configuration

Build Auto Scaling target tracking, step scaling, and predictive scaling policies.

Required Fields

AutoScalingGroupNamePolicyNamePolicyType

{
  "AutoScalingGroupName": "prod-web-asg",
  "PolicyName": "cpu-target-tracking",
  "PolicyType": "TargetTrackingScaling",
  "TargetTrackingConfiguration": {
    "PredefinedMetricSpecification": {
      "PredefinedMetricType": "ASGAverageCPUUtilization"
    },
    "TargetValue": 60.0,
    "ScaleInCooldown": 300,
    "ScaleOutCooldown": 60,
    "DisableScaleIn": false
  },
  "StepScalingPolicies": [
    {
      "PolicyName": "scale-out-high-cpu",
      "PolicyType": "StepScaling",
      "AdjustmentType": "ChangeInCapacity",
      "StepAdjustments": [
        {
          "MetricIntervalLowerBound": 0,
          "MetricIntervalUpperBound": 20,
          "ScalingAdjustment": 1
        },
        {
          "MetricIntervalLowerBound": 20,
          "ScalingAdjustment": 3
        }
      ],
      "Cooldown": 120,
      "MetricAggregationType": "Average"
    }
  ],
  "PredictiveScaling": {
    "PolicyName": "predictive-cpu",
    "MetricSpecifications": [
      {
        "TargetValue": 50,
        "PredefinedMetricPairSpecification": {
          "PredefinedMetricType": "ASGCPUUtilization"
        }
      }
    ],
    "Mode": "ForecastAndScale",
    "SchedulingBufferTime": 300
  }
}

Generated Output

Output will appear here...

About This Tool

Auto Scaling policies determine how and when EC2 Auto Scaling groups add or remove instances in response to demand. AWS offers three policy types: target tracking (maintain a metric at a target value), step scaling (scale in discrete steps based on alarm thresholds), and predictive scaling (use machine learning to forecast demand and pre-scale). Choosing the right policy type and configuring the scaling parameters correctly is critical for balancing performance with cost. The Auto Scaling Policy Builder helps you configure all three policy types with proper metric selections, cooldown periods, and scaling adjustments.

When to Use This Tool

•Setting up target tracking policies that maintain average CPU utilization at 60% across an Auto Scaling group
•Configuring step scaling policies with multiple thresholds for aggressive scale-out and gradual scale-in
•Building predictive scaling policies that pre-warm capacity before recurring daily traffic spikes
•Combining target tracking with predictive scaling for applications with both predictable and unpredictable traffic patterns

Frequently Asked Questions

Which scaling policy type should I use?: Target tracking is the simplest and works well for most workloads — you set a target (e.g., 50% CPU) and Auto Scaling handles the math. Step scaling gives you more control with discrete scaling actions at different alarm thresholds, useful when you need asymmetric scale-out vs scale-in behavior. Predictive scaling analyzes 14 days of historical data to forecast future demand and schedule capacity changes in advance. For best results, combine predictive scaling (for known patterns) with target tracking (for unexpected demand).
What is the difference between cooldown and warm-up?: Cooldown is a period after a scaling action during which Auto Scaling ignores further alarm triggers, preventing rapid oscillation. Warm-up (instance warm-up) tells Auto Scaling how long a new instance takes to reach steady state — during warm-up, the instance is not counted toward aggregate metrics. Setting warm-up too low means metrics spike down as unready instances are counted, triggering unnecessary scale-out. Setting it too high delays metric stabilization. Typical warm-up values range from 60 to 300 seconds depending on application startup time.

Related Learning Guides

EC2 Instance Types Explained24 min read

Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.

Auto Scaling Policy Builder

ComputeAWS

Build Auto Scaling target tracking, step scaling, and predictive scaling policies.

Auto Scaling Configuration

Build Auto Scaling target tracking, step scaling, and predictive scaling policies.

Required Fields

AutoScalingGroupNamePolicyNamePolicyType

Generated Output

Output will appear here...

About This Tool

When to Use This Tool

•Setting up target tracking policies that maintain average CPU utilization at 60% across an Auto Scaling group
•Configuring step scaling policies with multiple thresholds for aggressive scale-out and gradual scale-in
•Building predictive scaling policies that pre-warm capacity before recurring daily traffic spikes
•Combining target tracking with predictive scaling for applications with both predictable and unpredictable traffic patterns

Frequently Asked Questions

Which scaling policy type should I use?: Target tracking is the simplest and works well for most workloads — you set a target (e.g., 50% CPU) and Auto Scaling handles the math. Step scaling gives you more control with discrete scaling actions at different alarm thresholds, useful when you need asymmetric scale-out vs scale-in behavior. Predictive scaling analyzes 14 days of historical data to forecast future demand and schedule capacity changes in advance. For best results, combine predictive scaling (for known patterns) with target tracking (for unexpected demand).
What is the difference between cooldown and warm-up?: Cooldown is a period after a scaling action during which Auto Scaling ignores further alarm triggers, preventing rapid oscillation. Warm-up (instance warm-up) tells Auto Scaling how long a new instance takes to reach steady state — during warm-up, the instance is not counted toward aggregate metrics. Setting warm-up too low means metrics spike down as unready instances are counted, triggering unnecessary scale-out. Setting it too high delays metric stabilization. Typical warm-up values range from 60 to 300 seconds depending on application startup time.

Related Learning Guides

EC2 Instance Types Explained24 min read