Scaling Down: When and How to Reduce AWS Resources

One of the biggest advantages of cloud computing is its elasticity.

You can scale resources up when demand spikes and scale down when traffic drops.

While many businesses focus on scaling up to handle growth, scaling down is just as important, especially when optimizing costs.

AWS provides multiple ways to automatically reduce resources during off-peak times, ensuring you only pay for what you actually need.

In this post, let’s explore when to scale down and how to implement it effectively.

When to Scale Down

Scaling down is beneficial when your workload experiences predictable traffic usage.

Some common scenarios include:

• Business Hours vs. Off-Hours: If your application experiences peak usage during the day and lower traffic at night, you should reduce compute resources overnight.

• Weekday vs. Weekend Demand: E-commerce and SaaS applications sometimes have lower activity on weekends, making them ideal to scale down.

• Seasonal Demand: Retail businesses see spikes during holiday seasons, while other periods usually require fewer resources.

• Batch Processing Jobs: If your workloads run at specific intervals (like nightly reports or backups), resources should be minimized when they are not in use.

How to Scale Down AWS Resources

1. AWS Auto Scaling

AWS Auto Scaling helps adjust the number of running instances or services based on demand. While it’s commonly used for scaling up, it can also reduce instances when traffic drops.

• EC2 Auto Scaling Groups: Define scaling policies based on CPU utilization, network traffic, or custom CloudWatch metrics. Set a lower minimum instance count for off-peak hours.

• Services Auto Scaling: Scale services like DynamoDB, ECS, and Lambda provisioned concurrency based on request volume.

• Scheduled Scaling: If demand follows a predictable pattern, use scheduled actions in Auto Scaling Groups to reduce instance counts during low-traffic periods.

2. AWS Lambda for Serverless Workloads

For serverless applications, Lambda automatically scales based on demand. However, you can optimize costs further:

Reduce Provisioned Concurrency: If you use provisioned concurrency for performance reasons, set lower values during off-peak hours.

Dynamically Adjust Timeout and Memory: Optimize Lambda functions by reducing memory allocation when processing lighter workloads.

3. Custom Scripts for Manual Scaling

If AWS’s built-in scaling tools don’t fully meet your needs, custom scripts provide more flexibility:

• AWS CLI or SDK Scripts: Use cron jobs or scheduled Lambda functions to modify instance counts, database read/write capacity, or S3 lifecycle policies.

• Infrastructure-as-Code (IaC): Automate resource scaling changes using AWS CDK, CloudFormation, or Terraform.

• Stop Non-Essential Resources: Use scripts to stop EC2 instances, RDS databases, or containers when they’re not needed. (use AWS Instance Scheduler for this).

4. Spot and Savings Plans for Cost Optimization

While scaling down is effective, cost savings can be further improved by using:

• Spot Instances: Run non-critical workloads on EC2 Spot Instances, which are significantly cheaper than On-Demand instances.

• Savings Plans & Reserved Instances: If scaling down isn’t always possible, use discounted pricing for predictable workloads.

One Tip To Save Costs

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Curated Articles

1. “AWS Instance Scheduler: Everything you Need to Know and Tutorial“. Adam Novotný. August 12, 2022. https://www.stormit.cloud/blog/aws-instance-scheduler/