In the ever-evolving world of DevOps, the ability to efficiently scale applications is a crucial component of success. AWS Elastic Container Service (ECS) offers a powerful platform for managing Docker containers, and when it comes to scaling, understanding the right way to do it is paramount. In this blog post, we will explore common scaling policies and limits for AWS ECS, helping you make informed decisions and optimize your containerized workloads.

Understanding AWS ECS Scaling:

Before we dive into the scaling policies and limits, let’s grasp the foundational concepts of AWS ECS scaling:

  1. Service Auto Scaling: AWS ECS allows you to define Service Auto Scaling policies to automatically adjust the number of tasks within your service based on defined criteria. This dynamic scaling helps maintain application availability and performance.

  2. Capacity Providers: Capacity Providers in ECS are responsible for managing the underlying infrastructure. They allow you to specify how your tasks should be spread across different instance types and are crucial for scaling effectively.

Common Scaling Policies:

  1. Target Tracking Scaling Policy: This policy adjusts the desired count of tasks to maintain a target value for a specified metric. For example, you can set up a policy to maintain an average CPU utilization of 60% across your tasks. ECS will automatically adjust the number of tasks to meet this target.

  2. Step Scaling Policy: Step Scaling allows you to increase or decrease the number of tasks in steps based on the value of a specified CloudWatch metric. For example, you could set up a policy to add one task when CPU utilization crosses a certain threshold, and then add more tasks as utilization continues to rise.

  3. Scheduled Scaling Policy: This policy lets you define a schedule for scaling activities. You can schedule tasks to increase or decrease at specific times, like during peak usage hours, ensuring you’re not over-provisioned when demand is low.

  4. Manual Scaling: Sometimes, manual scaling might be necessary, particularly for tasks that cannot be easily predicted or controlled by automatic policies. You can manually adjust the number of tasks based on your observations or business requirements.

Limits to Keep in Mind:

When scaling your AWS ECS clusters, it’s essential to be aware of certain limits and constraints to avoid potential bottlenecks and performance issues:

  1. Service Limits: AWS ECS has limits on the maximum number of services, tasks, and task definitions you can create within an AWS account. Be sure to check these limits and request increases if necessary.

  2. Cluster Scaling: The maximum number of instances and tasks you can run in an ECS cluster depends on the instance types, instance limits, and regional restrictions. Make sure your cluster’s capacity provider configuration aligns with your scaling requirements.

  3. Auto Scaling Groups: When using Auto Scaling Groups in ECS, you need to ensure that your launch configuration and scaling policies are well-defined and optimized for your workload.

  4. CloudWatch Alarms: Use CloudWatch Alarms judiciously. Overuse of alarms can lead to unexpected scaling actions, while underuse can result in missed scaling opportunities.


Scaling AWS ECS the right way is essential for maintaining application performance and cost-efficiency. By understanding common scaling policies, such as target tracking, step scaling, and scheduled scaling, and keeping track of key limits and constraints, you can ensure that your ECS workloads are optimized for both high availability and cost-effectiveness.

Remember that every application has unique scaling requirements, so it’s crucial to fine-tune your ECS scaling strategy to align with your specific use case. Keep an eye on metrics, monitor your clusters, and regularly assess your scaling policies to ensure your AWS ECS deployment operates smoothly and efficiently.

By following these best practices and staying informed, you’ll be better equipped to harness the full potential of AWS ECS for your containerized applications.