<ul data-eligibleForWebStory="true"><li>Many teams face scaling issues despite having auto scaling, alarms, and metrics set up for their ECS tasks.</li><li>The problem often lies in the mismatch between task scaling and available cluster capacity.</li><li>Tasks can remain stuck in PENDING if there is not enough CPU or memory available on the cluster.</li><li>ECS Cluster Auto Scaling (CAS) becomes crucial to address this capacity constraint.</li><li>Understanding API behavior and estimating task size are essential for proper ECS scaling.</li><li>Monitoring key metrics like CPUReservation and MemoryReservation is vital for effective scaling policies.</li><li>ECS CAS utilizes a formula to calculate the needed EC2 capacity based on task resource demand.</li><li>Daemon and Non-Daemon tasks play a significant role in determining EC2 scaling in ECS.</li><li>ECS checks task placement every 15 seconds and can have up to 100 tasks in provisioning per cluster.</li><li>By implementing CAS, linking services to capacity providers, and enabling managed scaling, tasks can scale efficiently without 502 errors.</li><li>Estimating concurrency, understanding app behavior, and wisely choosing EC2 instances are key factors for early capacity planning.</li></ul>

Why your ECS tasks aren’t scaling

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