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# Amazon EC2 Auto Scaling now supports batch instance termination

**[AWS](https://daily.dev/sources/aws)** · 1 min read · 0 upvotes · 0 comments

## Summary

Amazon EC2 Auto Scaling now allows terminating up to 100 instances in a single API call via the TerminateInstanceInAutoScalingGroup API, cutting down the number of calls needed to scale down groups. All instances in a batch are validated atomically before termination, and existing behaviors like lifecycle hooks and load balancer connection draining are preserved. The feature targets workloads that scale down rapidly, such as AI/ML training jobs, container orchestrators, and event-driven architectures. It's available in all AWS Regions at no extra cost.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://aws.amazon.com/about-aws/whats-new/2026/08/amazon-ec2-auto-scaling-batch-termination>

## Questions this post answers

### How many EC2 instances can I terminate in a single Auto Scaling API call now?

You can terminate up to 100 instances in one call using the TerminateInstanceInAutoScalingGroup API. This batch termination feature reduces the number of API calls needed to scale down Auto Scaling groups quickly, and it validates all instances atomically before termination begins while preserving lifecycle hooks and load balancer connection draining. It is available in all AWS Regions at no extra cost.

_Track AWS Auto Scaling API changes like this on daily.dev before they affect your scale-down logic._

### Does batch terminating EC2 instances skip lifecycle hooks or load balancer draining?

No, existing Auto Scaling behaviors such as lifecycle hooks and load balancer connection draining are preserved for each instance in a batch termination call. This applies even when terminating up to 100 instances at once through the TerminateInstanceInAutoScalingGroup API, so workloads relying on graceful shutdown are unaffected.

_daily.dev helps infra engineers stay current on Auto Scaling behavior changes affecting deployment safety._

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