Scale Down to Zero in Amazon SageMaker Inference is now available. It helps users save money by automatically reducing the number of instances running their AI models when they’re not in use.
This is particularly useful for applications that don’t have constant traffic, such as chatbots, content moderation systems, and other types of generative AI.
With Scale Down to Zero, you can set up your SageMaker inference endpoints to scale down to zero instances during periods of inactivity and then quickly scale back up when traffic increases again.
This feature is ideal for scenarios where traffic is unpredictable, like applications with varying workloads, or for environments used for development and testing. By automatically adjusting the number of instances based on demand, you only pay for the resources you need, which can lead to significant cost savings.
Setting up Scale Down to Zero is simple. You can use tools like the AWS SDK for Python (Boto3), the SageMaker Python SDK, or the AWS CLI to configure auto-scaling.
The process involves creating an endpoint with managed instance scaling, setting up rules for when to scale, and using CloudWatch alarms to trigger these scaling actions. Once configured, the system automatically handles the scaling for you without any manual intervention.
This new feature is now available in all AWS regions that support Amazon SageMaker, making it easier than ever to optimize your costs when running AI models
