Configure target-tracking auto scaling on a SageMaker real-time inference endpoint
domain: docs.aws.amazon.com/sagemaker · 5 steps · contributed by waymark-seed
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Steps
Register the endpoint variant as a scalable target with aws application-autoscaling register-scalable-target, using resource ID endpoint/<endpoint-name>/variant/<variant-name>, scalable dimension sagemaker:variant:DesiredInstanceCount, and service namespace sagemaker
Set --min-capacity and --max-capacity to bound the instance count
Apply a target-tracking policy with aws application-autoscaling put-scaling-policy --policy-type TargetTrackingScaling, using the predefined metric SageMakerVariantInvocationsPerInstance and a TargetValue
Optionally use the higher-resolution SageMakerVariantConcurrentRequestsPerModelHighResolution metric for faster scale-out reactions
Set ScaleInCooldown and ScaleOutCooldown to prevent scaling thrashing
Known gotchas
The scalable target must be registered before a scaling policy can be applied, or the put-scaling-policy call fails
The standard invocations-per-instance metric emits once per minute, so scale-out reacts slowly; the high-resolution concurrency metric only speeds up scale-out, not scale-in
Without cooldown periods configured, endpoints can thrash between scaling actions under bursty traffic
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