Auto Scaling, Concurrency, and Cost Dynamics
Explore how to configure auto scaling policies and concurrency settings for Amazon SageMaker endpoints. Understand the impact of scaling decisions on latency and cost, compare provisioned and serverless inference, and learn best practices for monitoring and optimizing inference in production. This lesson helps you balance performance and budget while ensuring high availability and responsiveness in ML deployments.
We'll cover the following...
Auto scaling is a control loop. It continuously observes workload metrics, compares them against a target, and adjusts compute capacity to close the gap. For production ML systems, where traffic patterns shift with user behavior, marketing campaigns, or upstream data pipelines, this loop is not optional. It is foundational.
SageMaker integrates with two AWS services to implement this loop:
Amazon CloudWatch collects real-time metrics from your endpoint (invocation counts, model latency, and CPU utilization) and publishes them as time-series data.
Application Auto Scaling consumes these metrics, evaluates them against your defined policies, and issues scaling actions to add or remove instances behind your endpoint.
The SageMaker real-time endpoint itself is the scalable target. It abstracts the underlying instance fleet while exposing configuration knobs for capacity bounds and scaling behavior.
This lesson is structured around three decisions every ML engineer must make when operationalizing inference:
How to configure scaling policies that respond correctly to demand
How concurrency and throughput interact with instance count for different model types
When to choose auto scaling on provisioned endpoints vs. serverless inference
Each decision directly impacts both your costs and the latency percentiles your users experience. Getting scaling right is not a deployment afterthought. It is a core MLOps competency that connects serving infrastructure to the monitoring and retraining stages downstream.
Configuring target-tracking scaling policies
Target-tracking scaling is the recommended approach for SageMaker endpoints because it automates the feedback loop entirely. You specify a target metric value, and Application Auto Scaling continuously adjusts the instance count to maintain it. The primary metric is InvocationsPerInstance, the total number of inference requests received by the endpoint divided by the current number of instances. When this metric exceeds your target, the system scales out by adding instances. When it drops below, the system scales in by removing them.
Setting the right target value requires understanding your model's capacity. If ...