Amazon SageMaker · Finder + guided fix
Sagemaker Rightsize Instances
RightsizeInstances
Right-size overprovisioned SageMaker instances for notebooks, training, and inference. CloudFix identifies oversized instances and recommends optimal configs.
What happens, in order.
Finder · read-only
Finds the opportunity
Scans your connected accounts with a read-only role. It can see resource metadata and usage; it can’t change anything.
You
Review in Recommendations
It appears in your Recommendations view with what it would save.
Guided fix
You make the change
You apply it yourself, following the fix’s step-by-step instructions; the write-up below explains it.
Finder role
Read-only. It can see resource metadata and usage. It can’t change anything.
Fixer role
Not used here: this one is a guided fix, so the change is yours to make.
Saves by
Amazon SageMaker instances are often overprovisioned, leading to unnecessary costs. CloudFix finds notebooks, training jobs and endpoints whose capacity consistently exceeds actual usage and recommends a smaller instance size that closely matches the workload, reducing overprovisioning waste.
Amazon SageMaker instances, used for notebooks, training, and inference endpoints, are frequently overprovisioned, leading to unnecessary costs. CloudFix identifies opportunities to rightsize these instances by comparing their actual usage, as reported in Amazon CloudWatch, against the capacity you pay for, and recommends downsizing where instances are consistently overprovisioned, helping align costs with actual workload needs.
Manual Fix Required
CloudFix identifies potential rightsizing opportunities but does not automatically resize SageMaker instances. Modifying instance types for notebooks, training jobs, or endpoints requires careful planning and manual execution by the user to ensure compatibility and avoid disrupting workflows or impacting performance negatively.
Overview
Problem Statement
Selecting the optimal instance type and size for SageMaker notebooks, training jobs, and inference endpoints can be challenging. Often, instances are chosen with excess capacity to handle peak loads or based on initial estimates, leading to consistent underutilization and wasted expenditure during periods of lower demand.
Solution Identification
CloudFix uses your cost and usage data and the instances’ own CloudWatch utilization to find SageMaker instances whose capacity consistently exceeds what the workload needs, and recommends a smaller size in the same family where the savings are worthwhile. The result is a data-driven suggestion for you to act on.
AWS Services Affected
| Service | Icon |
|---|---|
| Amazon SageMaker |
 |
| Amazon CloudWatch |
 |
How CloudFix Identifies the Opportunity
CloudFix uses your cost and usage data and the utilization metrics SageMaker already publishes to Amazon CloudWatch to find notebooks, training jobs and endpoints that are consistently overprovisioned. Finding is read-only. Each opportunity appears in your Recommendations with a suggested smaller instance and the estimated savings, and you decide whether to make the change. You can exclude resources you don’t want CloudFix to recommend changes for.
Manual Fix Steps
After CloudFix identifies a SageMaker instance rightsizing opportunity:
- Review Recommendation & Metrics: Examine the specific instance identified, the recommended smaller size, and the supporting utilization data provided by CloudFix.
- Assess Workload Impact: Consider the nature of the workload running on the instance. Is the observed peak usage representative, or are there occasional, critical bursts that require the current capacity? Will downsizing impact training time, inference latency, or notebook responsiveness unacceptably?
- Implement the Change:
- Notebook Instances: Stop the notebook instance, modify its instance type through the SageMaker console or API, and then restart it.
- Training Jobs: Modify the instance type specified in your training script or job configuration for future training runs.
- Inference Endpoints: Update the Endpoint Configuration to use the new instance type and then update the Endpoint to use the new configuration. This typically involves deploying the model on new instances and then shifting traffic.
- Monitor Performance: After rightsizing, closely monitor the instance’s performance using CloudWatch metrics and application-level logs to ensure it still meets requirements. Be prepared to revert the change if necessary.
FAQ
Q: Why doesn’t CloudFix automatically resize the instance?
A: Resizing instances involves operational changes (stopping/starting notebooks, updating endpoint configurations) that require manual intervention and validation to avoid performance degradation or workflow disruption.
Q: Does this apply to all SageMaker instance types?
A: This applies to instances used for SageMaker Notebooks, Training Jobs, and Inference Endpoints where rightsizing is feasible.
Q: Is there downtime required?
A: Yes, typically. Resizing notebook instances requires a stop/start. Updating endpoints involves deploying new instances before terminating old ones, which can often be done with minimal or no user-facing downtime if managed carefully, but involves an update process. Modifying training jobs only affects future runs.
Q: What if my workload has rare but critical peaks?
A: Review longer-term metrics, including maximum utilization, before resizing. If occasional peaks are critical and cannot tolerate brief periods of higher utilization or throttling on a smaller instance, keep the current size. Rightsizing involves balancing cost savings with performance needs.
Related Resources
- Optimizing Costs for Machine Learning with Amazon SageMaker (AWS Blog)
- Analyze Amazon SageMaker Spend and Determine Cost Optimization Opportunities (AWS Blog Series)
- SageMaker Notebook Instance Types (AWS Documentation)
- Update a SageMaker Endpoint (AWS Documentation)
- ML Resize SageMaker (CloudFix Support)
Ready to start saving on AWS? See how much you could cut from your cloud bill with a free cost optimization assessment, or explore CloudFix automated Finder/Fixers that eliminate waste across 30+ AWS services.
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