Detecting and Managing SageMaker Cost Spikes Using AWS CLI

The Problem: Unexpected Cloud Costs

A common challenge when running workloads in the cloud is managing costs effectively. Just like analyzing your home electricity bill, understanding cloud costs requires both visibility and the right tools to investigate spikes in usage.

Recently, I encountered a situation where AWS Cost Anomaly Detection alerted me to a significant cost increase in SageMaker usage - we're talking about a 29,000% increase! This blog post walks through how to investigate and resolve such issues using AWS CLI.

The Investigation Strategy

When dealing with unexpected SageMaker costs, we need a systematic approach to:

1. Identify which resources are active
2. Determine which resources are generating high costs
3. Take action to stop unnecessary resource usage

The script I used for this investigation is available here: SageMaker Cost Analysis Script

Understanding the Results

When you run this script, it reveals several types of potentially costly resources:

1. GPU Instances: Look for instance types like ml.g4dn.12xlarge or ml.g5.12xlarge
2. Studio Sessions: Running JupyterLab instances
3. Canvas Sessions: SageMaker Canvas applications
4. Processing Jobs: Long-running data processing tasks

Taking Action

Once you've identified the costly resources, here are the commands to stop them:

# Stop JupyterLab instances
aws sagemaker delete-app \
    --domain-id <domain-id> \
    --user-profile-name <username> \
    --app-type JupyterLab \
    --app-name <app-name> \
    --region <region>

# Stop training jobs
aws sagemaker stop-training-job \
    --training-job-name <job-name> \
    --region <region>

# Stop processing jobs
aws sagemaker stop-processing-job \
    --processing-job-name <job-name> \
    --region <region>

Best Practices

To prevent future cost surprises:

1. Regular Monitoring: Run cost analysis scripts regularly
2. Resource Tagging: Implement proper tagging for all resources
3. Cost Alerts: Set up AWS Budget alerts
4. Automated Cleanup: Consider automating resource cleanup
5. Instance Right-sizing: Regularly review instance types being used

Tools for Cost Management

The AWS CLI and CloudShell are powerful tools for enterprise SageMaker users. Keeping scripts like these in version control provides several benefits:

• Quick investigation of cost spikes
• Automated resource management
• Consistent cleanup procedures
• Documentation of common operations

The script referenced in this post is part of a larger collection of AWS automation tools. You can find it and other useful scripts in the AWS-Gen-AI repository.

Conclusion

Managing cloud costs requires vigilance and the right tools. By using AWS CLI and simple scripts, you can quickly identify and address cost anomalies in your SageMaker environment. Remember to always double-check before terminating resources and consider implementing automated policies for resource management.

Want to learn more? Check out:

• AWS Cost Explorer Documentation
• SageMaker Best Practices
• AWS CLI Documentation

Want expert ML/AI training? Visit paiml.com

For hands-on courses: DS500 Platform

Recommended Courses

Based on this article's content, here are some courses that might interest you:

1. AWS AI Analytics: Enhancing Analytics Pipelines with AI (3 weeks) Transform analytics pipelines with AWS AI services, focusing on performance and cost optimization
2. Enterprise AI Operations with AWS (2 weeks) Master enterprise AI operations with AWS services
3. AWS AI Analytics: Building High-Performance Systems with Rust (3 weeks) Build high-performance AWS AI analytics systems using Rust, focusing on efficiency, telemetry, and production-grade implementations
4. MLOps Platforms: Amazon SageMaker and Azure ML (5 weeks) Learn to implement end-to-end MLOps workflows using Amazon SageMaker and Azure ML services. Master the essential skills needed to build, deploy, and manage machine learning models in production environments across multiple cloud platforms.
5. AWS Advanced AI Engineering (1 week) Production LLM architecture patterns using Rust, AWS, and Bedrock.

Learn more at Pragmatic AI Labs