SageMaker Cost Optimization
Stop overpaying for SageMaker. MLCostIntel analyzes your SageMaker notebooks, training jobs, and endpoints to find savings you're missing.
Common SageMaker Cost Challenges
Idle Notebooks
SageMaker notebook instances left running 24/7 when developers work 8 hours/day — you're paying 3x more than needed. A single ml.t3.xlarge notebook costs $150/month running idle overnight and on weekends.
Over-Provisioned Endpoints
Real-time inference endpoints sized for peak load but running at 10-20% utilization most of the time. Without usage-based scaling, you're paying for capacity you rarely use.
Training Waste
Training jobs using on-demand instances when managed spot training could save 60-90%, or running on oversized instances that leave GPU memory and compute underutilized.
No Cost Visibility
AWS Cost Explorer shows SageMaker as one line item — you can't see which notebooks, endpoints, or training jobs drive your bill. Without resource-level cost attribution, optimization is guesswork.
How MLCostIntel Optimizes SageMaker Costs
MLCostIntel connects to your AWS account and automatically breaks down your SageMaker spend by resource type, identifies waste, and provides actionable recommendations with estimated savings.
- ✓ Automatic SageMaker cost breakdown by resource type — notebooks, training, endpoints, processing, and data storage
- ✓ Idle notebook detection with auto-shutdown recommendations and scheduling policies
- ✓ Endpoint rightsizing based on actual invocation patterns and latency requirements
- ✓ Spot training analysis — identify training jobs that could use managed spot instances for 60-90% savings
- ✓ Per-experiment cost attribution for SageMaker training jobs linked to ML experiments and models
- ✓ Real-time cost anomaly detection for unexpected SageMaker spend spikes
How It Works
Connect Your AWS Account
Deploy a read-only IAM role via CloudFormation. No agents to install, no code changes. Setup takes less than 5 minutes.
We Analyze Your SageMaker Spend
MLCostIntel ingests your Cost and Usage Report and breaks down SageMaker spend across notebooks, training jobs, endpoints, and processing jobs.
Get Your Savings Roadmap
Receive prioritized recommendations with estimated savings, implementation guides, and an optimization score to track progress over time.
Where Teams Save on SageMaker
Idle Notebook Shutdown
Detect notebooks running outside working hours and set auto-shutdown policies
Endpoint Rightsizing
Match endpoint instance types and counts to actual invocation patterns
Spot Training
Switch eligible training jobs to managed spot instances with checkpointing
Instance Optimization
Choose the right instance families based on workload profiling and utilization data