GPU Cost Optimization
GPUs are the most expensive line item in AI infrastructure — and the most wasted. MLCostIntel correlates GPU utilization with spend to find idle GPUs, right-sizing opportunities, and spot savings across your AWS account.
Common GPU Cost Challenges
Idle GPUs
Expensive Amazon EC2 GPU instances — p4, p5, g5, g6 — running at low utilization or left on overnight and on weekends. GPU capacity is the priciest resource in your stack, so every idle hour is real money burned.
No Utilization-to-Cost Link
Amazon CloudWatch shows GPU utilization and the AWS bill shows dollars, but nothing connects them per resource. Without that link, you can't tell which specific GPUs are underused and costing you the most.
On-Demand Instead of Spot
Interruption-tolerant training jobs paying full on-demand price when managed spot could cut 40-90%. Batch and checkpointed workloads are ideal spot candidates, yet most run on-demand by default.
Over-Provisioned Inference
GPU inference endpoints sized for peak traffic but sitting idle most of the time. Without utilization data tied to cost, teams keep paying for capacity that rarely gets used.
How MLCostIntel Optimizes GPU Costs
MLCostIntel connects to your AWS account and joins real GPU utilization to spend for every resource — then flags idle and over-provisioned GPUs, recommends right-sizing and spot moves, and attributes GPU cost to the experiments and teams driving it.
- ✓ GPU utilization correlated with spend per resource — from Amazon CloudWatch / DCGM metrics plus the AWS Cost and Usage Report (CUR)
- ✓ Idle-GPU detection with waste estimates so you can act on the biggest offenders first
- ✓ Instance right-sizing recommendations across GPU families — p3/p4/p5 and g5/g6
- ✓ Spot-eligibility analysis for training jobs (40-90% savings on interruption-tolerant workloads)
- ✓ Coverage across Amazon EC2, SageMaker, Amazon EKS, and Amazon ECS GPU workloads
- ✓ Scheduling recommendations to shut down development GPUs off-hours, plus per-experiment and per-team GPU cost attribution
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 GPU Utilization and Spend
MLCostIntel ingests your Cost and Usage Report and collects GPU utilization from Amazon CloudWatch (and DCGM where available), correlating the two across EC2, SageMaker, EKS, and ECS.
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 GPUs
Total GPU Spend
Optimize overall GPU spend by combining idle detection, right-sizing, spot, and scheduling
Idle GPU Reduction
Detect GPUs running at low utilization or outside working hours and shut them down
Spot Training
Switch eligible training jobs to managed spot instances with checkpointing
Instance Right-Sizing
Match the GPU family and size to actual workload profiles and utilization data