Find out what your GPUs
are actually doing
A single p4d.24xlarge runs over $23,000 a month on-demand, and billing data alone can't tell you whether it earned that. MLCostIntel joins your cost data with GPU utilization metrics, so idle instances, oversized jobs, and spot candidates show up with dollar amounts attached.
Where GPU money leaks
Idle instances nobody owns
Development boxes left running over the weekend. A proof-of-concept cluster from a project that ended in March. A "temporary" instance someone spun up last quarter. Individually forgettable; together they're often four to five figures a month.
Low utilization on big instances
Jobs on 8-GPU instances using 2 GPUs. Training runs that fit in half the memory. Billing data alone can't see this — you need utilization joined with cost to know an instance is oversized.
On-demand where spot would do
Interruptible training and batch workloads running on on-demand pricing pay 3–10× more than managed spot with checkpointing would cost.
Shared clusters blur accountability
On shared EKS GPU node groups, teams request whole GPUs "to be safe." The padding is invisible because the bill arrives as one cluster-level number, so nobody ever has to justify what they reserved versus what they used.
How MLCostIntel eliminates GPU waste
MLCostIntel joins your Cost and Usage Report with GPU utilization metrics from DCGM and CloudWatch. Every recommendation is based on what your workloads actually used, so you can act on it without re-verifying the numbers yourself.
- ✓ Idle GPU detection — instances with sustained near-zero utilization flagged with their monthly burn rate
- ✓ Utilization-aware rightsizing — recommendations based on actual GPU memory and compute usage per workload
- ✓ Spot eligibility analysis — recurring interruptible workloads identified with quantified 60–90% savings per job
- ✓ Pod-level GPU attribution on EKS — shared node group costs allocated to teams and workloads
- ✓ Fleet-wide coverage — SageMaker, EC2 (p3/p4/p5, g4/g5/g6), and EKS GPU spend in one view
- ✓ Waste tracking over time — optimization score so you can prove utilization is improving quarter over quarter
What teams get out of it
GPU rightsizing savings
From matching instance types to actual workload profiles
Spot savings
On interruptible training and batch workloads with checkpointing
Idle spend target
Every idle GPU flagged with an owner and a shutdown recommendation
Shared cluster visibility
EKS GPU costs allocated to the workloads that consume them