See exactly where your GPU budget goes
Track utilization, spend, and idle capacity for every GPU workload and team, across your entire fleet, in real time.
Trusted by 2100+ companies globally
Problem
The problem with GPU spend past the invoice
GPU spend is a black box past the invoice
Teams know the total GPU bill but not which workload, team, or model is actually driving it.
Idle GPU capacity hides in plain sight
Without per-workload utilization data, overprovisioned replicas and idle partitions go unnoticed until someone audits manually.
Cost attribution breaks down across clouds and sharing strategies
Shared, partitioned, and cross-cloud GPUs make simple per-node cost math meaningless.
Key features
Every GPU dollar, traced back to its workload
Real-time utilization and spend, per workload
Track GPU utilization, memory usage, and performance in real time, broken down by workload, team, and application, not just by node or cluster.
Cost attribution that understands sharing and multi-cloud
Whether a GPU is fully dedicated, time-sliced, MIG-partitioned, or sourced from another cloud, Cast AI attributes cost back to the workload actually using it.
Find idle capacity before it becomes waste
Underutilized replicas, idle partitions, and overprovisioned node templates surface automatically, so finding optimization opportunities doesn’t require a manual audit.
Time-slicing
Share GPUs across multiple workloads using temporal partitioning. Configure 1 to 48 replicas per GPU to match workload density requirements.
MIG partitioning
Divide A100, A30, and H100 GPUs into physically isolated instances. Each partition has dedicated compute cores and memory with no noisy neighbors.
Dynamic resource allocation
Define what you need with Kubernetes-native ResourceClaims, and Cast AI provisions matching hardware automatically.
Global GPU capacity
Source GPU nodes from any region or cloud provider. OMNI handles provisioning and setup, so remote GPUs appear as native cluster nodes.
GPU-optimized bin-packing
Placement algorithm that accounts for GPU sharing, MIG partitions, and workload requirements. Maximize node utilization before scaling out.
GPU metrics & cost attribution
Track GPU utilization per workload, attribute costs to teams or apps, and spot idle capacity with optimization recommendations.
Case study
Akamai achieves 40-70% cloud savings, boosts engineer productivity
“I had an aha moment – an iPhone moment – with Cast. Literally two minutes into the integration, we saw the cost analytics, and I had an insight into something I had never had before and had tried to get for a very long time.”
Dekel Shavit
Sr. Director of Engineering
Case study
Yotpo automates Spot Instances, cuts 40% in cloud costs and saves time
“And with Cast AI, we didn’t do anything. Like, we didn’t do the move before, we didn’t do the move after. So there was a lot of human resources and time saved here. That was a very good experience. And again, from a cost perspective, it was highly optimized.”
Achi Solomon
Director of DevOps
Case study
Bede Gaming automatically optimizes K8s workloads with no risk to performance
“In my mind, it’s one less thing to worry about, and therefore teams can be focused on other things of potentially higher value. So having [Cast AI] just run in the background with a good level of confidence that we’re running as efficiently as we can, balancing the service that we’re providing – that’s great.”
Dan Whiteley
Chief Technology Officer
Learn more
Additional resources
Report
2025 Kubernetes GPU Trends & Cost Report
Real data on GPU availability, pricing patterns, and performance insights across clouds.
Blog
GPU Cost Optimization: How to Reduce Costs with GPU Sharing and Automation
GPU costs are skyrocketing as more teams run AI and ML workloads. Discover how GPU…
Blog
GPU Shortage Mitigation: How to Harness the Cloud Automation Advantage
Training AI models has never been buzzier – and more challenging due to the current…
FAQ
Your questions, answered
Real-time utilization, memory usage, performance, and spend, broken down by workload, team, and application.
Yes. Cost is attributed to the workload actually using the GPU, regardless of how it’s shared or partitioned.
Yes, including GPU capacity sourced from other providers through OMNI.
Yes, that’s the default view.
Yes. Underutilized replicas and overprovisioned node templates are surfaced automatically, without a manual audit.
It’s built into the core platform. There’s nothing separate to buy or install.
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