Let Cast AI
configure your
GPU scaling policies
OpsPilot analyzes GPU workload behavior and automatically generates and applies the right scaling policy, routing GPU workloads into GPU-specific configuration instead of lumping them in with everything else.
Trusted by 2100+ companies globally
Problem
The problem with manually tuning every GPU workload
Every GPU workload needs its own tuning
Manually configuring a scaling policy for each GPU workload doesn’t hold up past a handful of clusters. Someone has to know every application well enough to set it correctly.
Platform teams don’t know every application
Rolling out GPU optimization across an organization means configuring workloads the platform team didn’t build and doesn’t have deep context on.
Generic policies miss what GPU workloads need
Applying default scaling policies to GPU workloads treats them like any other pod, missing the sharing, replica, and hardware constraints that actually matter for them.
Key features
GPU scaling policies, configured for you
Automatic policy generation, no manual tuning required
OpsPilot analyzes workload behavior and automatically generates and applies the right scaling configuration, including routing GPU workloads into dedicated GPU-specific policies rather than generic ones.
Already running for every Cast AI customer
OpsPilot AI Policies is live today, released to every Cast AI customer, and already configuring GPU workload scaling in production.
Set once today, continuously refreshed next
OpsPilot generates and applies the right policy the first time a workload is analyzed, and you can always refresh that policy on an ongoing basis as workload behavior shifts.
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.
Setup
Get started in three steps
Select your provider and run a single script to deploy a lightweight, read-only agent that will analyze your cluster.
Set your intent in three questions. OpsPilot generates your scaling policies, Cast AI handles the rest.
Keep your cluster optimized with automation at every step.
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
It analyzes workload behavior and automatically generates and applies a scaling policy, including routing GPU workloads into GPU-specific configuration.
No. OpsPilot is built for platform teams rolling out GPU optimization across workloads they don’t have deep application-level context on.
It’s released to every Cast AI customer today.
Yes, that’s live today.
For most workloads, yes. You can still configure policies manually wherever you want direct control.
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