Deploy more AI workloads on fewer GPUs anywhere
OMNI Compute for AI enables scarce GPU and compute capacity across clouds and regions to be operated within the same Kubernetes cluster, allowing AI teams to scale anywhere without refactoring applications or adding operational overhead.
Trusted by 2100+ companies globally
Key features
Scale capacity anywhere. Operates GPU as one system.
Access GPUs wherever you
need them
Get access to scarce GPU capacity across clouds and regions through one control plane, while keeping full control over where workloads run.
- Discover and use GPU capacity across providers and regions with cost, performance, and compliance controls
- Deploy workloads where capacity is available, without code changes
Run more on every GPU
Maximize throughput from every GPU without sacrificing performance or isolation.
- Share and partition GPUs to increase utilization across workloads
- Place workloads with bin-packing and Dynamic Resource Allocation
Scale GPUs based on
real demand
AI demand is spiky. OMNI Compute adapts capacity continuously.
- Scale GPU capacity up and down based on real workload demand
- Use Spot and on-demand GPUs intelligently with automated fallback
See exactly how your GPUs perform
Maintain visibility and control as AI environments grow.
- Track GPU utilization, memory usage, and performance in real time
- Attribute GPU usage to workloads, teams, and applications
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 four steps
Select your provider and run a single script to deploy lightweight agents that will analyze and optimize your cluster.
Choose the best regions based on availability, latency, and costs. Add it to your existing cluster.
Set your node templates and let Cast AI optimize your cluster automatically.
Deploy AI Workloads anywhere and keep your cluster optimized.
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
OMNI Compute for AI extends your Kubernetes cluster across regions and clouds so workloads can consume scarce compute wherever it is available. This includes GPUs, TPUs, and CPU capacity, all managed through a single cluster.
It removes regional capacity constraints. When compute resources are unavailable in one region, OMNI Compute for AI allows to automatically provision capacity in other regions so workloads continue running without delays.
No. While it is built for AI workloads, OMNI Compute for AI supports any scarce or constrained compute, including GPUs, TPUs, and CPU-based workloads.
OMNI Compute for AI builds on Cast AI Autoscaler capabilities such as intelligent node selection, GPU time-sharing, and automated scaling. It continuously matches workloads with the best available resources to maximize utilization and keep performance stable without manual intervention.
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