GPU capacity for AI workloads is expensive and scarce and most of it sits unused. That’s the uncomfortable finding behind a lot of the current conversation around machine learning infrastructure costs and it’s the exact problem Cast AI has built its GPU optimization tools around. Cast AI is a Kubernetes optimization platform aimed at reliability and performance, using signals tied to service-level objectives (SLOs) to take guardrailed actions in production rather than leaving engineers to manually tune clusters.
This review looks at what the platform actually does for teams running AI, machine learning and inference workloads on Kubernetes, where it fits and where its limits are.
What is Cast AI?
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Cast AI automates the infrastructure decisions that sit underneath Kubernetes workloads: how much compute is provisioned, where it runs and when it scales up or down. The GPU optimization side of the platform extends that same automation to graphics processing units, which have become the most expensive and most contested resource in AI infrastructure.
Rather than treating GPU management as a separate product, Cast AI folds it into the same Kubernetes-native approach it uses for general infrastructure. That means clusters get monitored and adjusted continuously against SLO signals instead of through periodic manual reviews.
The GPU utilization problem Cast AI is built around
Cast AI’s 2026 State of Kubernetes Optimization Report, based on tens of thousands of Kubernetes clusters, found average GPU utilization of only 5%, meaning most provisioned GPU capacity sits idle at any given time. That single figure explains why GPU sharing and scheduling tools have become a bigger focus for infrastructure teams than raw chip access.
Pricing adds a second layer to the same problem. Cast AI’s 2025 GPU Price Report analyzed pricing and availability across 66 cloud regions using provider APIs, marketplace data and millions of scheduling events and found that continuous provisioning in favorable regions can create a 2-5x price advantage compared with average regional pricing. Idle capacity and regional price swings are two different issues, but they compound each other when a team is paying for GPUs by the hour regardless of whether they’re doing useful work. The general dynamics behind GPU scarcity in AI infrastructure have been documented elsewhere too, including in coverage of how demand for AI accelerators has outpaced supply across the industry.
How Cast AI works
On the utilization side, Cast AI applies GPU time-slicing, Multi-Instance GPU (MIG) and Multi-Process Service (MPS), letting several workloads share a single physical GPU instead of each one claiming a dedicated card it barely uses. GPU-aware bin packing groups workloads onto the smallest number of GPUs that can actually handle them and the platform also supports Kubernetes Dynamic Resource Allocation, the newer Kubernetes mechanism for requesting specialized hardware resources more flexibly than the older device plugin model. Readers unfamiliar with the underlying scheduler can find background on how Kubernetes handles resource allocation and pod scheduling in the project’s own documentation.
On the access side, OMNI Compute extends an existing Kubernetes cluster out to GPU capacity across other clouds and regions, so a team isn’t stuck with whatever a single provider or region happens to have available. Automated scaling and Spot or on-demand fallback handle the moment-to-moment decisions about which capacity to use and workload-level cost attribution gives teams a way to see which model, team or job is actually driving the bill.
The differentiator: turning idle GPU capacity into usable capacity
Plenty of infrastructure tools can autoscale a cluster. Fewer are built specifically around the fact that provisioned GPUs are usually not doing anything. Cast AI’s combination of time-slicing, MIG, MPS and bin packing is aimed directly at that 5% utilization figure and it’s the part of the platform that separates it from generic autoscaling or cost-monitoring tools that don’t touch GPU sharing at all.
That focus matters because GPU waste isn’t a rounding error. When a cluster’s GPUs sit mostly idle, a team is effectively renting hardware it isn’t using and no amount of Spot-instance discounting fixes that on its own. Sharing the hardware more efficiently is a different lever and it’s the one Cast AI leans on hardest.
A real-world result: the ALLEN Digital case
ALLEN Digital moved seven AI models from Amazon SageMaker to Cast AI’s GPU stack and reported around 20% savings immediately from GPU time-sharing, 30-40% after consolidating models onto shared GPU infrastructure and eventually more than 70% after additional rightsizing and node optimization. The published case study reports 71% savings compared with SageMaker, with latency for the tested open-source models remaining comparable to or lower than SageMaker’s.
That’s a useful data point because it shows the savings didn’t come from one single change. They built up in stages, starting with sharing GPUs across workloads, then consolidating further, then fine-tuning node sizing. Teams evaluating the platform should expect a similar phased path rather than a single toggle that immediately cuts a bill in half.
Key features worth knowing
GPU time-slicing, MIG and MPS support: lets multiple workloads share a physical GPU instead of each claiming a full card.
GPU-aware bin packing: packs workloads onto the fewest GPUs that can handle them, cutting the number of idle or half-used cards in a cluster.
Kubernetes Dynamic Resource Allocation: uses the newer Kubernetes mechanism for requesting specialized hardware, rather than relying only on older device-plugin approaches.
OMNI Compute: extends a cluster’s reach to GPU capacity in other clouds and regions instead of limiting a team to one provider’s inventory.
Workload-level cost attribution: breaks spending down by model, team or job, so finance and engineering can see where the GPU budget is actually going.
Who Cast AI is best for
This makes Cast AI particularly relevant for AI infrastructure, ML platform, DevOps and platform engineering teams that need to run more workloads on existing GPUs, reach capacity across regions or clouds and automate the Kubernetes plumbing underneath AI applications. Organizations running LLM inference or training pipelines on Kubernetes, where GPU spend is already a visible line item, are the clearest fit.
Smaller teams running a handful of models on a single managed GPU instance, without a Kubernetes layer underneath, likely won’t see the same payoff. The tooling assumes a Kubernetes-based setup with enough workload volume that sharing and cross-cloud placement actually move the needle.
Where it falls short
Kubernetes is a prerequisite. Teams running GPU workloads outside a Kubernetes environment, on bare VMs or through a fully managed inference API, won’t be able to use most of what’s described here.
The savings build over time, not instantly. The ALLEN Digital numbers came in stages, from time-sharing to consolidation to rightsizing. A team expecting an immediate, one-step cost cut should plan for a rollout instead.
It solves an infrastructure problem, not a model problem. Cast AI optimizes how GPU capacity gets used and accessed. It doesn’t change model architecture, training approach or inference latency at the algorithm level, so it won’t fix a genuinely inefficient model.
None of these are dealbreakers for the audience the platform is built for. They’re just the trade-offs of a tool that assumes a Kubernetes-native, multi-workload environment.
The Verdict
Cast AI’s case rests on a specific, well-documented problem: GPUs sitting mostly idle while teams still pay for them by the hour. The platform’s response, sharing hardware more efficiently through time-slicing, MIG, MPS and bin packing, plus reaching across clouds and regions through OMNI Compute, is a coherent answer to that problem rather than a scattershot feature list.
Its ranking of #1 out of 223 solutions in its category and the documented ALLEN Digital result give the pitch more weight than most infrastructure tools can back up. For AI infrastructure and platform engineering teams already running Kubernetes and watching GPU costs climb, it’s a strong option to evaluate directly against current GPU spend and utilization data.