Intelligent Operations
GPU Infrastructure That Doesn't Waste Money.
We build and run the infrastructure behind your AI models, sized correctly for training and inference, so you're not paying for idle GPUs.
The Challenge
AI workloads need real computing power, and that power is expensive to rent. It's easy to end up paying for GPUs that sit idle most of the time, or the wrong type of instance for what you're actually running.
Get the setup wrong and you're either overspending badly, or your models are too slow to be useful.
NVIDIA
Kubernetes
AWS
Azure
Match Hardware To The Workload
So we start by matching GPU type and pricing model to what you're actually running, training bursts and steady inference are set up differently.
Explore CloudStop Paying For Idle GPUs
With the right hardware in place, autoscaling and scheduling mean you stop paying for compute that isn't being used.
Explore Cost OptimizationRun It Like Production, Not A Prototype
We run those workloads on properly configured Kubernetes, so they scale and recover the same way the rest of your systems do.
Explore KubernetesThe Result
Put together, this is what changes: GPU spend that matches real usage, not guesswork. And to be clear, this is different from CloudDrove Intelligence, that's our own AI that helps run your infrastructure. This is the infrastructure that runs yours.
Explore IntelligenceRelated Reading
Go deeper.
FAQs
Questions, Answered.
Is this the same as CloudDrove Intelligence?
No. CloudDrove Intelligence is our own AI tooling that helps run your infrastructure. AI Infrastructure is different: it's us building and running the GPU infrastructure that powers your AI models and applications.
Why is GPU infrastructure so expensive to get wrong?
GPUs are costly to rent, and it's easy to pay for far more than you're using, idle GPUs, oversized instances, or the wrong pricing model for your workload.
Do you help with training, inference, or both?
Both. The right setup is different for each: training needs raw power in bursts, inference needs to be fast and always available. We design for which one you actually have.
Which cloud providers do you work with for AI workloads?
AWS, Azure, and GCP, along with GPU-specific providers when they make sense for cost or availability.
Cloud Infrastructure Assessment
See exactly where your cloud stands.
A senior engineer reviews your architecture, cost, security, and reliability, then sends back a prioritized findings report, the fixes that matter most, in order.
- Architecture & scale
- Cost & efficiency
- Security & reliability
Complimentary · no obligation · no sales pressure
Work With Us
Running AI workloads? Let's talk cost and setup.
Tell us what you're training or serving and we'll show you where the waste is.
Talk to an Expert