Breaking Free from the Hyperscaler GPU Trap

Machine learning engineers (MLEs) often find themselves locked into hyperscaler ecosystems — AWS, Google Cloud, Azure — because those platforms appear to make everything easy. They promise seamless orchestration, managed Kubernetes clusters, integrated data pipelines, and one-click scaling. On paper, that sounds ideal. In practice, it’s an expensive kind of convenience.

The truth is that MLEs are paying a heavy premium for that illusion of simplicity. GPU prices on hyperscalers can run 4–5× higher than equivalent compute on independent cloud providers. Yet teams stick with AWS or GCP because of “data gravity” — the idea that once your workloads, S3 buckets, and EKS configurations live there, it’s too much hassle to leave. The cost of migration feels higher than the cost of overpaying. So people stay. And the cycle continues.

At its core, this is a software problem disguised as a hardware one. Hyperscalers have wrapped their infrastructure in proprietary tooling that makes standard Kubernetes and DevOps tasks feel approachable, but it also makes teams dependent. It’s easy to spin up a training job in SageMaker or Vertex AI. It’s not easy to move that workflow somewhere cheaper once you realize how much you’re spending. The friction is intentional.

But modern MLEs don’t actually need hyperscaler overhead to train or productionalize models. What they need is good orchestration, fast setup, and clean paths from notebook to deployment. That’s where SkyPortal comes in.

SkyPortal was built from the ground up to liberate MLEs from cloud lock-in. Our agentic orchestration system provides the same convenience developers expect from AWS — configuration, deployment, and monitoring — but without the hyperscaler tax. We handle the environment detection, setup, and job orchestration automatically. The difference is that you get to choose the hardware.

Whether you prefer RunPod, Vast.ai, Lambda, or any other Tier-3 GPU provider, SkyPortal abstracts the complexity so you can focus on your models instead of your infrastructure. The agent automatically detects the available environment, generates all the necessary YAML and Terraform configs, provisions your training jobs, and monitors progress in real time. It’s like having an MLOps team in a box — one that doesn’t care which GPUs you use.

This approach flips the hyperscaler model on its head. Instead of forcing MLEs to choose between ease and economy, SkyPortal delivers both. You can run your training where it’s cheapest and still enjoy the orchestration power you expect from the big clouds. That means your team can spend its budget on experimentation and iteration — not on inflated compute bills.

At a time when AI infrastructure costs are ballooning, this flexibility isn’t just nice to have; it’s essential. Companies that depend on deep learning and large-scale model training can’t afford to waste 5× on compute just because AWS made it easier to click “Launch.” The new generation of ML infrastructure has to be open, portable, and agentic — capable of handling multi-cloud reality without friction.

SkyPortal’s mission is simple: make model deployment and orchestration as effortless as hyperscalers pretend it is, while freeing teams to run anywhere they want. The future of ML doesn’t belong to one cloud — it belongs to whoever gives engineers true control.

Train anywhere. Deploy everywhere. That’s SkyPortal.