Hyperscalers Still Matter for Full-Stack Engineers — But Not for MLEs

It’s important to be clear: hyperscalers like AWS, Google Cloud, and Azure aren’t bad. In fact, they’re almost indispensable for full-stack engineers and backend developers who have to deal with the realities of scaling production web applications. When your product needs to handle millions of HTTP requests per second, load-balance NGINX clusters, manage databases, and push content through global CDNs, the infrastructure muscle of the hyperscalers truly shines. Their managed services, elastic IP allocation, autoscaling groups, and mature network fabrics make life simpler for web and app engineers.

If you’re building a consumer app or an API-driven SaaS platform, those things matter. You want your traffic to scale automatically, your Redis cache to stay alive under load, and your logs to flow cleanly into a monitoring stack. In that world, hyperscalers are worth the cost. They provide deep operational stability — and for traditional software, scaling web traffic is a harder, more chaotic problem than just spinning up more compute. Paying for that reliability is rational.

But for machine learning engineers, the story is very different.

MLEs don’t run live web servers serving millions of concurrent users. They train and deploy models. Their bottlenecks are GPUs, data throughput, and orchestration — not TCP latency or CDN caching. The “always-on, always-scaled” model that full-stack teams pay for is often overkill for ML workflows, which tend to be episodic and bursty. Training runs can last hours or days, then sit idle until the next experiment. Why pay hyperscaler rates for infrastructure that’s only busy a fraction of the time?

Most MLEs use hyperscalers for one simple reason: it’s easier. The cloud SDKs, the IAM roles, the familiar dashboards — all of it feels integrated. But what they really need from those tools is just orchestration. They want the ability to go from a Jupyter notebook to a reproducible training job, to a serving endpoint, without having to fight with YAML or provisioning scripts. That’s a software convenience, not a hardware requirement.

And that’s exactly what SkyPortal offers.

SkyPortal gives MLEs agentic orchestration — meaning the system itself can detect environments, provision jobs, configure clusters, and deploy models — without the user needing to write a line of infrastructure code. You get the same “one-click to production” feel that hyperscalers market so aggressively, but you’re not trapped inside their walled gardens or pricing models. You can connect to any low-cost GPU host — RunPod, Vast.ai, Lambda, or other neocloud providers — and still enjoy an enterprise-grade orchestration experience.

Better yet, SkyPortal doesn’t stop at compute. With the click of a button, you can link your training jobs to any data source — AWS S3, GCP Storage, Hugging Face Datasets, MinIO, or a local filesystem. The agent handles authentication, mounting, and access automatically. It turns data connectivity into a utility, not a project.

In other words, SkyPortal gives MLEs the freedom full-stack engineers don’t have. Full-stack apps live and die by their hosting fabric; ML pipelines don’t. For training and deploying models, the world is already distributed — data lives in different clouds, GPUs live on different hosts, and workflows span multiple services. What matters is having a system smart enough to coordinate all that complexity, not a hyperscaler to own it.

The age of hyperscaler dependency for machine learning is ending. The next generation of MLEs will be cloud-agnostic by design, using agentic tools like SkyPortal to stitch together the best compute, the best data, and the best performance — without paying for someone else’s convenience.

The hyperscalers built the web. But SkyPortal is helping build the next frontier of ML.