Choosing an MLOps Platform in 2026

Pick the wrong MLOps platform, and you’ll spend the next two years babysitting custom infrastructure. Pick the right one and your teams can actually ship models.

This guide is for people deciding what actually matters, what the options look like, and where Saturn Cloud fits if you need something that runs where your GPUs are.

Figure out your constraints first

Before you look at any vendor, answer these questions. They’ll eliminate most options:

• Are you on a single cloud, multiple clouds, or on-premises?

• Will your security team allow data to flow through a third-party SaaS provider, or does everything need to stay in your VPCs?

• Are you running small CPU jobs or serious GPU training and inference?

• Do you need GPU capacity or pricing that your current cloud can’t provide?

• Are you comfortable being locked into a single cloud vendor’s ML roadmap?

If you skip this step, you’ll waste time debating UI features rather than the actual trade-offs.

The three types of MLOps platforms

Most MLOps platforms fall into one of three categories.

1. Cloud-managed platforms

This is the “use your cloud provider’s ML service” option, think SageMaker, Vertex AI, Azure ML.

Works well when you’re committed to a single hyperscaler, your data’s already there, and you want tight integration with their storage and IAM.

However, you’re now tied to that vendor’s roadmap and complexity. Most teams find these platforms harder to use than they need to be. And if you need GPUs, your cloud can’t get you, whether availability or pricing, you’re looking at a migration, not a config change.

2. Hosted SaaS tools

These run outside your infrastructure. You sign up, connect your data, and start using their UI.

Suitable for moving fast without involving infra, and when your security team is fine with data flowing through a vendor’s environment. Works well for lighter use cases such as experiment tracking, basic deployments, and small teams.

The problem: networking, observability, and data flows now live partially outside your stack. That’s often a non-starter in regulated environments. And when you outgrow them, you’re unwinding workflows tightly coupled to their service.

3. Self-hosted platforms

You deploy these into your own infrastructure, including cloud accounts, Kubernetes clusters, and on-prem environments.

Makes sense when you want a better developer experience than cloud-managed platforms offer, when you need GPU capacity your current cloud can’t provide, or when security requires everything to stay on your network. You keep control over networking, IAM, and costs while giving ML teams a unified platform.

The trade-off: someone has to own deployment and upgrades. Requires alignment between infra and ML teams.

Saturn Cloud is in this third category.

What Saturn Cloud actually is

Saturn Cloud is an ML platform you deploy into your own environments like AWS, GCP, Azure, Oracle, Nebius, Crusoe, or on-prem. It runs within your VPCs, using your IAM and networking.

For ML engineers, it’s where you spin up Jupyter environments backed by NVIDIA GPUs, work with PyTorch, TensorFlow, RAPIDS, XGBoost, and then turn that work into scheduled jobs and pipelines. You can deploy real-time APIs, run workloads on Dask clusters instead of single machines, and share environments with your team.

For infra engineers, it’s another service in your accounts. You control which clouds it’s installed in, how it’s networked, and where logs go.

When Saturn Cloud makes sense

You probably don’t need it if you’re a small team running CPU jobs on a single cloud and happy with your current setup.

It becomes relevant when:

You’re frustrated by the complexity of cloud ML platforms. You want something Python-first that your team will actually use, not a sprawling managed service.

You need GPUs, your cloud can’t get you. Saturn Cloud connects to GPU clouds like Nebius and Crusoe. You can access H100s/H200s without rebuilding your stack.

Security requires everything in your accounts. Saturn Cloud runs in your VPCs, not in someone else’s SaaS.

You have workloads across multiple clouds. One platform instead of maintaining separate ML stacks per environment.

How to actually evaluate this

Run the same real workload on each platform type. Not a vendor demo—something you’d actually maintain: a GPU-backed notebook, a training job, a model service with logging.

Then ask:

• How hard was it to connect to your existing data, IAM, and observability?
• How much did you bend your infrastructure to fit the platform’s assumptions?
• How productive was the team compared to other options?
• What happens when you need GPUs that your current cloud can’t provide?

FAQ

What’s an MLOps platform?

It’s the infrastructure layer that handles the ML lifecycle—experiment tracking, training, deployment, and monitoring. The goal is to standardize how models move from notebooks to production, rather than everyone building their own scripts.

Do I need one, or can I just use MLflow and Kubeflow?

You can build a stack from open-source tools. The trade-off is you’re now running your own platform—Kubernetes, upgrades, auth, integrations are your problem. An MLOps platform makes sense when you want a supported way for multiple teams to run experiments and services without everyone reinventing the wheel on infrastructure.

Cloud-managed vs. independent platform—how do I choose?

Use cloud-managed if you’re all-in on a single cloud, can tolerate its complexity, and aren’t worried about GPU availability.

Use an independent platform if you want simpler tooling, need GPUs your cloud can’t provide, or want to avoid vendor lock-in.

Does multi-cloud support matter?

If you’re sure you’ll stay on one cloud forever, probably not. It matters when you need lower-cost or more widely available GPUs from providers like Nebius or Crusoe, or when you have teams spread across environments.

How do MLOps platforms affect GPU costs?

Three ways: scheduling and right-sizing (autoscaling, spot instances) prevent idle GPUs; better inference efficiency (batching, concurrency) reduces cost per request; platforms like Saturn Cloud let you route workloads to GPU clouds with better pricing without rewriting code.

What should I look for around security?

If you’re in a regulated environment: deployment into your VPC (not just SaaS), integration with your SSO/IAM, private networking to data sources, and logging into your existing observability stack. Saturn Cloud deploys into your accounts so security can treat it like any internal service.

How do I avoid lock-in?

Check these things: Can you use standard frameworks (PyTorch, TensorFlow, MLflow), or do you need proprietary APIs? Can it run on multiple clouds? Can you export models and pipelines to run elsewhere?

Saturn Cloud’s approach: run on your infrastructure, plug into your existing registries and feature stores, so you’re not starting over if things change.

Is Saturn Cloud just a notebook environment?

No. It’s notebooks and IDEs on CPUs/GPUs, plus jobs, pipelines, Dask clusters, and model services for inference—deployed into your own accounts.

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About Saturn Cloud

Saturn Cloud is a portable AI platform that installs securely in any cloud account. Build, deploy, scale and collaborate on AI/ML workloads-no long term contracts, no vendor lock-in.