Bare metal to serverless, on one platform — because the right level doesn't stay the same.
Most AI infrastructure asks you to pick a lane before you start. Managed endpoints that handle everything, or raw GPUs you run yourself. Both are reasonable choices. Both work.
What's harder is that the choice tends to get made once, early - before anyone knows how the workload will actually behave.
And workloads move. An experiment in a notebook turns into a service with real traffic. A model running happily on a managed endpoint starts needing tuning the endpoint doesn't expose. A training run outgrows the setup it started on. Each of those is a good problem to have. None of them should require changing platforms to solve.
That's the idea TrndX SDC (Specialized Data Center) is built around.
One platform, multiple levels
Rather than picking a level of abstraction for you, we offer the whole range over the same infrastructure - and let you choose how much you want to manage.
| Level | What you manage | Good for |
|---|---|---|
| Bare metal | Everything | Training at scale, and work that needs the hardware itself |
| GPU VMs | Your environment and stack | Control, without running hardware |
| Kubernetes | Your workloads and scaling | Containerised pipelines and multi-team setups |
| AI workbench | Your code and models | Getting started the same day |
| Inference endpoints | Your model and your API | Shipping to users |
Every level runs on the same infrastructure. Moving between them isn't a migration - it's a choice you can revisit.
Fine-tune on bare metal and serve from an endpoint. Start on a managed notebook and drop down a level when you want more control. Same platform, same data, same bill. That flexibility is the product.
Built for where you are now
If you're building an application, start at the top. A notebook or an endpoint, running in minutes, with none of the setup underneath it to think about.
If you're an ML engineer with specific requirements, start at the bottom. Dedicated hardware, your own stack, and a platform that stays out of the way.
If you're teaching, you probably need both at once. Most students want a managed environment that works on day one of the semester. A smaller group wants to build a cluster by hand, configure it, and see what breaks. On one spectrum, that's a single environment with different levels of access - and the infrastructure itself becomes part of the course.
And if you're somewhere in between, which most teams are, you can move as the work changes.
Not sure which level you need?
That's a normal place to start, and usually the honest answer depends on your workload, your utilization, and how much you want to operate yourself.
Our developer relations team will work through it with you directly - whether you're sizing a cluster, shipping a first feature, or planning next semester's lab.