Most platform evaluations fail in a boring way: someone spins up a notebook, runs a tutorial, says “it works,” and the team signs up. Three months later they find out that the quota they needed takes a week to approve, or that their data pipeline doesn’t fit the platform’s storage model. A proof of concept (PoC) exists to find those problems while leaving is still cheap.

Start With the Question, Not the Platform

Before touching any console, write down what decision this PoC feeds. “Can we train our ranking model here?” is a real question. “Is this platform good?” is not. A useful PoC has one or two decisions attached, such as whether to move training, or whether to host inference there, and a date by which you’ll make the call.

In practice, it helps to name a single owner and give them a fixed window, often two to three weeks. Open-ended trials drift. People get pulled onto other work, credits sit unused, and nobody can say afterwards what was learned.

Pick a Workload That Hurts a Little

Tutorials hide the friction. Choose a job you already run today, ideally one that is slightly annoying on your current setup. That gives you a baseline and a reason to care about the result.

Good candidates share a few traits:

• They use your real data format and roughly your real data volume, not a toy sample.
• They touch the awkward parts: reading from your existing object storage, pulling private container images, authenticating against your identity provider.
• They have a result you can compare, such as the same model quality or the same throughput target.

If you can only test one thing, test the data path. Compute is usually the easy part to match. Getting data in, keeping it close to the GPUs, and getting results out is where platforms differ most.

Write Success Criteria Before You Start

Writing criteria afterwards is how a PoC becomes a justification for a decision already made. Agree on them in advance, and keep the list short.

Area	Question to answer	Evidence to collect
Capacity	Can we get the hardware we need, when we need it?	Time from request to running job; any quota or approval steps
Workflow	Can the team run our job without heroic effort?	Number of manual steps; who needed vendor support
Data	Does our data move in and out acceptably?	Transfer time; any egress charges observed
Reliability	Do jobs finish, and do failures explain themselves?	Failed runs and how long diagnosis took
Cost	What does one realistic run cost end to end?	Invoice or billing export for the PoC period
Exit	Could we leave if we had to?	What is proprietary versus portable in our setup

Notice that “performance” isn’t a row of its own. Raw speed matters, but only relative to your baseline, and it is often not the deciding factor.

A Hypothetical Walkthrough

Imagine a small team that fine-tunes a text classifier weekly on a rented cluster elsewhere. They want to know whether a new platform would shorten the cycle.

They pick last week’s real fine-tuning job. On day one they request the GPU type they need and record that a quota increase took two days of back and forth. On day three they run the job against a copy of their data in the platform’s storage and find it finishes, but data loading dominates the runtime because the storage sits in a different region than the compute. They fix that by moving the copy and rerun.

By the end of week two they have two honest numbers: the cost of the run as billed, and the engineer hours spent getting there. They also noticed that checkpoints written to local disk vanished when an instance was reclaimed, which told them something about how they’d have to restructure the job. That last finding is exactly the kind a tutorial would never surface.

Measure the Boring Things

Keep a plain log as you go. Every time someone gets stuck, write down what blocked them and how long it took to resolve. These notes are more valuable than any benchmark.

A few things worth recording specifically:

• How long provisioning took, including any waits for capacity.
• Every place you had to read documentation to proceed, and whether it was clear.
• Support interactions: how fast the response was and whether it was useful.
• Anything you configured by hand that you’d need to automate before relying on it.

Check the vendor’s current documentation for limits and supported configurations rather than relying on marketing pages or old forum posts, since these change often.

Don’t Skip the Exit Test

Teams almost never test leaving. Do a small version: take the artifacts your PoC produced (trained weights, container images, configuration) and confirm you can run them somewhere else. If your job only works because it leans on a platform-specific feature, that’s not disqualifying, but it should be a conscious trade rather than a surprise. Our checklist on avoiding vendor lock-in goes deeper on this.

Close It Out Properly

When the window ends, hold a short review with the criteria you wrote at the start. For each row, state what you found and whether it passed, failed, or remains unknown. Then delete everything: instances, buckets, snapshots, test endpoints. Forgotten PoC resources are a classic source of surprise bills.

Frequently Asked Questions

How long should an AI cloud PoC run? Two to three weeks is usually enough for one well-defined workload. Shorter and you won’t hit the awkward cases; longer and the effort tends to stall.

Should we use the free credits or pay for the trial? Credits are fine for the trial itself, but model your cost as if you were paying. Otherwise you’ll have no real read on spend until the first invoice.

Do we need to test more than one platform? If the decision is close, yes, using the same workload and the same criteria. Comparing two against an identical rubric is far more informative than evaluating each in isolation.

What if we can’t get the GPUs we want during the trial? That is a result, not a failure of the test. Capacity and quota friction are real operating conditions, so record it and weigh it.

Who should be involved besides the ML engineers? Someone from security or infrastructure should review identity, networking and data handling early. Finding a compliance blocker in week three is much cheaper than finding it after migration.

Next Step

Pick one existing job this week, write six success criteria using the table above, and set an end date on the calendar before you create a single resource.