GuideCloud · 6 min
Which GPU cloud provider is best for us, and how do we compare them?
The best GPU cloud is the one that fits your workload. Compare chip generation, GPU networking, region, commit terms and data costs on one plan, not a ranking.
The Clectiv team ·
The best GPU cloud is the one that fits your workload, and no list of names can tell you which. A price per GPU hour is one number out of several. Put them all on the same plan and the choice gets clearer.
What should we compare besides the price per GPU hour?#
No provider is best for everyone, so compare on your workload. A price per GPU hour tells you what one chip costs for an hour. It doesn't tell you whether your job will run well on that chip, or what the rest of the bill looks like.
| Compare | What it means | The question |
|---|---|---|
| Chip generation | Which GPU it is, and how new | Is it the chip your software was built for? |
| Networking | How the GPUs are linked to each other | Can a big job spread across many servers without slowing down? |
| Region | Where the GPUs physically sit | Is it near your data and your users? |
| Commit terms | How long you sign for, and how much | What do you owe if you use less? |
| Data costs | What it costs to move data in and out | What does it cost to leave? |
| Support | Who helps when a job fails | Who answers at night, and how fast? |
A chip your software wasn't built for costs you engineering time. Some AI software is tuned for one maker's chips, so ask what your team's code expects before you compare anything else.
Providers sell GPUs in different shapes, which is why one plan matters. Megaport rents bare-metal GPU servers, meaning a whole physical machine that's yours alone. Akamai sells cloud GPUs.
Same question, two different answers. Each is one of our cloud providers, and Clectiv AI runs the process for you from start to finish: start a project.
Before you call anyone#
Start the comparison in Clectiv AI, not on a provider's pricing page. Then Clectiv AI runs the process: it bids the work out to the providers that fit, they compete, and you choose. Call one first and you're comparing its numbers with nothing to hold them against.
Training or inference: what changes in the choice?#
Training and inference lean on a GPU cloud in different ways. Training teaches a model. It's a big job that runs on many GPUs working together. Inference runs the finished model for your users, day after day.
Ask which one this purchase is for. The answer changes the term, the region and the networking you'll pay for.
- Training cares about the links between GPUs. Slow networking between servers makes a big job crawl.
- Inference cares about place and steadiness. It runs close to your users, and demand rises and falls through the day.
- Both care about the date. A cluster that shows up late is the same problem either way.
You may be doing both. Write down what you'll run with Clectiv AI, including how much of each. The plan says what to weigh, and it's the plan that decides, not a name.
Use what others already learned. Clectiv AI runs the process we've run for customers like you, so you start where they finished. These providers see the plan directly, which is how you get the best pricing, terms and contract we can get.
What do the contract terms hide?#
The hourly price is the part you can see. The terms are where the bill moves. Four are worth reading twice.
- Commit length. A longer term locks you in for longer. Ask what you get for it, and what you owe if your needs change.
- Data leaving. Moving data out of a provider can be billed, and AI work moves a lot of data.
- Minimums and renewal. A floor on what you spend, and what the price does when the term ends.
- A slipped date. If the GPUs show up late, do you pay anyway? Can you leave?
One hint: ask whether you can move to a newer chip partway through the term. Chips change often, and a term shouldn't freeze you on the old one.
Every answer should say what's in the price, what's billed on top, and what leaving costs. The plan asks the rest, and the team pushes on it before you decide.
Common questions#
Which GPU cloud provider is best?#
What should we compare besides the price per GPU hour?#
Does the choice change for training and inference?#
Can we use more than one GPU provider?#
How does Clectiv AI get this done?#
Bring it to Clectiv AI. Tell it what you're training or serving, where your data lives, and when you need GPUs. It scopes your GPU project with you and writes your plan: the workload, the chips, the region and the terms.
Then the Clectiv team takes your plan to the GPU and cloud providers that fit, all at once, through our partner network of 620+ providers. Those providers bid and compete, the team pushes on terms before anything gets signed, and you choose.
Other buyers already wore this path in. Clectiv AI runs the process we've run before, so you start where other buyers ended up. Then the providers that fit see the plan directly, for the best pricing, terms and contract we can get on each.
It's free to you, and the bids come back in days, not months. For all of cloud, see the cloud page.
If you're still working out where the GPUs will come from, read how to get GPU capacity.
Sources and method. No prices appear in this guide, and nothing in it ranks providers or says who has GPUs, how many, or when. Provider names are examples of who sells GPU compute, from Clectiv's provider directory.