Every cloud GPU provider’s homepage says the same three things: fast, scalable, affordable. None of that tells you what actually happens after you sign up.
If you’re an AI startup shopping for a cloud GPU provider, the decision usually comes down to five things nobody puts on the pricing page. Most founders compare hourly rates first, since that’s the number everyone leads with. It’s also the least useful number for predicting what you’ll actually pay, or how the next six months go.
Get these five wrong and you’re re-platforming mid-project, arguing with finance over a bill nobody predicted, or stuck waiting on a support ticket during a demo. Get them right and the hourly rate barely matters.
This applies whether you’re two people fine-tuning your first model or a funded team scaling a production API. The questions stay the same. Only the cost of getting them wrong changes.
Here’s what to actually check before you sign anything.
1. What “free” cloud GPU actually means
Free usually means a small trial credit, not sustained GPU time you can actually build a product on.
Every major provider, hyperscaler or specialised cloud, hands out some version of free credits. That’s not a trick, it’s just how the industry gets you in the door. And they’re genuinely useful, for testing a model, running a demo, or validating an idea before you spend real money.
Where it falls apart is scale. A single H100 or H200 training run can chew through a typical trial credit in hours. If your plan depends on stretching that free tier for months, you don’t have a plan. You have a countdown.
Before you sign up for any free tier, check three things:
- How many GPU-hours the credit actually covers
- Whether it expires on a calendar date or on usage
- What the price looks like the moment it runs out
That last one is the number that actually matters.
Budget for real GPU costs from day one.
CloudPe’s GPU instances start at Rs. 26,180 a month.
2. Whether you need a training GPU or an inference GPU
These are two different purchases, and startups mix them up constantly. Training needs memory and raw compute. Inference needs a lot less of both.
| Workload | What it needs | GPU class |
| Serving a trained model to users | Efficiency, lower cost per request | L4 |
| Training or fine-tuning under 30B parameters | Balanced memory and compute | A100 |
| Training or fine-tuning 70B+ parameters | High memory, long context | H100 or H200 |
| Computer vision training | High compute, moderate memory | A100 or H100 |
| 3D rendering, simulation | High single-GPU throughput | RTX Pro 6000 |
If I have to explain it with an example, here it is. A startup serving a fine-tuned 8B parameter chatbot to users doesn’t need 141GB of memory. Most of it will just sit unused. The better call is the L4, since the specs actually match the job. Now flip it. A startup training a 70B parameter model from scratch needs every bit of that memory. Put that on an inference GPU and it won’t even load the model.
If you’re scaling training across more than one GPU, check the form factor too, not just the model name. Multi-GPU training lives or dies on fast GPU-to-GPU communication, and that’s a separate spec from memory or compute.
If your training workload genuinely needs H200-class memory, it’s worth checking H200 GPU cloud pricing separately, since it sits in a different bracket than inference-class GPUs. The extra memory on an H200 over an H100 doesn’t make it faster, by the way. It just gives you room for longer context and bigger batches, and that only pays off if your workload is actually memory-bound to begin with.
CloudPe runs H200, H100, A100, L4, and RTX Pro 6000 instances, all hosted in India, so you can match the GPU to the workload instead of guessing.
See current GPU configurations3. What is actually in the cloud GPU bill besides the hourly rate
Egress fees, storage, and spot preemption risk, and together they can tack on 20 to 40% to your monthly bill. The hourly rate is the number every provider leads with. It’s also the smallest part of the real bill for most startups.
- Egress fees: Moving data or model weights out of the provider’s network, usually billed per GB.
- Storage: Model checkpoints and datasets sitting on disk between training runs.
- Spot preemption: Discounted instances that get reclaimed mid-job, sending your training run back to the last checkpoint.
- Forex exposure: Dollar-denominated bills moving with the rupee, on top of usage itself.
Say one provider quotes $2 an hour and another quotes $3. Add egress for moving 500GB of training data out for evaluation, a few months of checkpoint storage, and a spot instance that got reclaimed twice mid-run. The cheaper number on the homepage can end up being the pricier invoice.
CloudPe bills in INR with no egress fees, so the number on the pricing page and the number on the invoice are the same number.
Contact us here to know more
Before you sign anything, ask for a sample invoice from a real customer workload. If a provider can’t produce one, that’s an answer in itself.
4. Whether you can scale without re-platforming
Check this before you sign. Moving from one GPU to a cluster mid-project means migrating data, rebuilding your deployment pipeline, and losing weeks you don’t have.
This usually hits at the worst time, right after a funding round or a customer win, when the workload jumps overnight and everyone wants it shipped yesterday. A provider that can’t take you from one GPU to eight without a re-platforming project turns your best week into your worst one.
Re-platforming isn’t just moving a workload either. It’s re-exporting datasets, rebuilding CI/CD around a new API, and re-testing everything that depended on the old setup, usually while the product is still live.
- Bare metal vs. virtual machine vs. container. Bare metal gives you dedicated hardware, worth it once you’re past the experimentation stage.
- Single-GPU to multi-GPU path. Going from 1x to 2x, 4x, or 8x GPUs should be a config change, not a migration.
- Managed orchestration. Kubernetes support matters once you’re running more than one model in production.
CloudPe supports single, 2x, 4x, and 8x GPU configurations across H200, H100, A100, L4, and RTX Pro 6000, plus managed Kubernetes for teams running multiple models.
Check live GPU availability5. What happens when something breaks at 2am
If you are a startup, there is a high chance that you don’t have an SRE team on call, so your GPU provider’s support response time is basically your incident response plan.
A training run that silently stalls costs you GPU-hours and time either way. The only question is whether you find out in ten minutes or the next morning. For a startup, that gap can be the difference between a small delay and a blown demo day, a missed customer SLA, or a run that needed to finish before an investor update.
With CloudPe you get 99.9% uptime SLA. Support resolution under 2 hours, from a team of 120+ engineers working 24/7.
Talk to us before you commitQuick checklist before you sign
None of these five questions show up on a pricing page. All of them show up in your first three months with a provider.
| # | Question | Why it matters |
| 1 | What does “free” actually include? | Avoids budgeting around credits that run out in hours |
| 2 | Training or inference GPU? | Wrong choice means overpaying or jobs that don’t finish |
| 3 | What’s billed besides the hourly rate? | Egress and storage can add 20-40% to your bill |
| 4 | Can you scale without re-platforming? | Avoids a migration mid-project |
| 5 | What’s the support response time? | Your incident response plan, whether you planned for it or not |
Run any cloud GPU provider you’re evaluating through this list before you sign anything. The one that scores well across all five usually isn’t the cheapest per hour. It’s the one whose actual cost, actual uptime, and actual support match what you were told at sign-up.
Frequently Asked Questions
Is there a free cloud GPU for AI startups?
Free tiers exist as trial credits, not sustained GPU time. They’re genuinely useful for testing an idea, but budget for real usage from the start rather than planning a launch around a free tier.
What GPU do I need to train an AI model?
It depends on model size. Under 30B parameters, an A100 is usually enough. At 70B or larger, or with long-context workloads, you need H100 or H200-class memory. Nvidia’s own data centre GPU lineup covers the full range if you want to compare specs directly.
Should an AI startup use bare metal or virtual machines?
Virtual machines are the right starting point for experimentation, since they’re faster to spin up and easier to resize. Bare metal makes more sense once you have a stable, high-utilisation workload and want dedicated hardware without a shared-tenancy performance ceiling.
What’s the cheapest way to run GPU inference?
An efficiency-focused GPU like the L4 costs less per request than a training-class GPU for serving an already-trained model.
Do spot instances save AI startups money?
Yes, often significantly, but the job can be paused or reclaimed with little notice. Fine for fault-tolerant training, risky for anything time-sensitive.
What hidden costs should AI startups watch for in GPU cloud pricing?
Egress fees, storage, and forex exposure on dollar-denominated bills. Together, these can add 20 to 40% on top of the hourly GPU rate.
How fast should GPU cloud support respond to an incident?
For a startup without an in-house SRE team, under 2 hours is a reasonable bar. Ask any provider directly what their actual resolution time is, not just what their SLA page claims.
