What to look for in a GPU cloud (besides the price tag)

The GPU cloud market has exploded. There are dozens of providers offering NVIDIA GPU instances, and the natural instinct is to compare them on price per GPU-hour. That's a starting point, but it misses the factors that actually determine whether a provider will work for your workload.

Here's what to evaluate beyond the headline rate — and what GPU.ai's aggregation layer surfaces for you automatically.

1. Bare metal vs. virtualized

This is the single biggest differentiator that most comparison guides skip. Virtualized GPU instances — the default at most providers — run through a hypervisor that adds 10–15% overhead on memory-intensive workloads. For a quick experiment, this doesn't matter. For a month-long training run, it's the difference between finishing on schedule and slipping by a week.

Ask whether you're getting bare metal access or a virtualized instance. If the provider can't answer clearly, assume virtualization.

2. Interconnect topology

If you need more than 8 GPUs, interconnect becomes critical:

Within a node: Is NVLink available between all GPUs, or only in pairs? Full NVLink mesh vs. partial connectivity dramatically affects tensor parallelism performance.
Between nodes: Is InfiniBand available? What generation — HDR, NDR, NDR400? Or are nodes connected via ethernet? For distributed training, this is often the bottleneck.
Network isolation: Is your inter-node traffic competing with other tenants, or do you have dedicated bandwidth?

Many providers advertise GPU specs but are vague about interconnect. For training workloads, the network is as important as the GPU.

3. Real availability, not pricing-page availability

"Available" on a pricing page and "available right now for your configuration" are different things. Check:

Can you actually provision the GPU type and count you need today?
What's the lead time for larger configurations?
Are instances preemptible/spot, or guaranteed?
What's the provider's track record on uptime?

The cheapest provider isn't cheap if you can't get instances when you need them. This is why GPU.ai's availability feed polls every 30 seconds across suppliers — we publish actual real-time stock, not aspirational SKUs.

4. Support quality

GPU infrastructure is complex. Things break — NVIDIA drivers have issues, InfiniBand links flap, NCCL hangs during distributed training. When they do, you need someone who understands the stack, not a generic support ticket system.

Ask about response time SLAs. Ask whether support engineers have actually operated GPU infrastructure. Ask for direct communication channels — Slack-shared, not just ticketing.

5. Data location and compliance

For regulated industries or international teams, where your data physically resides matters. Understand:

Which datacenter(s) will your instances run in? (Region, not just "U.S." — us-east-1a and us-east-1b are not the same.)
Does the provider offer data residency guarantees?
Are there compliance certifications — SOC 2, ISO 27001, HIPAA where relevant?

Multi-region matters more than ever. Our partnership with NovaCore exists precisely because "U.S. east coast and India, billed together" was something customers were asking for and no single provider was solving cleanly.

6. Contract flexibility

The GPU cloud market is moving fast. Hardware generations turn over every 12–18 months. Locking into a rigid multi-year contract on current-gen hardware may not serve you well.

Look for providers that offer:

Flexible terms — month-to-month or quarterly, not just annual.
Hardware upgrade paths as new generations ship.
The ability to scale up or down without penalty.

If a provider's pricing only makes sense at a one-year commitment, ask yourself what they're actually selling you.

7. Pricing transparency

The single most important and most rare attribute: can you see the real price, in real time, without a sales call?

A surprising number of GPU "clouds" still gate H100 pricing behind a calendar invite. That's not a cloud — that's a colocation contract with a website. If a provider can't quote you a public per-second rate, they cannot be priced against; they cannot be optimized against; they cannot be left.

The bottom line

Price per GPU-hour is table stakes. The providers worth working with differentiate on bare metal performance, interconnect quality, actual availability, support expertise, and the willingness to publish their real numbers. Those factors compound over time and determine whether your infrastructure accelerates your AI work or becomes a constant source of friction.

We built the aggregation layer so you don't have to evaluate every provider individually on every one of these axes. You query once, we surface the best fit, you ship.