GPU VPS Pricing: Is Contabo the Cheapest GPU Cloud in 2026? 

What you need to know 

• Contabo GPU VPS costs $1,199 a month for one NVIDIA RTX PRO 6000 Blackwell Server Edition, with 96 GB of video memory on the card, 18 processor cores, 96 GB of RAM and 900 GB of NVMe storage. No setup fee. 

• Of the nine configurations we priced across seven providers in September 2026, only two came in lower. 

• For a card that stays yours for the whole month, $1,199 was the cheapest rate we found. 

• All figures are on-demand list prices in USD, checked September 2026. 

For reliable GPU cloud that remains dedicated to your use, the answer is yes! Contabo GPU VPS is $1,199 a month for an RTX 6000 Pro class card with 96 GB of video memory, 900 GB of NVMe storage and no setup fee. Two rows in the table below undercut it, both by selling spare or marketplace capacity rather than a machine you keep. Against everything that stays yours for the full month, $1,199 is the cheapest GPU cloud rate on the market. 

Cheapest GPU Cloud Providers Compared: Full Pricing Table 

Everything below is what providers had on their pages in September 2026, on demand, single 96 GB card. The card is the same across the whole comparison. That’s important, because the moment you start putting a 48 GB part next to a 96 GB one, the whole GPU cloud cost comparison turns into noise, and plenty of pricing roundups do exactly that. Reserved rates aren’t here. They’ll beat these numbers, sure, if you’re happy committing to a year or three. And the H100 price per hour is a separate conversation. Different cards for different budgets. 

GPU cloud cost comparison, September 2026. On-demand list pricing, single GPU, USD, excluding tax. * Monthly figure calculated from the published hourly rate at 730 hours. 

GPU VPS vs Dedicated GPU Servers: Why the Price Gap Exists 

A GPU VPS costs less because you’re not renting the whole machine. 

The graphics card is yours alone. Everything around it; the processor, the memory, the network connection, gets shared with other customers on the same physical server. Think of it like renting a room in a shared flat rather than taking the whole place. Same bed, same desk, but you split the kitchen. 

The card itself isn’t cheap. An RTX PRO 6000 costs the same to buy whether it sits on a shared server or one that’s all yours. What changes is how much of the surrounding computer lands on your bill. 

The hourly column makes the gap easy to see. Hetzner’s bare metal server runs $2.24 an hour and IONOS asks $3.25. Spread across a month, the Contabo GPU VPS works out to around $1.64 for the same card. Nearly a third less than Hetzner, and roughly half what IONOS charges, for hardware that does the same job. 

What that extra money buys on bare metal is the rest of the machine. Hetzner’s server comes with 256 GB of memory and 24 processor cores. Leaseweb gives you 128 GB and two processors. IONOS goes up to 256 GB across two more. Powerful kit, and none of it wasted if your work needs it. 

Some jobs lean on the processor rather than the graphics card. Video encoding does. So does chewing through large amounts of text or reading huge files off the disk fast enough to keep the card busy. Get that part wrong, and you’ve rented an expensive graphics card that spends its day waiting for work. That’s when the whole machine earns back the difference. 

Here’s the honest trade-off, then. The Contabo plan gives you 18 processor cores and 96 GB of memory alongside the card. Plenty for running a trained model, and enough for most smaller training jobs. Less breathing room than a 256 GB machine, though. Pile heavy data preparation on top of your model and you’ll hit the limit. 

 We think that Contabo GPU VPS strikes the perfect balance between all the hardware features to give you a powerful piece of high-performance computing power.Where Reserved Hyperscaler Pricing Still Wins 

There are two ways AWS and Google Cloud get cheaper than their list price, and one of them can undercut anything in the table above. 

To land under $1,199, a discount would need to take roughly two thirds off that. Multi-year committed-use terms can reach that range, so this is a real competitor rather than a theoretical one. Check the current rates for your own region and term. 

What you’re buying with a commitment is predictability rather than the lowest number on the page. And it comes with three things attached. 

• The promise itself: You pay for twelve months whether the work materializes or not. Projects get cancelled. Models get replaced by better ones. A year is a long time to be certain about what you’ll need. 

• The setup around it: Reserved capacity on a hyperscaler means navigating instance families, regions, quotas and billing accounts before you get to the useful part. Some teams have someone whose job that is. Most small ones don’t. 

• Data transfer: Sending files off their network still costs extra, commitment or no commitment. For work that produces large outputs, that line has a habit of eating the discount you negotiated. 

When Paying by the Hour Beats Paying by the Month 

The second way is spare capacity, which Amazon calls spot pricing. The same 96 GB card goes for $1.43 an hour on AWS that way, which comes to about $1,044 across a full month. That’s below $1,199 at any level of use, so on price alone, hourly wins. 

Google Cloud’s spot rate is higher, at $1.7185. There the crossover sits near 698 hours a month. Run the card less than that and Google Cloud Spot is cheaper. Run it more and the flat monthly rate is. 

Three things an hourly rate leaves out, though. Spare capacity gets reclaimed with barely any notice, so a training run that takes three days needs save points and a way to pick up where it left off. Storage is billed separately on the Google Cloud instances. And sending files off either network costs extra, which for work producing large outputs has a habit of eating the saving. 

So, the split is fairly clean. Work that arrives in bursts and survives being interrupted belongs on hourly pricing, and a year-long commitment makes sense if your workload is predictable that far out, and you need the rest of the platform around the card. If what you actually need is one card and somewhere to run it, a flat monthly rate is the cheaper answer. 

Why Run Your GPU VPS Workload on Contabo 

$1,199 a month gets you one NVIDIA RTX PRO 6000 Blackwell Server Edition with 96 GB of VRAM, 18 processor cores, 96 GB of RAM and 900 GB of NVMe storage. No setup fee, no commitment, and the card stays yours. 

Against the other on-demand GPU VPS options for the same card, that’s about half what AWS charges and roughly a third of Google Cloud’s rate. Those are the closest like-for-like comparisons here, because they’re the only other providers selling a virtual machine with a dedicated card and guaranteed availability. 

The included storage matters more than it looks in that list. Model files are enormous and they multiply. A single video generation setup can pull down 40 or 50 GB of files before it renders a frame, and that’s before you keep a few versions around to compare results. Google Cloud’s figure includes none. 

The price is flat and monthly, too, so working out next quarter is multiplication rather than forecasting. Month four costs what month one cost. 

Worth stating clearly before you order. 

This is an unmanaged VPS, with one useful exception. The NVIDIA driver, CUDA and the NVIDIA-container-toolkit come preinstalled, so a CUDA container runs on first boot rather than after an evening of matching driver versions to toolkit versions. Everything above that layer is yours: your framework, your models, your inference server, patching and backups. There’s no managed layer between you and the hardware. If you need a platform that schedules jobs and manages models for you, one of the hyperscalers is the better fit. 

FAQ: GPU VPS Pricing