Executive Summary
A $519 GPU server and a GPU instance billed at $3.29 per hour can both look attractive. Which one makes financial sense depends on what you’re doing with the hardware. A developer running a training job for a weekend has different requirements from a company operating an AI application 24 hours a day. The first may be better served by hourly computing. The second could spend considerably more than necessary by leaving a cloud GPU instance running month after month.
That’s what makes GPU server hosting comparisons more complicated than they appear. ProlimeHost, DonWeb, ServerMania, OVHcloud, Lambda, and CoreWeave all provide access to GPU computing, but they don’t necessarily serve the same customers or sell the same type of infrastructure.
We’ll compare their offerings, examine hardware differences, and put some actual numbers behind the monthly and three-year expenses. We’ll also look at a few things that tend to receive less attention during the purchasing process, including GPU memory, system RAM, storage expansion, network allowances, and deployment times.
The objective isn’t to crown one provider the winner in every category. It’s to help businesses avoid spending money on GPU capacity they don’t need while making sure the hardware they order can handle the work.
Why GPU Hosting Prices Are So Difficult to Compare
A customer asks for an NVIDIA RTX 5090 server. Another wants an A100 with 80 GB of memory. A third isn’t sure which GPU to choose but knows the application needs to process thousands of requests every day.
All three are shopping for GPU hosting, but they aren’t necessarily shopping for the same solution.
An RTX 5090 has 32 GB of VRAM. That’s plenty for many applications, but not every AI model will fit into that amount of memory. An A100 80GB provides considerably more VRAM and different capabilities. The additional expense may be justified, although buying more GPU memory than an application needs accomplishes little.
CPU selection can be just as important. Data preparation, application services, database requests, and other background processes don’t disappear simply because a server has a powerful GPU. An undersized processor can hold up the entire operation.
The same applies to RAM and storage. A server with 96 GB of memory may be perfectly adequate for one application and frustratingly limited for another. NVMe storage can help with large datasets, but customers also need enough drive capacity to accommodate future growth.
Then there’s the question of utilization.
A GPU that runs for 40 hours a month presents a very different financial picture from one operating nearly continuously. That’s why I prefer beginning with the application rather than a particular GPU model. What does the software need, how heavily will it use the hardware, and how long is the project expected to run?
Those answers tell us far more than an advertised starting price.
GPU Server Hosting Compared: Six Providers Worth Considering
The providers covered here represent two broad approaches to GPU infrastructure: dedicated physical servers and cloud-based GPU computing.
| Provider | Hosting approach | Billing model | Typical applications |
|---|---|---|---|
| ProlimeHost | Dedicated GPU servers | Monthly | AI inference, rendering, private GPU infrastructure |
| DonWeb | Dedicated GPU servers | Monthly | Private AI deployments, Argentina-based hosting |
| ServerMania | Dedicated and custom GPU servers | Monthly/custom | Enterprise applications, specialized hardware |
| OVHcloud | Dedicated GPU servers | Monthly | Bare-metal computing, international infrastructure |
| Lambda | GPU cloud and clusters | Hourly/reserved | AI development, training, temporary workloads |
| CoreWeave | Specialized GPU cloud | Usage-based/contract | Large AI training and inference deployments |
With dedicated hosting, customers rent a physical server containing the GPU, CPU, RAM, storage, and network resources specified in the agreement. Cloud GPU services provide computing capacity through a different arrangement, often making it easier to obtain resources temporarily or scale a project.
Neither approach is automatically less expensive. The answer changes with the workload.
ProlimeHost: Dedicated GPU Servers With Monthly Pricing
ProlimeHost offers dedicated GPU servers built around NVIDIA accelerators and AMD Ryzen or EPYC processors. These are physical machines rented on a monthly basis, with the CPU, memory, storage, and network resources included in the quoted configuration.
Take the RTX 5090 system as an example. At a referenced $519 per month, it comes with a Ryzen 9950X processor, 96 GB of RAM, 3.84 TB of NVMe storage, and 50 TB of transfer on a 10 Gbps port. That’s a substantial amount of supporting hardware for a single-GPU server, particularly for applications that need fast local storage alongside GPU processing.
The monthly rental is the same whether the GPU is busy for a few hundred hours or running nearly continuously. There may be additional charges for optional services or bandwidth beyond the included allowance, but the base rental isn’t calculated from GPU hours.
For a company operating its own inference service, that arrangement removes one variable from the monthly budget. The remaining question is whether the RTX 5090 and the rest of the server can handle the application at the required performance level.
ProlimeHost GPU Server Configurations
| GPU configuration | Processor | RAM | NVMe storage | Monthly reference price |
|---|---|---|---|---|
| NVIDIA RTX A5000 | AMD Ryzen 9950X | 96 GB | 3.84 TB | $429 |
| NVIDIA RTX 5090 | AMD Ryzen 9950X | 96 GB | 3.84 TB | $519 |
| 4× NVIDIA RTX 3090 | AMD EPYC 7443P | 128 GB | 1.92 TB | $749 |
| 2× NVIDIA A100 80GB | AMD EPYC 7443P | 512 GB | 2× 3.84 TB | $1,649 |
Prices and specifications are historical reference configurations. Confirm current availability and quotation terms before ordering.
The hardware choices cover considerably different requirements. A single RTX 5090 can be useful for inference and development work that fits within its memory capacity. Multiple RTX 3090 GPUs may appeal to applications capable of distributing work across several accelerators. A100 systems offer higher individual GPU memory capacity for workloads that require it.
It’s worth remembering that four GPUs with 24 GB each don’t automatically behave like one GPU with 96 GB. Software support, communication between accelerators, and memory allocation all affect what a multi-GPU configuration can accomplish.
ProlimeHost also offers custom hardware requests, subject to compatibility and availability. That can be useful when a standard configuration needs more RAM, additional storage, or another adjustment.
GPU hosting locations include Los Angeles, Dallas, New York, and Utah. Customers can review the configurations at ProlimeHost GPU Dedicated Servers.
DonWeb vs ProlimeHost: Geography and Network Capacity
DonWeb hosts dedicated GPU servers in Argentina and has advertised systems equipped with NVIDIA RTX 3090 and RTX 5090 accelerators. Its GPU hosting information also references tools such as CUDA, Docker, Ollama, and Open WebUI.
That software support may be convenient for customers setting up private AI environments, although I’d still confirm exactly what’s installed and configured before ordering.
Argentina is an important part of this comparison. A business with customers and supporting infrastructure in South America may prefer hosting there rather than routing application traffic to a US data center. Depending on the application and network routes, a nearby facility can improve response times.
ProlimeHost’s GPU hosting locations are in the United States, including Los Angeles, Dallas, New York, and Utah. The two companies therefore serve somewhat different geographic needs, even when the GPU hardware is similar.
Their advertised network specifications aren’t identical, either. DonWeb has listed 1 Gbps connectivity, while ProlimeHost’s RTX 5090 system includes a 10 Gbps port with 50 TB of monthly transfer.
For customers moving large datasets, that difference deserves attention. A 10 Gbps connection has a higher theoretical transfer capacity, although actual speeds depend on network conditions and service terms.
I’d be interested in DonWeb’s current RTX 5090 pricing before drawing a financial comparison. Without a confirmed quotation, the more useful distinctions are location, network capacity, and what comes with the server.
ServerMania vs ProlimeHost: What Does the Extra $360 Buy?
ServerMania’s previously advertised starting price of $879 per month is $360 higher than ProlimeHost’s $519 RTX 5090 configuration. That’s $4,320 over a year and $12,960 over three years, assuming the prices remain unchanged.
The catch is that these are starting figures for different hardware offerings, not two quotations for identical servers.
ServerMania offers custom GPU infrastructure, including configurations that may be better suited to enterprise applications. If a customer needs an accelerator with 80 GB of VRAM or particular enterprise capabilities, an RTX 5090 with 32 GB isn’t going to be an adequate substitute.
But what about an application that runs perfectly well on the RTX 5090? In that case, I’d have a hard time justifying additional expense unless the more costly server offers another benefit the customer actually needs.
ProlimeHost’s $519 RTX 5090 configuration includes a Ryzen 9950X, 96 GB of RAM, and 3.84 TB of NVMe storage. That’s the hardware included in the price. Before paying ServerMania an additional $360 per month, I’d want to see exactly what they’re putting on the table. More GPU memory? A different processor? Additional storage or network capacity? Any of those might justify the expense, depending on the application.
Over three years, that $360 difference adds up to $12,960. That’s a substantial amount to spend without knowing what you’re getting in return.
OVHcloud vs ProlimeHost: What Happens When You Need More Hardware?
OVHcloud offers GPU-equipped dedicated servers and has an international hosting presence. For businesses already using its infrastructure, keeping everything with one provider may simplify network administration and billing.
But hardware requirements rarely stay frozen.
A company might order a server with 96 GB of RAM, then find itself needing 192 GB as the application grows. Can the existing machine accommodate that upgrade, or does the customer have to move everything to another server? That depends on the motherboard, available memory slots, installed modules, and what the provider is willing or able to change.
Storage presents similar questions. A server with limited drive bays may be fine initially, but adding several terabytes of NVMe storage later could be difficult. Before ordering from OVHcloud or ProlimeHost, I’d want a clear answer about the machine’s expansion limits. Migrating an active application because the original server has run out of room isn’t anyone’s idea of a good afternoon.
OVHcloud has the advantage of a larger international footprint. ProlimeHost concentrates on US locations and can accommodate custom hardware requests when the equipment is available. I’d be particularly interested in what happens when a customer outgrows the original server. Can another 96 GB of RAM be installed? Is there room for additional NVMe drives? If not, how much downtime would a move to another machine involve? Those answers could matter a lot more twelve months down the road than a small difference in the original monthly price.
Lambda vs ProlimeHost: When Hourly Billing Stops Being a Bargain
Lambda’s hourly GPU services can make good financial sense for temporary computing jobs. A developer can rent an accelerator, run an experiment, and release the resources afterward, subject to the provider’s billing terms.
Consider a 30-hour training job using Lambda’s previously published NVIDIA A10 rate of $1.29 per hour. The compute expense would be $38.70. For that kind of project, renting a dedicated GPU server for an entire month may be unnecessary.
The calculation changes when the application remains online.
At 730 billable hours per month, that same A10 rate produces a charge of $941.70. Lambda’s previously published H100 PCIe rate of $3.29 per hour would reach $2,401.70 under the same assumption.
ProlimeHost’s RTX 5090 server has a reference price of $519 per month.
Of course, the hardware isn’t equivalent. An H100 PCIe has 80 GB of GPU memory, compared with 32 GB on the RTX 5090. The accelerators also have different computing capabilities, so their performance on a particular workload may vary substantially.
For applications that can operate effectively on an RTX 5090, though, continuous hourly billing deserves a close look.
At $1.29 per hour, 100 billable hours costs $129. Five hundred hours costs $645. Those numbers illustrate how quickly the monthly expense changes as utilization increases, although they don’t account for performance differences between GPUs.
A development project can also change over time. What begins as a short experiment may become a production service, and the hosting arrangement that made sense during testing may no longer be the least expensive option.
I’d review the billing model again once the application’s computing requirements become predictable.
CoreWeave vs ProlimeHost: When One GPU Isn’t Enough
CoreWeave operates specialized GPU cloud infrastructure for demanding AI workloads, including large training and inference deployments.
The infrastructure requirements can be substantial. Multiple accelerators may need to exchange data at high speed, storage must keep up with the computing workload, and the software has to coordinate processing across several machines.
That’s quite different from operating a private inference application on one dedicated server.
Consider an internal document-search system. Depending on the model and expected traffic, the application might need only one GPU, enough system RAM for the supporting services, and fast storage for the document index.
A large training project has another set of requirements entirely. It may need many accelerators, high-speed communication between them, and infrastructure designed to support distributed computing.
CoreWeave is relevant to those larger deployments. ProlimeHost’s dedicated GPU servers address customers who want physical hardware assigned to their own applications, including single-server and multi-GPU configurations.
The deciding factor is the computing job. A small organization can have an enormous AI workload, while a large enterprise may need only one accelerator for a particular internal service.
GPU Hosting Pricing: Compare the Entire Server, Not Just the GPU
GPU hosting prices aren’t always presented on the same basis. A monthly dedicated server rental may include CPU, RAM, storage, and a network allowance. Hourly GPU computing may involve separate charges for storage, data transfer, or other services.
Here’s a summary of the reference prices used in this comparison.
| Provider / configuration | Reference price | Billing |
|---|---|---|
| ProlimeHost RTX 5090 dedicated server | $519/month | Monthly |
| ServerMania GPU hosting | From $879/month | Monthly |
| Lambda NVIDIA A10 24GB | $1.29/hour | Hourly |
| Lambda NVIDIA H100 PCIe 80GB | $3.29/hour | Hourly |
| DonWeb RTX 5090 | Quote required | Monthly |
| OVHcloud dedicated GPU | Configuration-dependent | Monthly |
| CoreWeave GPU cloud | Configuration-dependent | Usage/contract |
Competitor figures are historical reference prices and have not been reconfirmed for this edition. These are not equivalent hardware configurations.
I’d want the full quotation before making a purchasing decision. That means the exact GPU model, CPU, installed memory, storage, network allowance, setup charges, and any recurring extras.
A 10 Gbps port with 50 TB of included transfer, for example, isn’t the same thing as 10 Gbps unmetered bandwidth. Those details may be relatively unimportant for a small inference application but significant for a business moving large datasets every day.
Three-Year GPU Hosting Costs: What the Monthly Bill Doesn’t Show
A $519 monthly server rental may not sound like a major infrastructure expense, particularly when compared with the price of high-end GPU hardware. Stretch that commitment across three years, though, and you’re looking at $18,684. That’s assuming the monthly rate stays at $519, with no additional services or changes to the configuration.
Hourly billing tells a different story. The cost depends on how long the instance remains active and billable. For the Lambda examples below, we’re using 730 hours per month. Over three years, that works out to 26,280 hours. It’s a useful way to illustrate continuous usage, even though an application may not fully utilize the GPU every hour.
| Hosting option | Monthly equivalent | Annual cost | Three-year cost |
|---|---|---|---|
| ProlimeHost RTX 5090 | $519.00 | $6,228.00 | $18,684.00 |
| Lambda A10 at $1.29/hour | $941.70 | $11,300.40 | $33,901.20 |
| Lambda H100 PCIe at $3.29/hour | $2,401.70 | $28,820.40 | $86,461.20 |
These figures illustrate rental and compute expenses using historical reference rates. The GPUs aren’t performance-equivalent, and the calculations exclude taxes, discounts, optional services, and other potential charges.
The numbers make an important distinction between renting capacity continuously and paying only when it’s needed.
They don’t tell us which GPU finishes a particular job fastest or which accelerator is suitable for a specific model. Those questions require workload testing.
Still, the financial implications are substantial enough that I’d want a three-year projection before committing to an infrastructure arrangement expected to remain in service for several years.
We’ve examined similar long-term cost questions in our article, Cloud Storage vs Dedicated Storage Servers, where recurring expenses can also change the financial picture considerably.
The Hardware Outside the GPU Can Make or Break the Deployment
An expensive GPU won’t solve every performance problem. Sometimes the limiting factor is a processor that can’t prepare data quickly enough. Other times it’s system memory, disk performance, or the network.
A busy inference server, for example, may spend considerable CPU time handling requests and preparing inputs. If the processor can’t keep up, the GPU may spend part of its time waiting for work.
Memory shortages create their own problems. Databases, application services, and cached information can consume far more RAM than expected. Once the operating system starts swapping heavily to disk, performance can deteriorate.
Storage tends to get overlooked until the application starts filling the drives. AI models, training datasets, and saved checkpoints can consume several terabytes, sometimes much sooner than anticipated. NVMe storage helps with read and write performance, but capacity matters just as much. A fast drive that’s nearly full isn’t much use when the next dataset arrives.
Bandwidth is another area where the advertising can be confusing. A server with a 10 Gbps port doesn’t necessarily include unlimited transfer. ProlimeHost’s RTX 5090 configuration, for example, includes 50 TB per month. That’s a substantial allowance for many applications, but a customer moving large datasets regularly needs to know what happens after reaching it.
I’ve seen server specifications where nearly all the attention goes to the GPU, while the CPU, RAM, and storage are treated as secondary details. That’s a mistake. A fast accelerator doesn’t accomplish much when the rest of the machine can’t keep it supplied with data. Sometimes another 64 GB of system memory or an additional NVMe drive will do more for an application than upgrading the GPU.
There’s also no requirement that every supporting service run on the GPU machine. Databases, web applications, and other processes can be hosted separately, leaving the accelerator available for the work it was purchased to do. ProlimeHost offers dedicated servers for those supporting workloads.
Choosing a GPU Hosting Provider Without Overbuying
GPU shopping can get expensive before anyone notices what’s happening. A customer starts with an RTX 5090, sees an accelerator with 80 GB of memory, and begins wondering whether the larger GPU would be a safer purchase. Maybe Executive Summary
It can look tempting to pick up a GPU server for $519 per month or a GPU instance billed by the hour. However, the choice between different providers will depend upon your overall use case. A developer running a training task for a weekend and a company running an AI application will have completely different requirements. The first will potentially benefit from an hourly-based GPU, however the latter could end up paying far more than is needed for a cloud-based GPU instance.
This is why GPU server hosting can be a bit trickier than it looks. ProlimeHost, DonWeb, ServerMania, OVHcloud, Lambda and CoreWeave offer GPU-based solutions, but they are not the same for everyone.
We’ll take a look at the options available and compare the hardware specs. Then we’ll see what the real costs are for each option, both on a monthly and 3-year basis. We’ll also take a brief look at some of the other features that can be overlooked, such as GPU memory, RAM, storage, bandwidth and deployment times.
The aim of this is not to give a clear winner, but to help businesses avoid paying for unnecessary GPU resources, and ensure the server will be able to handle their workload.
Why Comparing Prices Can Be Difficult
Let’s say you want an NVIDIA RTX 5090-based server, or an A100 with 80GB of memory. Or maybe you’re not sure, but you know you need something that can handle thousands of requests per day.
Depending on your needs, the options may not be the same.
An RTX 5090 has 32GB of VRAM, meaning it can handle many applications. However, not all AI models are going to fit into 32GB of memory. An A100 80GB has significantly more VRAM than the RTX 5090, however you may have to pay extra for it. If you don’t need extra GPU memory, it doesn’t really benefit you.
The CPU can also be important. Your data prep, application services, databases etc. will still need to run, even if your server has a powerful GPU. If it doesn’t have a powerful enough CPU, the whole thing will slow down.
Your RAM can also be an issue. A server with 96GB of memory may be perfect for one application, but not for another. Large datasets can also be a problem if you don’t have enough NVMe storage. You should also ensure you have enough storage for the future.
Utilization
If you have a GPU that is only running for 40 hours a month, it will be a completely different financial choice than one that is running almost constantly. Therefore, I’d like to start off by looking at the application. How much does the software need? Will it utilize the GPU heavily? How long will it be running for?
This will tell us much more than the starting price.
GPU Server Hosting Comparison – 6 Options To Consider
We’ll now take a look at the different options. They all offer different options for GPU hosting, either dedicated or cloud-based.
| Provider | Type | Pricing | Common Uses |
|---|---|---|---|
| ProlimeHost | Dedicated | Monthly | AI Inference, Rendering, Private GPU |
| DonWeb | Dedicated | Monthly | Private AI, Argentina-based |
| ServerMania | Dedicated/Custom | Monthly/Custom | Enterprise, Custom Hardware |
| OVHcloud | Dedicated | Monthly | Bare Metal, International |
| Lambda | Cloud/Clusters | Hourly/Reserved | AI Development, Training, Temporary |
| CoreWeave | Specialised Cloud | Usage/Contract | Large AI Training & Inference |
Dedicated will mean you rent a physical server with the GPU, CPU, RAM, storage and network as specified. Cloud-based options will offer you computing power, often making it easier to scale or rent for temporary projects.
The price depends on your needs.
ProlimeHost – Dedicated GPU Server
ProlimeHost offers dedicated GPU servers with NVIDIA GPUs and AMD Ryzen/EPYC CPUs. You’ll pay a monthly fee for the server. The example shows an NVIDIA RTX 5090 with a Ryzen 9950X, 96GB of RAM, 3.84TB of NVMe storage and 50TB of transfer on a 10Gbps port. This server has a lot of hardware to support your single GPU, especially if you need fast local storage.
You’ll pay the same base rental price regardless of how much your GPU is being utilised. You may be charged extra for optional features or extra bandwidth.
If the company is running its own inference service, you know the base monthly price in advance. You’ll just need to make sure the RTX 5090 and other features will handle your application.
GPU Server Specs
| GPU | CPU | RAM | Storage | Monthly Price |
|---|---|---|---|---|
| NVIDIA RTX A5000 | AMD Ryzen 9950X | 96GB | 3.84TB NVMe | $429 |
| NVIDIA RTX 5090 | AMD Ryzen 9950X | 96GB | 3.84TB NVMe | $519 |
| 4 x NVIDIA RTX 3090 | AMD EPYC 7443P | 128GB | 1.92TB NVMe | $749 |
| 2 x NVIDIA A100 80GB | AMD EPYC 7443P | 512GB | 2 x 3.84TB NVMe | $1,649 |
Prices and specs are for historical reference. Please check current availability and pricing.
The options offer a wide range of hardware. Single GPU options such as the RTX 5090 can be good for inference and development tasks. Multiple GPUs such as the RTX 3090s may be better for applications that can utilize multiple GPUs. The A100s offer more memory per GPU.
However, 4 x 24GB GPUs don’t act like one 96GB GPU. This will depend on what software supports it and how the GPUs can communicate and utilize memory.
ProlimeHost also offers custom hardware, so if you need extra RAM, storage or another adjustment to a standard config, you can order it if it is compatible and available.
GPU servers are available in Los Angeles, Dallas, New York and Utah, subject to current inventory. You can find out more about the configurations here.
DonWeb vs ProlimeHost – Location & Bandwidth
DonWeb offers dedicated GPU servers in Argentina. Their servers advertise NVIDIA RTX 3090 and RTX 5090 GPUs. They also mention support for CUDA, Docker, Ollama and Open WebUI.
This can be useful if you are looking to set up your own AI environment. However, make sure you know what you will get.
The location can be an important factor. If you are based in South America and have customers and infrastructure in the region, you may prefer to host in Argentina. If your application is running in a location closer to you, you may get faster responses, depending on network routing.
ProlimeHost offers GPU servers in the US (Los Angeles, Dallas, New York & Utah). Both options offer different locations.
They also offer different bandwidth. DonWeb advertises 1Gbps, whereas ProlimeHost has a 10Gbps port with 50TB of transfer on the referenced RTX 5090 server.
If you are transferring large amounts of data, this is worth considering. You get a higher theoretical transfer speed with 10Gbps, although actual speeds will depend on the network.
I’d like to know what the current price of the RTX 5090 is at DonWeb before comparing. For now, the main differences between the two are location, bandwidth and what you get.
ServerMania vs ProlimeHost – Extra $360?
ServerMania’s previously advertised starting price of $879 per month is $360 more than ProlimeHost’s $519 RTX 5090 configuration. Over a year, this would be $4,320, and over 3 years it would be $12,960.
However, these are not the same servers.
ServerMania offers custom GPU setups. If you need a GPU with 80GB of VRAM or enterprise capabilities, the RTX 5090 with 32GB won’t be suitable for every application.
However, if your application works well with the RTX 5090, it will be difficult to justify the extra cost unless there is some other benefit.
ProlimeHost offers its RTX 5090 server for $519 per month. This configuration includes a Ryzen 9950X, 96GB of RAM and 3.84TB of NVMe storage. That’s the hardware included in the price. Before paying ServerMania an additional $360 per month, I’d like to know what you are getting for this. Is there more GPU memory? A different CPU? Extra storage or bandwidth? This can justify the price depending on your application.
Over 3 years, that $360 difference adds up to $12,960. This is a lot to pay without knowing what you are getting.
OVHcloud vs ProlimeHost – Additional Hardware Needs
OVHcloud offers GPU-enabled dedicated servers. They also offer international hosting. If your business uses their infrastructure, it can be easier to manage your network.
However, your hardware needs are rarely fixed.
If you ordered a server with 96GB of RAM, but then realized you need 192GB due to your application growing, can you upgrade the server? This depends on the motherboard, RAM slots, modules installed and what the provider can do.
Storage can also be a problem. If you have a server with limited storage bays, you may struggle to add several terabytes of NVMe storage. I’d want to know how much the server can be upgraded before ordering from OVHcloud or ProlimeHost. You don’t want to have to migrate an active application due to the first server running out of space.
OVHcloud offers a wider international range, whilst ProlimeHost focuses on the US. ProlimeHost can also offer custom hardware if available. I’d be interested to know what happens if you outgrow the first server. Can you add more RAM? Is there space for more NVMe storage? If not, how much downtime would it take to move to another server? This could be much more important 12 months down the line.
Lambda vs ProlimeHost – Hourly Pricing Can Be Too Expensive
Lambda offers hourly GPU services. This can be a great financial choice if you need something for a short period of time. You can rent an accelerator, perform your experiment and then release it.
If you only need the server for 30 hours, using their previously published price for the NVIDIA A10 of $1.29 per hour, the compute expense would be $38.70. It wouldn’t be worth paying for a dedicated GPU server for that particular task.
However, if your application is running constantly, you will be charged for the hours the instance remains billable. At $1.29 per hour, 730 billable hours would cost $941.70. Lambda’s previously published H100 PCIe rate of $3.29 per hour would be $2,401.70 for the same period.
ProlimeHost’s RTX 5090 server is $519 per month.
Obviously, the hardware isn’t the same. The H100 PCIe has 80GB of GPU memory, whilst the RTX 5090 only has 32GB. The GPUs also have different computing capabilities.
However, if your application works well with the RTX 5090, the hourly pricing can be a big consideration.
At $1.29 per hour, 100 billable hours cost $129, and 500 hours would be $645. This shows how quickly the price can increase depending on how much you are utilising your server. It doesn’t take into account any differences in performance.
Development projects can often change. What started as a small experiment may turn into a live service. This may mean your hosting won’t be the cheapest option anymore.
Once your application has predictable compute needs, I’d look at the pricing again.
CoreWeave vs ProlimeHost – Single GPU Not Enough
CoreWeave offers specialised GPU cloud infrastructure. This is suitable for large AI projects, such as large training and inference workloads.
The infrastructure can be quite intense. If you have multiple accelerators, they may need to communicate at high speeds. The storage will also need to cope with the compute demands. The software will also need to manage the processing across multiple machines.
This is completely different from having your own inference application running on a single dedicated server.
If you are creating an internal search application, you may only need a single GPU, enough RAM for your services and fast storage for your search index, depending on the model and expected traffic.
However, large training projects will need a lot of accelerators, fast communication between them and infrastructure to handle distributed computing.
This is where CoreWeave comes in. ProlimeHost offers dedicated GPU servers for customers who want their own hardware for their applications. This can be single or multiple GPU servers.
It depends on your compute needs. Even a small organization can have a large AI workload. A large enterprise may only need one accelerator for its internal service.
GPU Hosting Pricing – Server Costs
Not all providers will list their prices the same. Some will charge a monthly fee for a dedicated server with CPU, RAM, storage and bandwidth. Others will charge hourly for the GPU, with additional costs for storage, data or other services.
Below is a summary of the reference prices.
| Provider/Config | Price | Type |
|---|---|---|
| ProlimeHost RTX 5090 Dedicated | $519 | Monthly |
| ServerMania GPU Hosting | From $879 | Monthly |
| Lambda NVIDIA A10 24GB | $1.29 | Hourly |
| Lambda NVIDIA H100 PCIe 80GB | $3.29 | Hourly |
| DonWeb RTX 5090 | Quote Required | Monthly |
| OVHcloud Dedicated GPU | Varies | Monthly |
| CoreWeave GPU Cloud | Varies | Usage/Contract |
These are historical reference prices and have not been confirmed for this edition. They are not the same hardware.
I’d want to see the full quote. This would include the exact GPU, CPU, RAM, storage, bandwidth, setup costs and any ongoing costs.
Having a 10Gbps port with 50TB of included bandwidth isn’t the same as 10Gbps unlimited. This may not be a big concern for a small inference application, however it can be important for businesses with large data transfers.
3-Year GPU Hosting Costs
A $519 monthly server rental may not sound like a major infrastructure expense, particularly when compared with the price of high-end GPU hardware. Stretch that commitment across three years, though, and you’re looking at $18,684, assuming no extra services and the server remains $519 per month.
Hourly billing can be a bit more tricky. It depends on how long you have the instance running. Using the examples from Lambda, we can assume 730 hours per month. Over 3 years this will be 26,280 hours. This can be useful to show how much you would be paying if it ran constantly, although your application may not utilize the full GPU every hour.
| Option | Monthly Equivalent | Yearly | 3-Year |
|---|---|---|---|
| ProlimeHost RTX 5090 | $519.00 | $6,228.00 | $18,684.00 |
| Lambda A10 at $1.29/hour | $941.70 | $11,300.40 | $33,901.20 |
| Lambda H100 PCIe at $3.29/hour | $2,401.70 | $28,820.40 | $86,461.20 |
This shows how much you would be paying to rent the GPU resources. These are historical reference prices. The GPUs are not performance equivalent, and the figures do not include tax, discounts, optional services or other potential charges.
This shows how much you would be paying to rent the resources constantly.
However, it doesn’t show which GPU will complete your task the quickest, or which accelerator will be best for your model. This will depend on testing your workload.
However, it is still a big financial consideration. I’d want to know how much it would cost over 3 years.
We’ve discussed similar issues in our article Cloud Storage vs Dedicated Storage Servers.
Hardware Can Be A Make or Break
Having an expensive GPU won’t necessarily help. Sometimes your CPU may not be able to handle the data prep quickly enough. Or your RAM, disk or bandwidth may be the bottleneck.
If you have a busy inference server, your CPU may be working hard to handle the requests and prepare the inputs. If your CPU can’t keep up, your GPU may be sitting idle for some of the time.
If you don’t have enough RAM, your databases, application services and caching can take up much more than you expect. This can cause the OS to start swapping heavily and impact performance.
Storage can be an overlooked consideration. AI models, training datasets and saved checkpoints can take up several terabytes of storage. This can happen much quicker than you expect. Whilst NVMe storage can help with the speed, you also need enough storage. If your fast drive is almost full, it won’t help much.
Bandwidth can be a bit misleading. Just because you have a 10Gbps port, doesn’t mean you have unlimited bandwidth. ProlimeHost’s RTX 5090 has 50TB of monthly transfer. Whilst this is a lot for many applications, if you are transferring large data regularly, you need to know what happens once you hit this limit.
Sometimes the server specs will focus on the GPU, and the CPU, RAM and storage will be considered as an afterthought. This is a mistake. If your accelerator is fast, but the rest of the machine can’t keep it fed with data, it won’t help much. Sometimes adding more system RAM (another 64GB) or adding another NVMe drive can help your application more than upgrading your GPU.
Not all of the supporting services need to run on the GPU machine. You can host your databases, web applications and other processes separately, so your accelerator can do what it was bought for. ProlimeHost offers dedicated servers for this.
Choosing A GPU Hosting Provider – Don’t Overspend
It can get expensive when choosing a GPU. Let’s say you start with an RTX 5090. You see an accelerator with 80GB of memory and start to think maybe you should go for the bigger one. Maybe this will be the case. However, if the application requires less VRAM (e.g. 20GB), then this spare space might not ever be used.
Leaving some extra space for growth is fine. But if no one can tell you how much growth you’re expecting or when you’ll need it, then that’s where problems arise.
I like to start by looking at the application. If you already have your application running somewhere, you’ll have insight into how much memory, GPU usage, CPU usage and storage your application will require. If the model can fit nicely inside 32GB of VRAM, there is no reason it needs to be run on an 80GB accelerator. Trying to run a larger model on a smaller GPU just to save hosting costs can cause issues.
You should consider where you’ll be hosting. DonWeb hosts GPU infrastructure in Argentina, ProlimeHost offers a few options in the USA. OVHcloud has international infrastructure, while ServerMania offers dedicated and custom configurations. If you’d prefer to host your application on a cloud GPU provider, then Lambda and CoreWeave are both options.
How long do you plan to run your application? If you’re running a research project which will finish next month, then the financial considerations will be different. Whereas if you’re running something in production, it could potentially run for years.
Having some extra capacity is great, but paying monthly for hardware you may not even use can be difficult to justify.
Things To Consider Before Ordering
Firstly, your quote should include specifics:
GPU model, VRAM, CPU, memory, storage and network port (including monthly transfer).
Have a think about whether the server you’re looking at is actually available. Inventory can change and having specific hardware or lots of memory can take longer to build. If you have a definite launch date for your project, ask when you can expect to have your server ready. Even if you get a great price, it won’t matter if you don’t get it in time for your application to be launched.
Can you upgrade your memory/storage? Can you add another GPU? Will it require you to replace the server?
Will they support you with your upgrades? Are they responsible for replacing the hardware, managing the OS, GPU drivers and application?
Before you order, it’s good to know what you’ll be charged. If you’re ordering a $519 GPU server, will you be charged any extra for IPs, additional bandwidth or specific hardware upgrades? Is there a setup charge? Does the price you’re being quoted require a longer-term commitment? It’s much easier to sort these things out before you deploy your server.
FAQ
Is ProlimeHost cheaper than ServerMania?
Yes, based on the listed reference prices. ProlimeHost lists its RTX 5090 server as costing $519 per month, compared with ServerMania’s previously advertised GPU hosting starting at $879. This is $360 cheaper per month. However, these are not the same hardware configurations. ServerMania may have some configurations with features that could justify the higher price.
Does DonWeb offer RTX 5090 dedicated servers?
Yes. DonWeb has listed dedicated RTX 5090 and RTX 3090 GPU servers in Argentina. If you’re interested in either of these, ask for an updated quote to ensure they can provide the hardware.
Can you run AI inference on an RTX 5090?
Yes. The 32GB VRAM can support many inference workloads, depending on your model, precision, context length and concurrency requirements. If you’re using a larger model, you may require more memory or multiple GPUs.
When would you want to use hourly GPU hosting?
If you’re running a short training task or experimenting, then hourly billing can be a good option. However, the more hours you’re billed for, the less economical it may become. Especially if you’re running it 24/7.
Does ProlimeHost offer multi-GPU servers?
ProlimeHost has offered 4x RTX 3090 and 2x NVIDIA A100 80GB configurations. If you’re looking for specific hardware, you may be able to get it, depending on availability.
What is the biggest mistake people make when comparing GPU hosting providers?
Comparing prices without checking if the servers can do the same work. This can be affected by the GPU memory, CPU, storage and networking. Just because it’s the cheapest server listed does not mean it’ll be the cheapest to complete your workload.
Final Thoughts: Purchase the Right Amount Of Compute
I’ve never thought the most expensive GPU is automatically the best server. What matters is whether your hardware can complete the work without causing unnecessary costs. If you’re only doing a small inference application, there is no real reason to rent a large GPU cluster. Whereas if you’re training some large AI models, you shouldn’t expect a consumer GPU to do all the work.
The 6 providers listed have different approaches to their offerings. ProlimeHost, DonWeb, ServerMania and OVHcloud all offer dedicated GPU hosting. Whereas Lambda and CoreWeave offer GPU hosting in the cloud.
If you need your GPU up and running 24/7, then dedicated monthly hosting could be a good option for you. Whereas if you’re doing a short training task, then hourly billing could be a better option. Especially if the accelerator you need is expensive to rent.
The best thing to do is look at your application. How much GPU memory do you need? How intensive will it be? What do you need your server to support? Compare the real price of running the application. This will give you a much better idea of where to spend your infrastructure budget, rather than going for the flashiest GPU.
Talk To Us About Your GPU Hosting Needs
ProlimeHost offers dedicated GPU servers for AI inference, machine learning, rendering and other high-intensity applications. If you’re looking to deploy your application, talk to us about your GPU, CPU, RAM, storage and bandwidth requirements. We’ll look at what configurations we can offer, if we can provide bespoke hardware and give you our latest pricing.
View our GPU Dedicated Servers or contact us.
Disclaimer
Prices and configurations may change. Please check with the companies directly to see what hardware they can offer, their network terms and what they charge. The previous competitor prices are for demonstration purposes only. They do not reflect current quotes. The examples given compare different GPU models. They do not show that the GPUs will complete the same amount of work, just the cost of the bills. Trademarks and company names belong to their respective owners. ProlimeHost is not affiliated with any of the competitors mentioned.
About The Author
Steve Bloemer is the Director of Sales & Operations at ProlimeHost. He has been working there since 2015 and helps customers find the right dedicated server, GPU, storage and network configurations for their needs, whether they need standard servers or bespoke hardware.
