Have you thought about whether you use AI APIs from external services such as ChatGPT or run your AI models directly on cloud infrastructure? Either way, there are important risks to consider. Partial data—including metadata and more—can potentially be used by cloud providers. On top of that, high costs per API token or for hardware and bandwidth of your cloud servers can quickly add up. Be especially cautious about default sharing settings and third-party tool integrations, as their data policies might expose your sensitive information.
It’s not just about privacy. The cost of AI compute in the cloud is skyrocketing—especially when you scale. While hyperscaler platforms make it easy to get started, they also come with high markups for compute, storage, and especially bandwidth. If your workload includes large models or constant training, those invoices can quickly spiral out of control.
Use APIs or Run Your Own Models?
One of the first questions AI teams face is whether to build using APIs from providers like OpenAI, Anthropic, or Cohere—or to run their own infrastructure with open-source models such as LLaMA, Falcon, or Mixtral.
AI APIs offer speed and simplicity. But the costs are significant. As an example, if you use GPT-4 Turbo for production inference, the token costs can add up fast. A single month of frequent usage across multiple endpoints may cost thousands of dollars, especially when factoring in token length, system prompts, and retry mechanisms. If your use case involves fine-tuned models or large-scale LLM applications, you’re likely paying premium rates to rent intelligence that you don’t own.
Running your own models on cloud GPUs sounds like a better deal—until you discover the fine print. You're billed by the hour for compute. You're charged for every gigabyte of storage. And network egress (getting your data out of the cloud) is often the silent budget killer. Even mid-sized projects can result in cloud bills that exceed $10,000 per month once usage ramps up.
The High Cost of Using AI APIs or Cloud AI Platforms
To give you an impression of the price difference, let’s look at a recent comparison we made on a standard NovoServe config. We benchmarked a physical server from our fleet—a HPE DL360 G10 with dual Intel Xeon 6252 CPUs (48 cores), 384 GB RAM, and 4x 960 GB SSDs—against a similar virtual instance from one of the major hyperscalers.
At NovoServe, this configuration costs €379 per month. On the hyperscaler, the equivalent virtual machine is priced at over €3,600 per month—nearly 10 times more. That’s before bandwidth costs. For bandwidth, the gap is even wider.
This isn’t an isolated case. It's a structural issue: when you rent infrastructure optimized for sharing, you pay not just for performance—but for the virtualization layers, orchestration overhead, and brand markup. In contrast, a dedicated AI server gives you raw compute power with no middlemen.
Cloud Wasn't Built for AI
The original purpose of cloud computing was to enable flexibility and resource efficiency by letting multiple users share infrastructure through virtualization. For many workloads, that's fine. But AI isn’t one of them.
AI workloads are resource-hungry by nature. Whether you’re training foundation models or deploying real-time inference, your processes demand full GPU utilization, high memory bandwidth, fast I/O, and dedicated resources. Sharing simply doesn’t make sense. In fact, virtualization often adds bottlenecks that reduce training speed and inference throughput.
With bare metal AI servers, you avoid these limitations. Your team gets complete control over every core, every gigabyte of RAM, and every GPU resource. There's no hypervisor eating performance. No noisy neighbors affecting your latency. Just raw, dedicated infrastructure built for AI.
The Data Privacy Risk of Cloud AI
If you're developing AI tools, you're likely working with proprietary datasets, fine-tuned models, and private algorithms. These assets are intellectual property. Losing control over them—even just through metadata leakage—could mean giving competitors a strategic edge.
Public clouds are filled with complex sharing policies, multi-tenant architectures, and data pipelines connected to other services. It's easy to lose track of where your data goes—and who might be analyzing it. Even if the cloud provider is compliant, third-party tools integrated into your stack might not be.
Bare metal servers solve this by giving you true data sovereignty. Your workloads run in isolated environments. Your logs stay private. Your data never leaves the server unless you explicitly allow it. For AI teams handling sensitive or regulated data, that control is not just a nice-to-have—it’s a necessity.
Dedicated AI Servers from NovoServe
At NovoServe, we offer dedicated servers designed for AI workloads. Whether you're experimenting with small-scale fine-tuning or running full-scale transformer training, we have hardware and network capacity tailored to your needs.
Our dedicated AI servers come equipped with:
- Premium Nvidia / AMD / Gigabyte GPUs
- Supermicro chassis optimized for AI acceleration
- Up to 2 TB of RAM, fast NVMe storage, and customizable RAID setups
- Up to 50 Gbps unmetered bandwidth for massive data throughput
And best of all, your infrastructure runs on bare metal, ensuring optimal performance, cost transparency, and complete control.
Special Promo on GPU Servers
This summer, we're offering a limited-time discount on our AI-ready GPU servers. If you've been waiting to upgrade your infrastructure or bring your AI workloads in-house, this is your chance to do it affordably.
👉 Explore our Summer GPU Promo here
Need help deciding on the right setup? Whether you’re training, fine-tuning, or running production inference—we’ll help you find the right configuration. Just reach out to our team and we’ll guide you through GPU choices, storage needs, and network options.
Run AI on Your Terms
AI workloads are different than the rest. It’s the future of your business. And the infrastructure you choose today will shape the performance, cost, and security of everything you build going forward.
Running your AI on cloud infrastructure might seem convenient, but brings upon data security risks and high costs. You sacrifice control, data privacy, and long-term efficiency. With a dedicated AI server, especially one optimized for GPU compute, you reclaim ownership of your stack and protect the integrity of your data.
Choose bare metal. Run AI on your terms—with NovoServe.
