The race on AI is heating up; the next generation of vibe coders and prompt engineers are entering the job market as we speak, and AI is the hottest line item on most IT budgets this year. Building the next great home automation software or adaptive learning platform with cutting-edge machine learning is great and all, but like all great software, it needs to start with the plumbing.

What's the plumbing, you may ask? It's the infrastructure, networking, and low-level decisions that make your product work. AI/ML organizations need infrastructure that balances power, scalability, cost, and control. The ability to get the most out of your compute, move it when you need to, and scale up or down on command is crucial, but the question is how do you do this all without breaking the bank?

Most will flock to the hyperscalers, AWS, GCP, etc, and pay the exorbitant VMware bill (don't forget the Broadcom tax ) and end up breaking the bank before they can scale. Those who don't go this route will find a great balance with Cycle. We're helping new and old organizations break into or optimize AI products and do it with whatever cloud or bare metal GPUs they want to use, all while cutting down on overall spend.

"In 2025, paying a premium for hypervisors just to run GPUs feels outdated. Enterprises need full control, strong security, and no overhead. That's where Cycle comes in — a modern approach that strips away the tax and gives you direct, efficient control over your infrastructure." 

Mark Panthofer, Vice President of DevOps @ Nvisia

Typically, you will trade convenience and control for cost when you turn to hyperscalers and VMware. Cycle aims to meet you in the middle at a more digestible price point without losing the efficiency that's so crucial with AI applications.

Managing and Provisioning GPUs

If you are building AI, you're most likely working with GPUs, and if not, you're using high-powered compute, likely bare metal. Whatever situation you're in, you know GPU and high compute resources are a valuable commodity and sometimes hard to access. Having many options at your disposal for the type and location of your compute is crucial for building powerful AI.

Cycle supports the latest NVIDIA data center-class GPUs . Our model of BYOC (bring your own compute) operates great in the AI space. Being able to easily provision and manage GPU compute from multiple sources gives users the flexibility and confidence to take any project to scale. You can take advantage of Bare metal GPUs for a more digestible price point from our partner Vultr or any other bare metal provider you are comfortable with. Cost goes down as you move out of the cloud, and power sky rockets!

"Bare metal GPUs promise incredible value for AI teams. The performance-to-cost ratio is unbeatable. But there's a catch: managing those machines isn't easy, and the VMware tax adds insult to injury." 

Once your infrastructure is secure and managed by Cycle, it can get out of the way so that you can focus on driving business value and taking your AI to the next level.

Bare Metal Efficiency & Cost Optimization

We'd be doing a disservice if we didn't dive into the true value that bare metal is offering versus the cloud. What you are giving up in ease of use, you are making up tenfold in efficiency and cost. With direct access to hardware, you avoid the typical cloud GPU markups and the overhead that comes with virtualization.

On Cycle.io, you can strategically allocate workloads, heavy training jobs run on high-performance bare metal nodes, while lighter inference or testing workloads can leverage cloud resources. The platform also automatically scales down idle inference services, so you're never paying for compute you're not using. The result is tangible: AI companies running on Cycle.io report significant infrastructure savings without sacrificing performance, freeing up capital to invest back into innovation.

Scalability & Observability

AI workloads aren't static. They swing from small-scale experiments to massive production systems with thousands of users or models in parallel. Having the capability to observe, right-size, and scale infra isn't just a commodity; it's crucial for all meaningful AI/ML setups. Whether it's bare metal GPU or high-performance cloud CPUs or both, Cycle is the single pane of glass you need to manage and ensure the reliability of your applications.

The platform automatically manages the provisioning and orchestration of infrastructure across bare metal and cloud providers, so workloads scale up seamlessly when demand spikes and scale down when idle. Whether it's GPU-heavy AI inference or lightweight microservices, Cycle ensures resources are allocated efficiently without requiring teams to babysit clusters, tweak Kubernetes manifests, or overprovision hardware, which can lead to thousands of dollars down the drain.

From GPU usage and workload performance to logs, metrics, and distributed traces, Cycle makes it easy to see exactly what's happening in real time. This visibility helps teams detect bottlenecks, debug issues faster, and ensure models and applications are running reliably — all without juggling several monitoring tools or dashboards.

Cloud and On-Prem Portability

A huge challenge and fear for a lot of AI teams is lock-in. With GPU prices as volatile as they are and previously reliable tools like VMware 100xing prices overnight, teams need to stay agile. They need the ability to move workloads wherever they need, whenever they need. In Cycle, you can deploy workloads on bare metal, private data centers, or multiple cloud providers without rewriting configuration or worrying about provider-specific services. This is great for teams looking to optimize for cost, or with the introduction of the new EU data act , for compliance. When a lot of these variables often vary depending on the project, Cycle's portability ensures teams can focus on building and scaling models - not wrestling with infrastructure.

Conclusion: AI Without the Infrastructure Headaches

AI comes with plenty of extra variables, variables that can cause chaos if not contained. Cycle gives an easier off-ramp for those building or looking to change the way they build their AI projects. At the end of the day, building world-class AI shouldn't mean getting bogged down by infrastructure complexity and chaos. We work with those who are serious about controlling their infrastructure in the way they need, but without it getting in the way of their goals, which is building better products. Delivering out-of-the-box scalability, observability, and portability of your infrastructure is how Cycle is taking many AI teams to the next level.