Why 81% of Enterprise AI Initiatives Run Into the Same Wall
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Introduction
This is a practical guide to match GPU infrastructure against the AI workload you’re dealing with. On paper, these four look almost interchangeable. The same class of chip sits underneath, the hourly numbers land in the same range. So teams choose by habit, or by whatever the last project used, and move on.
The effect of choosing wrong only shows up months later. As capacity gets billed far more than it gets used, or a training run slower than the hardware should allow.
What settles the choice is the workload, in two questions:
- Are you training or serving?
- How busy is the GPU through the day?
One wrinkle the title hides: three of the four, VMs, bare metal, and BMaaS, are forms of the machine. Kubernetes is a layer that runs on top of any of them. So it is two decisions, not one, and we take them in order, the machine first.
The machine comes first, and the workload picks it
The first question is whether you are training or serving. Virtualization, the thin layer that lets a VM share one physical server safely, costs each workload differently. For inference it is close to free: virtualized GPUs came within 95 to 104% of bare metal in MLPerf tests. For large distributed training it can cost as much as 30%, because training leans on every microsecond of communication between the cards. Over weeks, that gap is real money.
The second question is how busy is the GPU. A GPU has two costs, what you pay while it works and what you pay while it sits idle, and the machine decides how much of the idle time lands on your bill. A virtual machine scales down when the work stops, so you pay for load. A dedicated machine is yours around the clock, a bargain when busy and waste when not.
Put the two together and a rule falls out. Default to a virtual machine. Move to dedicated bare metal when you train at scale, when the GPU stays busy enough that a commitment beats hourly, or when isolation is mandatory.
Everything else, development, fine-tuning, and most inference, runs well on a VM at near bare-metal speed for less.
The three shapes, and when each earns its place
- GPU VMs are the elastic option. They provision in minutes, run on a shared host with network isolation. The default for serving, fine-tuning, and anything with variable load.
- Dedicated bare metal is a whole server, or a cluster, with no virtualization and no other tenant. Full throughput and predictable performance for large training, hard-latency inference, and single-tenant regulated work. The trade is commitment.
- BMaaS is that dedicated metal without the wait: reserve and start a cluster from a console. Modern self-serve bare metal comes up in minutes rather than weeks.
Unsure? Run the workload down these axes. Mostly left, start on a VM. Mostly right, you want dedicated metal.
| Decision axis | Lean GPU VM when… | Lean bare metal / BMaaS when… |
| Workload | Dev, fine-tuning, inference, variable demand | Large or distributed training, latency-critical serving |
| Utilization | Bursty, on and off through the day | Busy most of the time |
| Duration | Short or unpredictable | Long-running and sustained |
| Isolation | A VM plus network isolation is enough | Single-tenant hardware is required |
| Cost model | Pay by the minute for load | Commit for a predictable rate |
The second decision: do you need Kubernetes?
Once the machine is set, a separate question: how do you run and scale work on it. One job or one notebook needs nothing on top. A fleet of services that has to scale, recover from failures, and update without downtime is what Kubernetes is for.
Kubernetes is orchestration, not a machine. It schedules your containers onto the GPUs you have, restarts the ones that fail, and scales with load. It runs on VMs or on bare metal, which is why it is a second decision and not a fourth option. On Velocis the service is VKE, managed Kubernetes that runs your containers across your GPUs.
What this looks like across real workloads
This matters more each quarter because inference, not training, is now about two-thirds of AI compute, up from a third in 2023. For most teams the elastic option should be the default, with dedicated hardware kept for the training and hard-latency serving that need it. How it lands across industries:
| Industry | Example workload | Best-fit machine | Why |
| Financial services | Real-time fraud scoring; regulated model training | Dedicated bare metal | Latency-critical serving with no hypervisor overhead, plus single-tenant isolation for regulated data |
| Healthcare & life sciences | Genomics and imaging training on sensitive data | Bare metal / BMaaS | Distributed training that also needs dedicated-hardware isolation for sensitive data |
| Retail & e-commerce | Recommendation and search inference, spiky at sales | GPU VMs | Bursty demand, so you pay for load and scale down |
| Media & entertainment | Generative image and video; batch rendering | VMs for burst loads, Bare metal to train | Elastic for spikes, dedicated metal for heavy training |
| AI-native startups | Experimentation, fine-tuning, first inference | GPU VMs | Fastest start, lowest cost, no commitment early on |
| Manufacturing | Computer-vision defect detection across lines | GPU VMs / Kubernetes | Many small, independent inference jobs that pack and scale per line |
All of it, on Neysa Velocis
A provider that sells one shape traps you: VM-only makes a training run pay the tax forever, metal-only puts a cluster-sized commitment on every experiment. Velocis offers every shape on one platform, over the same GPUs, storage, and network, so moving between them is a setting, not a migration.
- Every form factor, plus the platform around it. GPU VMs and containers, self-serve BMaaS, dedicated bare metal, and VKE for Kubernetes, with managed notebooks, ready-to-run inference endpoints, and model libraries on top. Prototype in a notebook, serve from an endpoint, scale to a cluster, without leaving the platform.
- Variety of GPUs available across all types. NVIDIA L4, L40S, H100, H200 and B300, with the AMD MI350X as one of the GPUs on Velocis, so the tenancy decision never costs you the chip you want.
- Infrastructure built for AI, and proven. WEKA storage and a 3,200 Gbps Arista network with RoCE v2 and GPU Direct keep the GPUs fed at full speed, behind a defined uptime SLA, SOC 2, ISO 27001, 27017 and 27018, CSA STAR, and India data residency. Velocis is the only Indian cloud in SemiAnalysis’s independent ClusterMAX rating.
- Engineers who run AI in production. MLOps and infrastructure specialists who size clusters and tune the stack, 24×7, with direct-to-engineer escalation instead of a ticket queue. If a workload needs help moving or scaling, you have people beside you.
- Spend you can see. Autoscaling with an idle-killer that stops billing the moment the work stops, plus minute-level metering and invoices you can split by project, so every shape shows up as a traceable line rather than one opaque bill.
Where this leaves you
There is no single best setup, only the fit for the workload in front of you. Serve at variable load on a VM and keep nearly all the performance for a fraction of the idle cost. Train at scale, serve under hard latency, or carry regulated data, and move to dedicated metal, with BMaaS to make it quick. Run more than a couple of services, and put Kubernetes on top. Match the machine to the job, and keep the freedom to change as the job does.
Not sure which shapes your workloads want? Our engineers will size it with you. Talk to a Neysa engineer ->
