AI IaaS vs AI PaaS for GPU Workloads

NoraLin 31 2026-09-16 05:27:36 Edit

Quick Verdict: Buy AI IaaS when you need exclusive GPUs and will staff the runtime. Buy AI PaaS when you want jobs and endpoints without owning Kubernetes, queues, and image pipelines. The fork is who operates the stack after the invoice, not whether the vendor says “platform.”

AI IaaS is a rented GPU, network, and storage plant that you (or a managed operator) still have to run, while AI PaaS is a rented control plane that accepts jobs and model endpoints and hides most of that plant. Both can sit in a U.S. facility. They are not the same purchase.

This page is the buying fork. It is not a glossary of what private AI IaaS includes, and it is not a product tour. If you already know you only want an API, you are past this comparison.

What do you actually receive on the invoice?

Dimension AI IaaS AI PaaS
Primary object Nodes, fabric, volumes Queues, workspaces, endpoints
Who patches the runtime You or a managed ops contract The platform vendor
Customization Drivers, schedulers, and images you choose Whatever the platform exposes
Failure you feel first A node, NIC, or filesystem A job API, quota, or region outage
Best fit Dedicated GPUs and custom stacks Standard training and serving paths

A GPU virtual machine on a hyperscaler is still IaaS if you install the scheduler. A “GPU cloud” that only gives you a fine-tune job object is PaaS even if the landing page shows an H100 photo. Name the object you can SSH to. If you cannot name it, you bought a platform.

When does AI IaaS win for GPU workloads?

IaaS wins when the model, data path, or compliance review needs a stack the catalog does not offer. Teams that pin driver versions, attach a parallel filesystem, or keep weights off a multi-tenant control plane stay here. You accept tickets for firmware and capacity because you refused a smaller menu.

Serving Decision Matrix: Enterprise LLM Inference Infrastructure

Serving Infrastructure Model Compute & Memory Contention P99 Tail Latency Predictability Multi-GPU Tensor Parallelism Support Optimal Enterprise Workload Fit
Shared Multi-Tenant Model APIs Multi-tenant shared workers; opaque resource pooling Severe tail latency jitter during peak concurrency spikes Black-box; no control over model parallelism or KV cache sizing Low-volume prototyping or asynchronous background tasks
Virtualized Cloud GPU Instances Hypervisor vGPU slices subject to CPU/PCIe interrupts Moderate jitter caused by neighboring tenant network bursts High inter-node latency limits multi-GPU tensor scaling (TP=4/TP=8) General internal apps with modest throughput requirements
OneSource Dedicated Private GPUs Dedicated bare-metal hardware with 100% VRAM & compute reservation Deterministic microsecond P99 response times under peak load Dedicated RoCE v2 RDMA fabric enables low-latency TP=4/TP=8 scaling Mission-critical, low-latency, regulated enterprise production serving

Private AI infrastructure is usually sold as exclusive IaaS: the GPUs are not a shared slice, and you decide what runs on them. Managed AI infrastructure does not turn that plant into PaaS. It means someone else runs the plant while you still own the runtime contract.

When does AI PaaS win instead?

PaaS wins when the scarce skill is not cluster operations but shipping evals and endpoints. Research groups and product squads get a workspace, a quota, and a deploy button. They should not learn RoCE to publish a classifier.

The cost of that speed is a smaller surface. If the platform lacks your orchestrator, your identity model, or your data plane, you will rebuild IaaS beside it. OnePlus Platform, OneSource Cloud’s AI orchestration platform, is the PaaS-like layer on exclusive capacity: quotas, workspaces, and job routing without turning the hardware into a public multi-tenant API.

Which shared-responsibility traps show up in reviews?

IaaS buyers under-count people. Someone must own images, idle GPUs, and the weekend page. PaaS buyers under-count exits. Exporting checkpoints is easy. Exporting the scheduler rules, feature flags, and identity mappings is not. Write both lists before legal redlines the MSA.

Also write what “included operations” means. Monitoring a node is not the same as monitoring a model SLO. AI orchestration and AI storage architecture sit on different sides of that line. If the vendor cannot say which side they staff at 02:00, you do not have a platform. You have a landing page.

FAQ

Is Kubernetes on rented GPUs still IaaS?

Yes, if you install and upgrade the cluster. The rental is capacity. The platform is the control plane you did not build. Managed Kubernetes from a cloud can blur the line; ask who owns the GPU device plugin and the drain procedure.

Does AI PaaS cost less than AI IaaS?

Not as a rule. PaaS often looks cheaper until idle endpoints, egress, and premium SKUs appear. IaaS looks expensive until you add the people who keep utilization honest. Compare a quarter of real jobs, not list rates.

Can one vendor sell both?

Yes. Many do. Your contract still needs two scopes: exclusive nodes you can treat as IaaS, and a job or endpoint plane you can treat as PaaS. Mixing them in one SKU is how teams discover they cannot leave.

Where does OneSource Cloud sit on this fork?

OneSource Cloud’s U.S. plants, including Texas / Richardson, are exclusive capacity first. OnePlus adds orchestration when several teams must share that capacity without a public PaaS tenancy model. That is IaaS plus an optional control plane, not a forced platform.

Summary

AI IaaS is the plant. AI PaaS is the window into a plant you do not run. Choose the window only when the menu covers the workload. Choose the plant when the menu is the risk.

If exclusive U.S. GPUs matter more than a catalog of job types, start from OneSource Cloud private AI infrastructure and add orchestration only when more than one team must reserve the same nodes.

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