Which GPU Providers Can Quote Container, Bare-Metal, and Managed-Cluster Options for the Same Workload?

Start with a provider that sells all three deployment options on one platform, prices them in the same unit, and itemizes everything that sits outside the GPU rate. GMI Cloud's Cluster Engine is built that way. When hardware, billing rules, and account terms stay constant, the gap between quotes reflects the deployment option itself rather than two vendors' different conventions.

Container Service, Bare Metal GPU, and Managed GPU Cluster (currently in early access) are all part of our GPU infrastructure and run from the same GMI Cloud console. GPU compute is priced per GPU-hour, with the rate depending on GPU type and deployment model, and before you confirm an order the console shows the list price, discount, and estimated monthly total. Hold the hardware, workload, and term fixed, then compare what each quote includes and who operates each layer of the stack.

What each deployment option includes on GMI Cloud

The three options share GPU hardware and billing units, and all GPU resources on GMI Cloud are dedicated, single-tenant instances, so none of the options shares a GPU with other users. What differs is how much of the stack you control and how capacity is provisioned.

Container Service

Container Service provides GPU-optimized, Kubernetes-based environments with fast startup and elastic scaling. You launch a single container on demand from a template (vLLM, SGLang, JupyterLab, or your own custom image) and get root access inside the container through the web shell. It suits rapid prototyping, elastic inference, and internal AI services. Reconfiguring a container permanently removes the data stored inside it, so persistent storage is its own line item.

Bare Metal GPU

Bare Metal GPU gives you a whole physical server with full root and OS access, custom drivers, persistent local NVMe, and hardware-level control. You can stop, start, reboot, or power-cycle the machine yourself. Bare metal runs in an isolated VPC, and GMI's GPU infrastructure is RDMA-ready for high-throughput networking. It fits large-scale training, long-running high-utilization workloads, and performance-sensitive inference that needs predictable, isolated GPU performance.

Managed GPU Cluster (early access)

Managed GPU Cluster provides fully managed, Kubernetes-based multi-node GPU clusters that GMI provisions and operates, aimed at distributed training and large-scale inference. You choose the Kubernetes version, node specification, node count, and OS image. Managed clusters are built on H200 or B200 nodes. H200 is available now, while B200, like all NVIDIA Blackwell hardware on GMI Cloud, is pre-order and coming soon, so a quote you can act on today is an H200 quote. Clusters are provisioned on request after GMI reviews the configuration. Requests for additional nodes on an existing cluster are Node Requests, tracked alongside cluster requests in the console. Managed GPU Cluster supports managed clusters across both GMI Cloud and BYOS environments, and is built for organizations with existing GPU clusters.

Start every quote from one workload brief

Three quotes compare cleanly only when they answer the same question. Use one brief for all three:

• GPU model and count. Keep hardware fixed, for example eight H100 or H200 GPUs, so the deployment option is the only variable.
• Region or data center. GMI deploys across US, APAC, and EU, and pricing is region-aware, so region belongs in the brief rather than being left open.
• Runtime and utilization. Hours per month, steady or bursty, and how busy the GPUs actually are.
• Term and billing model. Pay as you go or prepaid, and for how long.
• Storage. Capacity and type: container volumes, local NVMe, or shared storage.
• Networking. Public IPs, and expected ingress and egress.
• Success metric. p95 latency or throughput for inference; tokens or steps per hour for training.

Line up the quotes on the same terms

Even inside one provider, the three options carry different billing mechanics. These fields most often change which option looks cheapest:

Live hourly rates by SKU and region appear on the console's Compute home page, and GPU pricing lists public starting rates. Record any line a quote leaves out as "not quoted," not zero.

Billing rules that belong to only one option

Check each option's billing rules before comparing totals. Minimum terms, storage, and resources left running after teardown can outweigh a difference in the GPU-hour rate.

Container Service: the plan is the commitment

Containers run either pay as you go or on a prepaid plan. A pay-as-you-go container has no expiration date and can be terminated at any time. A prepaid container cannot be terminated before its rental period ends, and renewal periods range from one day to three years. The plan a container quote assumes therefore decides how much of the bill is fixed, so put the assumed plan and period next to the total rather than comparing a prepaid container rate against a pay-as-you-go cluster rate.

Storage needs the same care. Reconfiguring a container permanently deletes the data stored inside it, and GMI's service level agreement excludes downtime events that stem from relying on ephemeral or local disk. If the workload requires persistent data, include the volume and its cost in every quote.

Elastic IPs: a line that outlives the workload

Elastic IPs are billed by the minute on pay as you go. They keep billing after they are disassociated, stop billing only when released, and are not released automatically when a container is terminated. For container work that starts and stops often, put a release step in the teardown plan so idle addresses do not keep billing, and ask each quote to list IPs as their own line.

Managed GPU Cluster: an estimate before a request

Managed clusters can be billed pay as you go, charged by the minute with no upfront cost, or prepaid. The request form's summary panel shows the list price, discount, and estimated total for the configuration you enter, which gives you a figure for the managed option built on the same node type and count as the other two quotes. Submitting the form does not start the cluster: the request is not processed automatically, and you contact support to activate it. Scaling later goes through Node Requests, so a quote for a cluster that grows should say how added nodes are priced.

Bare Metal GPU: get the terms in writing

Bare metal is priced per GPU-hour like the other options, but ask for the billing model, minimum term, and metering granularity to be written into the quote so it can sit on the same row as the container and cluster figures. Because bare metal includes persistent local NVMe, check whether the quote already covers the storage the brief needs or adds shared storage on top.

Map who operates what

Include the engineering work each option leaves with your team in the cost comparison.

• Container Service: GMI runs the Kubernetes-based GPU platform. You own the container image, the application, and your data, including where persistent data lives, since container-local data does not survive reconfiguration.
• Bare Metal GPU: GMI provides the physical server and isolated networking. You own the operating system, drivers, CUDA and framework versions, scheduling, and scaling.
• Managed GPU Cluster: GMI provisions and operates the Kubernetes cluster to your chosen version and node configuration and provides centralized cluster lifecycle management. You run the workloads and data on it. The order specifies how control-plane upgrades, node replacement, driver updates, and incident response are handled.

Uptime commitments follow the same split. For container instances, GMI's service level agreement sets monthly availability at no less than 99% for instances in a single region and 99.9% for instances in multiple regions. Service credits equal the number of affected GPUs multiplied by the downtime hours, capped at that month's fees for the service, and the SLA is incorporated into each order. Bare Metal GPU comes with SLA-backed delivery, with its availability commitment and service credits set in your order. For Managed GPU Cluster, early-access terms, including service levels, are set in the order.

Convert each quote into cost per unit of work

Divide each option's full total (GPU charges, storage, networking and IPs, and any support or setup fees) by the successful units of work the brief's success metric counts. On a prepaid term, idle hours stay in that total, and engineering time an option demands, such as OS patching on bare metal, is a real cost even though it never shows up on an invoice.

Test the cheapest-looking option before committing

A short pay-as-you-go run shows whether a quote's assumptions hold. Because a pay-as-you-go container can be stopped whenever the test is done, it measures the workload before any prepaid term locks in. Container monitoring shows GPU usage and GPU memory usage over windows from 15 minutes to 30 days, so the brief's utilization estimate becomes a measured number. For managed clusters, a node request on an existing cluster shows how scaling works in practice. When a test needs formal terms, GMI offers a paid proof of concept under a defined agreement.

Other providers that sell more than one deployment option

CoreWeave offers managed Kubernetes and SUNK, its Slurm-on-Kubernetes service, and lists GPU pricing per instance-hour, such as an 8-GPU HGX node. Lambda sells self-serve instances priced per GPU-hour alongside 1-Click Clusters on commitments of two weeks or longer. Nebius offers GPU instances, Managed Kubernetes, and Managed Soperator for Slurm. Their pricing units and treatment of storage, egress, and IPs differ, so the alignment table above applies to every provider on a shortlist.

FAQ

Can one provider cover container experiments and bare-metal training? Yes. On GMI Cloud, Container Service and Bare Metal GPU run on the same platform and console, so once both are enabled for your organization, a team can prototype in containers and move training to bare metal without a second vendor account or billing relationship.

Can a managed cluster cover our own GPUs as well as cloud capacity? Yes. Managed GPU Cluster is built for organizations with existing GPU clusters and provides a unified management experience across GMI Cloud and BYOS environments. Scope and terms for a combined setup are part of the early-access conversation with our team.

Should we run bare metal ourselves or use managed inference? Bare metal fits teams that want to own the OS, drivers, and serving stack. For production inference without operating the runtime, Prime Inference provides single-tenant endpoints on reserved NVIDIA GPUs with runtimes tuned per model, billed per GPU-hour.

Get a quote for your workload

To price committed, volume, or custom capacity against your workload brief, talk to our team. To see live hourly rates by GPU and region first, sign in to the GMI Cloud console and open Compute, or read the GPU Compute documentation for setup details on each option.