White-label GPU cloud: why service providers need their own brand, not a hyperscaler's
What software do companies use to build a white-label GPU cloud?
Service providers building a white-label GPU cloud typically deploy a neocloud platform that handles multi-tenant GPU provisioning, billing, and customer management, then configure it with their own brand, domain, and pricing. hosted·ai is a GPUaaS software platform purpose-built for this use case. It includes a white-label customer portal with configurable branding, RBAC for multi-tenant access control, a metering and billing engine, and an admin panel for infrastructure management. The operator deploys the platform on their own hardware or sources capacity through GPU Mesh, the wholesale capacity alliance. From the customer's perspective, they are buying GPU cloud from the service provider's brand. hosted·ai is not visible to end customers unless the operator chooses to reference it.
Why reselling hyperscaler GPU is the wrong model
The short answer: you are not building a business. You are building a dependency.
When you resell AWS or Azure GPU instances, your margin is whatever the hyperscaler leaves you after their pricing. That margin is thin and getting thinner as hyperscalers compete more aggressively in the GPU market. H100 on AWS runs at $6.88/hr on-demand. Azure is $12.29/hr. Wholesale GPU through networks like GPU Mesh is available at rates significantly below those figures. The reseller margin on hyperscaler GPU is typically 10% to 20% before your own operational costs.
More importantly, customers see AWS or Azure in the transaction, not you. Your brand is invisible. When the customer grows and decides to buy direct from the hyperscaler, the switching cost for them is low and the relationship you built goes with them.
The operators building durable GPU cloud businesses are doing it the other way: using neocloud software to run their own product, under their own brand, with their own pricing model and their own customer relationships.
What white-label GPU cloud actually requires
Building a branded GPU cloud is not the same as deploying a virtual machine platform and calling it GPU. There are several things that are specific to GPU cloud that a generic infrastructure platform does not handle well.
Multi-tenant GPU isolation. GPU workloads share physical hardware. Tenants need to be isolated from each other at the VRAM level, not just at the network level. This requires a GPU scheduling layer that handles context-switching between tenant workloads and enforces per-tenant resource limits.
Overcommit and utilisation management. Running a single tenant per physical GPU is uneconomic. The platform needs to manage overcommit ratios, which controls how many virtual GPU slots are sold per physical GPU. Get this wrong and either your margins are bad (too little overcommit) or your customers complain about performance (too much).
Consumption billing. GPU customers expect to be billed on actual resource consumption, specifically GPU time, VRAM allocation, and sometimes TFLOPs. Standard VM billing by the hour does not map cleanly to GPU workload patterns. The billing engine needs to be GPU-aware. Also, you need to have a full control over the billing and should be able to change it as per the market and industry trend.
Customer self-service. AI developers expect to provision a GPU instance in minutes through a portal, not submit a ticket and wait. The customer-facing product needs to match that expectation.
hosted·ai handles all four. The platform includes temporal and spatial GPU scheduling, configurable overcommit from 2x to 10x per pool, consumption-based metering, and a self-service customer portal. The admin panel lets the service provider configure products, set pricing, and manage customers without writing code.
What the portal looks like to customers
From the customer side, the experience is a standard cloud portal: sign up, choose a GPU product (GPU type, region), provision an instance, get credentials, start running workloads. The portal carries the service provider's logo, domain, and visual identity. Pricing is set by the service provider. Support contacts are the service provider's team.
Customers can manage their own teams through RBAC, set resource quotas per user, view billing history, and access usage dashboards. All of that is included in the hosted·ai platform without custom development.
Custom domains are supported, so the portal runs on the service provider's own domain rather than a hosted·ai subdomain. That matters for brand consistency and for customers who are security-conscious about third-party URLs in their provisioning workflow.
The margin difference
The economic argument for white-label over reselling comes down to where the margin sits.
Reseller model: buy at $6.88/hr, sell at $7.50/hr. Margin: $0.62/hr per GPU, before your support costs and margin erosion from hyperscaler pricing changes.
Own-brand model with overcommit: buy or own hardware at an effective cost of $1.50/hr per physical GPU slot, sell effective capacity at $2.50/hr with 5x overcommit. Gross margin per physical GPU-hour: $12.50 revenue against $1.50 cost. The numbers are illustrative but the structure is real. Overcommit changes the denominator of the unit economics calculation in a way that reselling cannot.
Service providers running their own GPU cloud product with efficient overcommit consistently outperform resellers on gross margin. The tradeoff is that you own the customer relationship and the operational complexity. That is a tradeoff worth making if you are building a business, not a pass-through.
Getting the first product live
The practical starting point is a single GPU pool on a defined hardware base, with one pricing model and a limited initial customer set. That is enough to validate the commercial model, test the billing accuracy, and learn what customers actually need before expanding the product catalogue.
A white-label GPU cloud launch on hosted·ai typically takes two to four weeks from platform deployment to first paying customer. The neoclouds page covers the full platform capability, and the team can walk through your specific infrastructure and commercial situation in a 30-minute call.