Your AI. Your boundaries. Operated by Rackspace.

Available today: dedicated GPUs

Your models run on GPUs dedicated to your organization, in Rackspace-owned infrastructure operated by Rackspace. You keep control of access, models and policies through your Organization and its Tenants. You can run many models in your own environment.

Dedicated GPUs remove direct GPU sharing with other customers. They are not the same as a dedicated server: the host and the provider's control plane can still be shared. We say this plainly because your risk assessment needs it.

Built to lead, and to prove it

• First on price and speed for every model we serve. Each model is tuned and quantised for the hardware it runs on, held to a quality floor, and proved in public, with the method published.
• 99.9% availability per deployment, with service credits when we miss, restore within an hour, and a named owner for every incident.
• Every model qualified, not just listed. The models your use cases need, each tuned and approved for the level your data requires.
• Evidence every month, not once a year. What ran, where, which controls held, and what it cost.
• Runs where your data needs it. Dedicated GPUs today; in your jurisdiction or on your own hardware next, on one operating model.

Outcomes you can expect

• Lower cost. About a third fewer GPUs for a multi-model estate: small models share GPUs, fine-tunes share a base model, and idle models scale to zero. Cost is reported per unit of work, not per GPU.
• Better answers. Grounded in your documents with citations, fine-tuned on your data, and held to an evaluated quality floor. A cheaper model that answers worse never counts as a saving.
• Faster to production. A measured result about eight weeks after the first conversation, and each new workload in weeks, not quarters.
• Audits answered from evidence. Every agent action is attributable to a rule and an approver, and every monthly report shows what ran, where, and what it cost.

What it looks like (illustrative). A regional bank's 1,200 loan officers stop re-keying forty-page credit files and review a draft instead. Examiners see where every file was processed. Cost per thousand files sits on one line of the bill. Fraud triage and a customer-service assistant follow as new declarations, not new projects.

What we commit to

< 10 min

from a provisioned environment to your first answer

99.9%

availability per deployment, with service credits when we miss

< 1 hour

to restore service after a serving failure

Monthly

evidence of what ran, where, under which controls, and at what cost

Behind every number: one named owner for the whole workload, and no requirement quietly relaxed. Your data is not used to train anyone else's model, and you can leave with your data, context and models.

Questions to bring to us

Sovereignty is more than location. Before choosing a deployment, consider:

1. What data will the workload touch?
2. What may the model do: answer only, read your systems, or act on them?
3. Does it need to run in an acceptable region, or under a contractual jurisdiction commitment?
4. Must it stay inside your own perimeter?
5. Who may administer or support it, and from where?