What is private AI infrastructure?

Private AI infrastructure is compute, storage, networking, and AI serving capability deployed within a controlled environment — on-premises, in a private cloud, or in a sovereign region — without reliance on hyperscaler platforms such as AWS, Azure, or GCP. It gives organizations full control over where their data lives, who can access it, and what it costs to operate AI workloads against it.

What is the difference between private AI infrastructure and colocation or managed cloud?

Private AI infrastructure is not colocation, a managed cloud subscription, or a software layer running on a hyperscaler. It is a dedicated, in-region stack of compute, storage, and AI services — operated under the control of the deploying organization, not shared across public cloud tenants, and not routed through centralized hyperscaler architecture. Aurora builds, deploys, and operates the full stack, while the customer or partner controls the brand, the data, and the commercial relationships.

Who is Aurora built for?

Aurora builds for three types of organizations. Enterprises and public sector organizations — including regulated industries and government agencies — that need private AI infrastructure for their own operations, in-region and under their control. Telcos, MSPs, and regional operators who white-label and resell private AI services under their own brand. Channel and GTM partners such as system integrators and distributors who introduce Aurora into enterprise accounts and own the customer relationship.

How does Aurora compare to hyperscalers on cost?

Aurora delivers private AI infrastructure at 50 to 80 percent lower total cost than hyperscalers. GPU pricing runs 2 to 7 times below AWS, GCP, and Azure rates. Aurora Storage is priced at $5.99 per TiB per month with no egress fees — hyperscaler object storage generates additional costs for every data movement to compute. At production AI scale, the economics of private infrastructure are not marginal. They are decisive.

Can Aurora's platform be white-labeled and resold?

Yes. Aurora's full platform is available as a white-labeled service for telcos, MSPs, and regional operators. Partners present compute, GPU, storage, and AI services under their own brand — their logo, their domain, their pricing, and their customer relationships. Aurora operates the infrastructure and remains invisible at every layer. Partners own the margin.

What regulatory and data residency requirements does Aurora support?

Aurora deploys infrastructure in-region by default — data does not leave the target jurisdiction unless explicitly configured to do so. Aurora's network layer is built for GDPR, the EU AI Act, and NIS2 compliance contexts, with VPC isolation, BYO KMS encryption, full audit logging, and air-gapped deployment options. Aurora supports organizations with data sovereignty requirements that hyperscaler infrastructure cannot satisfy.

How quickly can Aurora deploy infrastructure?

Aurora Storage is available immediately via self-serve free trial. Software platform deployments on existing customer infrastructure go live in 1 to 4 weeks. New GPU builds take 8 to 16 weeks depending on configuration. Full data center builds from greenfield take 8 to 16 weeks for the hardware layer, with the Aurora platform deployed on top within 2 to 4 weeks.

Private AI Infrastructure, Built & Operated

THE AURORA AI STACK

Delivering the Full AI workload.

Full-stack means every layer is Aurora's responsibility and yours to command: the building, the power, the GPUs that train and serve your models, the storage that feeds them, and the cloud services that put them to work.

Pick one layer or the whole stack. It is built to work together.

Inference

Private, in-region inference and agents. More tokens per dollar, and your data stays in your region.

Explore Aurora Inference →

Compute

Dedicated compute, VMs, and managed Kubernetes, operated by Aurora so your team ships instead of running ops.

Explore Aurora Compute →

Storage

Self-serve S3-compatible object storage that feeds your clusters. $5.99/TiB/mo, 1 TiB free to start.

Explore Aurora Storage →

GPU Clusters

Turnkey managed GPU clusters that train and serve your models, built and operated by Aurora.

Explore Aurora GPU →

Data Centers

Bespoke, MW-scale data center buildout for dedicated capacity on your terms. Owned facilities, built for AI density.

Explore Aurora Data Centers →

How the layers work together

Compute, or the Aurora AI Cloud turns raw capacity into services: inference endpoints, agents, and dedicated compute.
Inference is the managed serving layer: private, in-region endpoints and stateful agents, autoscaled and metered per token.
Storage feeds the clusters. Datasets, checkpoints, and artifacts sit close to compute, in-region, S3-compatible.
GPU Clusters train and serve your models. They are the compute core of the stack.
Data Centers provide the bespoke, MW-scale capacity underneath it all when standard footprints are not enough.
One console ties them together: dashboard, buckets, credentials, VMs, networks, volumes, and event log behind a single login.

Which layer do I start with? Training, or want dedicated GPU capacity you control? Start with GPU Clusters
Need somewhere to put data first? Start with Storage (free 1 TiB, instant self-serve today).
Just need to run a model behind an API? Start with Inference.
Need dedicated, sovereign capacity at scale? Start with Data Centers
Not sure? Talk to Sales and we will map the stack to your workload.

We build our infrastructure so we can stand behind it.

Aurora is built by engineers who have stood up GPU clusters at scale and lived with them in production: the power, the cooling, the networking, the scheduling, and the discipline it takes to keep ten thousand nodes healthy at three in the morning. We build our own infrastructure because that is the only way to be certain it holds when your model is training and your customers are depending on it.

So we do not hand you a rack and walk away. We own the data centers, we procure the hardware, we deploy the cluster, and we operate it for as long as you run on it. When we commit capacity, it is backed by hardware and real estate we control, not an allocation we are waiting on. You get infrastructure you direct, in the region your data has to live, under a brand you choose.

In-region by default.

Your data stays in the jurisdiction you choose.
Across our global footprint, or in your own facility.

Your brand or ours.

White-label the full stack for you or your partner.
Your customers see your name.

No lock-in.

Open, standards-based interfaces across the stack.
S3-compatible storage, OpenAI-compatible inference, the models you choose.

Aurora runs managed operations for as long as you run on the platform:

24x7 monitoring, incident response, and on-call staffed by the team that built your cluster.
A 99.9% uptime SLA backed by managed operations, not best-effort.
Firmware, driver, and scheduler maintenance handled for you, with change windows you approve.
Capacity planning and expansion so you scale in weeks, not procurement cycles.
A single event log and console view so your team always sees what we see.

Why choose Aurora for private AI infrastructure?

Because the hard part is not buying GPUs.

It is powering, cooling, networking, scheduling, securing, and operating them, and then earning back what they cost. The real advantage comes from utilization: keeping expensive silicon busy instead of idle, with scheduling that fills the clusters and an inference layer that turns spare capacity into served tokens.

Aurora does the full job and keeps doing it, so your GPUs earn their keep.

GPUs that earn their keep.

Scheduling and an inference layer that turn idle capacity into served tokens.

One console for everything.

Dashboard, buckets, credentials, VMs, networks, volumes, event log.

Open and portable.

S3-compatible storage, OpenAI-compatible inference, standards-based interfaces.

Sovereign by default.

In-region hosting, with sovereign and air-gapped options.

We operate what we build.

Managed new builds, 24x7 ops, one team from the rack through daily run.

Your brand, or your partner's.

White-label across the stack if you choose.

Aurora supports three hosting models so infrastructure lands where your data and policy require:

Aurora-hosted: in our global network of owned data centers. We run the facility and the stack.
Customer-hosted: we build and operate inside your facility, under your control.
Hybrid: a mix, so sensitive workloads stay in-region while burst or non-sensitive workloads run elsewhere.

Aurora runs on open, standards-based interfaces, so your data and models stay portable in and out:

Storage speaks S3, so your existing tooling and SDKs work unchanged.
Inference speaks the OpenAI API, so you can point your app at Aurora, or away from it, with a config change.
Compute runs standard VMs and Kubernetes, so your images and orchestration move without rework.
Your data, models, and workloads stay portable, and you can take them with you cleanly whenever you choose.

What is data sovereignty, and how does Aurora deliver it?

Data sovereignty means your data is subject to the laws* and governance of the jurisdiction it sits in, and stays there by design.

Aurora delivers it structurally: in-region hosting by default, owned data centers in the regions we serve, and sovereign and air-gapped options for the most sensitive workloads.

* Aurora does not provide legal or compliance advice. Confirm applicability with your legal team.

In-region by default.

Data stays in the jurisdiction you choose.

Residency for GDPR and the EU AI Act.

European workloads stay in Europe.

Sovereign and air-gap options.

For regulated, classified, or isolated environments.

You hold the keys.

Aurora operates the stack; you retain control of your data and your brand.

Sovereignty controls

Regional residency: workloads and data pinned to a chosen region, enforced at the infrastructure layer.
Sovereign hosting: deployment inside a jurisdiction with operations and access scoped to that jurisdiction.
Air-gapped deployments: physically or logically isolated environments for the highest-sensitivity workloads.
Auditability: a single event log across the stack, so access and change history are visible to your team.
Regulatory alignment: designed to support GDPR data residency and EU AI Act obligations. (Note: Aurora does not provide legal advice; confirm applicability with your legal team.)

Sovereignty + Private AI

Private AI means your models and data run on dedicated infrastructure you control. Sovereignty adds the jurisdictional half of that promise: private, and running under the laws you choose.
Aurora delivers both at once.

Private AI infrastructure,
built & operated in your region