What types of data center engagements does Aurora deliver?

Aurora delivers four types of data center engagements. New capacity development builds net new AI data center infrastructure from the ground up — Aurora procures, installs, finances, and operates the full stack. Modernization and retrofit upgrades existing facilities to support GPU and AI workloads without a full rebuild. Migration and replatforming moves data and workloads from AWS, GCP, Azure, or co-location environments to private infrastructure in-region. Operations and scale provides long-term managed operations — monitoring, incident response, capacity scaling, and quarterly expansion planning — for infrastructure Aurora has deployed.

How long does it take Aurora to deploy new AI data center capacity?

Aurora delivers new GPU capacity in 8 to 16 weeks depending on configuration. CPU-only builds take approximately 8 weeks. 8-node GPU configurations take approximately 12 weeks. 32-node GPU configurations take approximately 16 weeks. Software platform deployment on existing infrastructure takes 2 to 4 weeks. All timelines are indicative — Aurora scopes every engagement and confirms timelines during the advisory process.

What scale of data center can Aurora build and operate?

Aurora deploys infrastructure from 5 kW edge pods to MW-scale AI training environments. The modular approach means deployments start where the customer is and scale incrementally without architectural dead ends. Reference configurations include an 8-node B300 cluster for AI inference and fine-tuning at approximately 100 kW, and a 32-node B300 cluster for large AI training at approximately 400 kW.

What are the indicative costs for an Aurora AI data center build?

Aurora publishes three reference configurations as indicative benchmarks. A large AI training environment with 32x HGX B300 nodes (256 GPUs) has an indicative total capex of $17.5 to $19.0 million. An AI inference and fine-tuning environment with 8x HGX B300 nodes (64 GPUs) has an indicative total capex of $4.5 to $5.0 million. A general cloud environment with 16x Dell R760xa servers and no GPU has an indicative total capex of $0.8 to $1.3 million. All figures are indicative — confirmed pricing is delivered through the Aurora Advisor engagement process.

Can Aurora procure and finance hardware on behalf of the customer?

Yes. Aurora can procure, install, and finance hardware — clients can access new AI data center capacity without upfront hardware capex. Customers choose their delivery model — a full white-labeled platform or a Kubernetes API handoff — with or without hardware financing. Contact an Aurora Advisor to discuss the financing model applicable to a specific engagement.

How does Aurora handle migration from hyperscalers to private infrastructure?

Aurora manages the full migration of data and workloads from AWS, GCP, Azure, or co-location environments to private AI infrastructure. Data migration is anchored in Aurora's S3-compatible storage. Workloads are replatformed into Aurora Compute and GPU infrastructure. Migration is phased to maintain uptime and reduce risk. There are no egress fees on data leaving Aurora once migrated. The destination infrastructure is operated by Aurora post-migration, in-region and under the customer's control.

What ongoing operations does Aurora provide after deployment?

Aurora provides 24/7 monitoring and incident response, GPU driver and firmware and software updates, capacity scaling as workloads grow, SLA-backed 99.9% uptime guarantees, and quarterly infrastructure review and expansion planning. Aurora operates what it builds — the engagement does not end at deployment. Aurora stays in the stack as a long-term operational partner.

Who is Aurora's data center infrastructure built for?

Aurora builds for six types of organizations. Data center operators looking to introduce AI capacity or offer private AI services to enterprise tenants. Enterprises and public sector organizations deploying private AI for their own operations. Telcos and regional operators with rack space and power capacity who want to launch GPU cloud services under their brand. Resellers and channel partners introducing AI data center capability into enterprise accounts. Governments and regulated industries with data sovereignty requirements that hyperscaler infrastructure cannot satisfy. Greenfield organizations starting from no existing hardware or facility.

Private AI Data Center Infrastructure

AI Data Center Capacity Is the Bottleneck. Aurora Removes It.

The top four hyperscalers are spending over $600 billion on AI infrastructure in 2026 and still cannot build fast enough.

Aurora is the engineering partner filling that gap — designing, deploying, and operating private AI data center infrastructure for operators, enterprises, and reseller partners. Deployments scale incrementally from a single edge pod to a multi-MW environment, without architectural dead ends.

$600B+ hyperscaler AI infra spend in 2026

5kW →MW deployment scale range

8-16 wks new GPU build lead time

99.9% platform uptime SLA

Aurora Infra scalable private AI infrastructure

Four Ways Aurora Delivers Data Center Infrastructure.

Engagements are delivered directly or in collaboration with local operators, depending on regulatory, geographic, and operational requirements.

New Capacity Development

Aurora delivers net new AI data center capacity using modular GPU infrastructure as the core unit. From 5 kW edge pods through MW-scale AI training environments — Aurora procures, installs, finances, and operates the full stack.

Modular GPU builds — H100, B200, B300
5 kW edge deployments to MW-scale AI environments
Aurora procures, installs, and finances hardware
Full platform deployment — compute, storage, network
Managed operations from day one
Lead time: CPU-only ~8 wks | 8-node GPU ~12 wks | 32-node ~16 wks

Modernization & Retrofit

Existing data centers modernized for AI workloads. Aurora introduces GPU compute infrastructure and upgrades storage, networking, and power to support AI capacity — without requiring a full facility rebuild.

GPU node integration into existing rack infrastructure
Storage upgrades for AI ingest pipelines
Networking upgrades — InfiniBand and high-speed Ethernet
Power and cooling assessment and upgrade planning
Aurora AI Platform deployed on upgraded infrastructure
Incremental deployment — start with one rack, scale from there

Migration & Replatforming

Aurora manages the migration of data and workloads from AWS, GCP, Azure, or co-lo environments to private AI infrastructure — in-region, under your control, with no hyperscaler dependency on the other side.

Data migration anchored in Aurora Data Management (S3-compatible)
Workload replatforming into Aurora Compute and GPU infrastructure
Phased migration to maintain uptime and reduce risk
Aurora operates the destination infrastructure post-migration
No egress fees on data leaving Aurora once migrated
In-region deployment — data stays in jurisdiction

Operations & Scale

Aurora manages day-to-day operations of deployed infrastructure. Monitoring, incident response, capacity scaling, and long-term expansion planning — with a 99.9% SLA and a dedicated engineering team behind it.

24/7 monitoring and incident response
GPU driver, firmware, and software updates
Capacity scaling as workloads grow
SLA-backed uptime guarantees
Quarterly infrastructure review and expansion planning
Long-term operational partnership — Aurora stays in the stack

Three Reference Configurations for New Capacity.

All configurations are indicative. Aurora scopes every engagement to the specific requirements of the deployment — hardware, power, networking, and operational model.

	Config A	Config B	Config C
Use Case	Large AI Training	AI Inference & Fine-Tuning	General Cloud (no GPU)
GPU / Compute	32x HGX B300 — 256 GPUs	8x HGX B300 — 64 GPUs	16x Dell R760xa — no GPU
Est. HW Capex	$16.0-16.8M	$4.0-4.2M	$0.4-0.6M
Networking	$0.8-1.2M	$0.3-0.5M	$0.1-0.2M
Storage	$0.3-0.6M	$0.2-0.3M	$0.3-0.5M
Power Draw	~400 kW	~100 kW	~30 kW
Hosting / Month	~$78K	~$20K	~$6K
Total Capex	$17.5-19.0M	$4.5-5.0M	$0.8-1.3M
Lead Time	~16 weeks	~12 weeks	~8 weeks

The Engineering Partner Behind Your Infrastructure.

Aurora was founded by a team that combines deep systems engineering expertise with decades of experience delivering and operating large-scale data center and enterprise infrastructure globally. That foundation allows Aurora to design and deploy infrastructure that is technically robust, legally defensible, and commercially viable.

Modular Deployment

Aurora deploys capacity incrementally, not monolithically. Start with what you need today. Add capacity as workloads grow. No architectural dead ends.

Faster Time to Market

GPU builds from 8-16 weeks. Software platform deployment on existing infrastructure in 2-4 weeks. Aurora compresses the timeline from decision to live infrastructure.

Zero Client Capex Options

Aurora can procure, install, and finance hardware. Clients choose their delivery model — full white-labeled platform or Kubernetes API handoff — with no upfront hardware cost.

Existing Asset Utilization

Organizations with owned rack space, hardware, or data center capacity can monetize it without a full rebuild. Aurora deploys the platform, operates the stack, and handles everything behind the scenes.

In-Region Deployment

Aurora deploys in-region. Data stays where regulations and operations require it. Nordics, GCC, North America Turkey, and APAC regions supported.

Long-Term Operations

Aurora operates what it builds. The engagement doesn't end at deployment — Aurora stays in the stack for monitoring, scaling, and long-term infrastructure management.

Built for Operators, Enterprises, and Partners.

Data Center
Operators

Operators with existing facilities looking to introduce AI capacity, modernize for GPU workloads, or offer private AI services to enterprise tenants. Aurora provides the platform and the operational model.

Resellers & Channel Partners

Partners who want to introduce AI data center capability into enterprise accounts without building or operating it themselves. Aurora provides the infrastructure and the engineering depth — partners own the customer relationship.

Enterprises &
Public Sector

Organizations that need private AI infrastructure for their own operations — not to resell. PiB-scale data handling, regulated workloads, and in-region deployment requirements.

Governments & Regulated Industries

Organizations with data sovereignty requirements, air-gap needs, or regulatory obligations that hyperscaler infrastructure cannot satisfy. Aurora builds and operates in-country.

Telcos &
Regional Operators

Telcos and ISPs with rack space and power capacity who want to launch GPU cloud or private AI services under their brand. Aurora deploys and operates — they sell and keep the margin.

Greenfield AI
Builds

Organizations starting from scratch — no existing hardware, no existing facility. Aurora scopes, procures, finances, and builds the infrastructure from the ground up.

Resell AI Data Center Capacity Under Your Brand.

Aurora's data center infrastructure is available as a fully white-labeled service.
Your customers get private AI infrastructure — with your brand on the portal, the domain, and the billing.
Aurora engineers and operates everything behind it.

Learn more about the Aurora Partner Program

Every Infrastructure Engagement Starts with a Conversation.

Data center infrastructure is not a self-serve decision. Whether you're scoping a new build, planning a modernization, or evaluating a migration — the fastest path to a proposal is a direct conversation with an Aurora Advisor.
We scope your deployment, identify the right configuration, and deliver a written requirements document and indicative pricing within 1-2 calls.

Talk to an Aurora Advisor

Tell us about your infrastructure needs. We'll scope the right engagement and get you to a plan.

Connect with Aurora

Private AI Data Center Infrastructure.Built, Operated, and Scaled.