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Hyperscalers Make AI Cost Unpredictable — and Hard to Leave.
Public clouds aren't built for the way AI actually runs.
Compute is priced for the workloads hyperscalers want to keep, storage is cheap to fill and punishing to use, and egress fees make it uneconomical to run AI against your own data. As your datasets and GPU footprint grow, the bill climbs with no ceiling — and leaving only gets harder.
Aurora owns the stack — the data centers, the platform, and the hardware financing — so variable, uncapped cost becomes fixed, predictable capacity. Private compute, managed Kubernetes, GPU clusters, and S3-compatible storage, deployed in your region and operated by Aurora — to run yourself or resell under your brand. You keep the data, the jurisdiction, and the margin.
Two Products. One Private Cloud Solution.
Aurora Compute and Aurora Storage run on the same management layer, the same SLA, and the same operational model. Provision a GPU-aware Kubernetes cluster and the object storage that feeds it from a single portal — white-labeled under your brand, with Aurora invisible at every layer.
What Every Aurora AI Cloud
Deployment Includes:
What Enterprises Run on The Aurora AI Cloud Platform
Organizations with owned GPU or CPU infrastructure that want to run private AI workloads without routing through a hyperscaler. Aurora deploys the platform, manages operations, and keeps data in-region.
GPU-aware Kubernetes clusters for inference endpoints, model fine-tuning, and batch training — fed by high-ingest S3 storage with no egress drag on AI economics. Teams focus on models, not infrastructure.
Dedicated instances, private networking, and immutable storage for organizations with data residency, compliance, or air-gap requirements. Compute and data stay in-region, under your control.
Archive &
Disaster Recovery
Immutable WORM storage for ransomware-resilient backup, with configurable retention and legal hold. Lifecycle policies move data automatically between active and archive tiers.
Full S3 API compatibility means most workloads migrate without code changes — and no egress fees on the way out of Aurora once you're in. No lock-in, in either direction.
Operators with rack space, hardware, or capacity to monetize white-label compute, storage, and AI under their brand — or build a regional cloud or GPUaaS offering outright. Aurora runs the platform; you set pricing, own the customer, and earn on every GPU-hour and TiB.
Let's Scope Your Deployment.
Whether you're running compute and storage for your own AI workloads or building a cloud to resell, the technical demo is the fastest way to see exactly what Aurora can deliver for your infrastructure and your business model.