Question 1

What topics does Aurora publish insights on?

Accepted Answer

Aurora publishes technical insights on private AI infrastructure — including GPU cluster deployment and management, S3-compatible object storage architecture, data residency and regulatory compliance for AI workloads, sovereign AI deployment, managed Kubernetes for AI, and the economics of operating AI outside hyperscaler platforms.

Question 2

Who writes Aurora's technical content?

Accepted Answer

Aurora's content is produced by its engineering team — the same team that designs, deploys, and operates private AI infrastructure for enterprises, telcos, and governments. Aurora's engineering leadership includes one of the original architects of IPFS and Filecoin, joined by an EU-based team with decades of combined experience across data center builds, global infrastructure deployments, and sovereign AI systems.

Question 3

What is the Aurora perspective on hyperscaler AI infrastructure?

Accepted Answer

Aurora's position is that hyperscaler infrastructure was designed to serve the hyperscaler's business model — not the organization running workloads on it. Egress fees, proprietary APIs, shared compute, and centralized data routing create structural constraints on AI economics, data sovereignty, and regulatory compliance. Private AI infrastructure is an architectural response to those constraints — not a political position.

Question 4

What is sovereign AI and why does it matter?

Accepted Answer

Sovereign AI refers to AI infrastructure and workloads that operate within a defined jurisdiction — under local data residency requirements, local regulatory frameworks, and organizational control over encryption keys and access. It matters because regulations such as GDPR, the EU AI Act, and NIS2 create specific obligations around where data is processed and who can access it. Hyperscaler default architectures route data in ways that are difficult to constrain to a single jurisdiction. Private, in-region AI infrastructure is the practical solution for organizations with sovereignty requirements.

Question 5

What are the economics of private AI infrastructure versus hyperscalers?

Accepted Answer

Private AI infrastructure delivers 50 to 80 percent lower total cost than hyperscalers at production scale. GPU pricing on Aurora 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 billing events for every data movement to compute. At PB-scale data volumes and regular training or inference workloads, the cost difference between private and hyperscaler infrastructure is decisive, not marginal.

Aurora Team

Blog Post by Aurora Team

AI Data Residency Under GDPR and the EU AI Act

The Hidden Cost of Hyperscaler Storage

AI Confidentiality and Data Privacy in Legal

Private AI Infrastructure for Private Equity Portfolios