“We have a backup system” is often treated as a sufficient answer to data loss risk. However, the real cost of data loss is not limited to the technical value of lost files, deleted records, corrupted databases, or inaccessible systems. When data loss occurs, the real bill is created by a combination of operational downtime, lost revenue, customer churn, regulatory exposure, legal costs, reputational damage, recovery expenses, employee productivity loss, insurance impact, and crisis management efforts.

Most organizations connect to cloud services over the public internet. To access a resource on AWS, Microsoft Azure, Google Cloud, or another cloud provider, traffic leaves the company office, data center, or branch location, enters the internet service provider’s network, passes through several transit points, and finally reaches the cloud provider’s network. This connectivity model can be sufficient for many standard workloads. However, it is not always enough. For critical applications, large-scale data transfers, hybrid cloud architectures, financial transaction systems, backup replication, AI/ML data pipelines, and regulated workloads, the variable performance of the public internet can become a limitation in terms of security, latency, reliability, and cost.

Backup strategy is often treated as a secondary infrastructure topic until an organization experiences data loss, a system outage, or a ransomware attack. However, backup is not simply about keeping a copy of files. It is a critical layer that directly affects business continuity, data integrity, cyber resilience, and regulatory compliance. Buying backup software, planning storage capacity, defining retention policies, monitoring backup jobs, troubleshooting failed backups, testing restores, and keeping the system operational 24/7 requires continuous operational effort. For many IT teams, this workload is difficult to sustain alongside daily infrastructure responsibilities.

The way organizations manage IT infrastructure is changing fundamentally. Buying physical servers, moving them into a data center, configuring them, maintaining them, and managing hardware refresh cycles require significant capital investment and continuous operational expertise. IaaS, or Infrastructure as a Service, is a cloud computing model where core IT infrastructure resources such as servers, storage, networking, and virtualization are delivered as a service over the internet. Instead of owning physical hardware, organizations use infrastructure resources through a usage-based or contract-based service model.

Artificial intelligence projects are no longer only on the agenda of large technology companies. Organizations of all sizes are now exploring model training, inference infrastructure, large-scale data analytics, real-time decision systems, and high-performance computing workloads. However, these workloads share one critical reality: standard data center infrastructure is not designed to support AI and GPU-intensive workloads efficiently.