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Sep 05 2025
Cloud

Why Some Workloads Are Coming Home: The Case for Cloud Repatriation in Healthcare

As cloud costs rise, healthcare IT leaders rethink what truly belongs off-prem, such as resource-heavy workloads and sensitive patient data.

To better position themselves to invest in artificial intelligence (AI), healthcare IT leaders are under pressure to curb cloud spending. That’s why many organizations are re-evaluating their “cloud first” strategies and moving some workloads from the public cloud back on-premises, which can be considered a “cloud smart” approach. It’s a trend known as cloud repatriation, and it’s meant to reallocate the 21% of cloud infrastructure spending that is typically wasted on underused resources.

However, repatriation requires strategic planning. Healthcare IT teams must select which workloads should reside in the data center and which ones should remain in the cloud, according to Caitlin Gordon, vice president of product management at Dell Technologies.

Sometimes, teams opt for a hybrid or multicloud setup, with multiple vendors in a data center. Others prefer using multiple public clouds for different workloads, notes Gordon.

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The Driving Force Behind Cloud Repatriation

Although only 8%-9% of organizations intend to implement full workload repatriation, the cost and regulatory challenges driving the trend are real, according to IDC’s recent Server and Storage Workloads Survey.

“What we hear a lot is that the unpredictability of cost for some workloads in the cloud has become untenable,” Gordon says. Teams also need to make room in their budgets to afford AI infrastructure.

Healthcare organizations using “private AI” applications are likely turning to on-premises infrastructure, says Rob Tiffany, research director for cloud and edge infrastructure at IDC.

“They’re going to have large language models or small language models running on their own gear and training or fine-tuning those AI models with their own private corporate data,” Tiffany says. These companies are hesitant to share their LLMs with AI vendors.

EXPLORE: How do cloud lifecycle services optimize cloud operations?

How Healthcare Is Moving Data Workloads

Strict compliance or data residency regulations govern data sets, potentially leading healthcare organizations to repatriate and move IT networks back on-premises.

Compliance requirements are also a big factor — particularly in healthcare. The cost to retain data in electronic health records could impact healthcare providers’ decisions on how to design their architecture, according to Gordon. Genomic sequencing, for example, is a resource-intensive workload that could leverage data on-premises. Healthcare organizations often operate in hybrid mode using Software as a Service.

“Although there might be some experimenting in the cloud, regulated industries are thinking about how to keep production more centrally in their data center for security and availability,” Gordon says.

In other words, he says, the most valuable or most sensitive data could end up back in the data center.

Tiffany recommends keeping sensitive data in private cloud infrastructure, and then running large workloads in Microsoft Azure or Amazon Web Services. Application programming interfaces make this integration between the public and private cloud possible.

READ MORE: Build healthcare IT infrastructure to meet the needs of organizations.

How To Handle Cloud Repatriation

A tech partner such as CDW can help teams modernize on-prem infrastructure using hyperconverged infrastructure and software-defined data centers. Experts can also run cloud infrastructure assessments to determine which specific workloads are ideal for repatriation. NetApp and PureStorage are two companies that offer high-performance storage options for AI workloads.

“Scale your compute and storage separately, and don’t lock in to any single vendor,” Gordon advises.

“In a hybrid strategy, make sure those workloads are either using traditional VMs or containers like Kubernetes, so that they’re more portable from the get-go,” Tiffany says. “Then, make sure that you actually have a legitimate hybrid cloud infrastructure between your public and private clouds, so that those workloads can move back and forth freely as needed.”

For example, a Microsoft database workload in SQL Server running on-premises could sync data with an Azure SQL Server, according to Tiffany. This kind of “disaggregated model,” says Gordon, allows teams a bit more flexibility as they restructure workloads.

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