Companies are moving selected workloads—not their entire IT estates—back from public cloud when security and compliance, data location, predictable costs, latency, or ties to existing systems make another environment a better fit. Survey findings show that repatriation is a real consideration, but they support a shift toward hybrid placement, not a wholesale retreat from cloud.
What “moving back to on-premises” means
Cloud repatriation is the relocation of a workload from public cloud to a company’s own data center, a colocation facility, or a privately operated cloud. It does not necessarily mean abandoning public cloud: an organization might move a sensitive database or latency-critical application while keeping elastic, globally distributed, or managed services in the public cloud.
The distinction between considering a move and completing one matters. VMware’s 2025 Private Cloud Outlook reported that 69% of enterprises were considering moving workloads from public to private cloud, while 35% had already repatriated some workloads. Those figures describe different stages, not a count of companies that have fully reversed cloud adoption.
Why companies are reconsidering where workloads run
Security, compliance, and data sovereignty
Control requirements are among the most frequently cited reasons. Broadcom’s 2025 survey of 1,800 senior IT decision-makers identified security as the leading driver of workload repatriation; 66% said they were very or extremely concerned about public-cloud compliance. Rackspace Technology’s 2025 survey found that 50% cited data security and compliance as a reason for considering repatriation.
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Keeping a workload in a controlled environment may make it easier for an organization to manage access, encryption keys, logging, and the physical or legal location of data. That does not make on-premises infrastructure inherently secure or automatically compliant: the organization still has to configure, monitor, patch, and protect it, and meet the rules that apply to its data and operations.
More predictable costs for steady, data-heavy workloads
Public cloud can suit changing demand because organizations can scale consumption rather than buy capacity for a peak. But for a workload that runs at consistently high utilization or moves large amounts of data, recurring compute, storage, and transfer charges can be harder to predict. Rackspace Technology reported that 44% of respondents cited cost savings as a repatriation rationale. That is a reported motivation, not evidence that on-premises is always less expensive.
A fair comparison must include more than cloud bills or server purchase prices. For an owned environment, account for hardware refreshes, facilities, power, staffing, software licenses, backup, disaster recovery, security tooling, and the effort and risk of migration. Compare those costs with the cloud charges for the same workload, including storage and data movement, over a period that captures capacity changes and refresh cycles.
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Latency, data movement, and integration
An application may depend on industrial equipment, local networks, specialized hardware, or systems that are difficult to move. Putting a tightly coupled workload near those dependencies can reduce network delay and avoid repeatedly transferring large datasets. VMware’s 2025 survey reported that organizations identified high security or compliance concerns (51%), data intensity (46%), integration with other systems (41%), and latency sensitivity (36%) among characteristics driving workloads toward private cloud.
These are survey responses about reported workload drivers, not performance guarantees. Whether a move improves response time or reduces transfer costs depends on the application’s architecture, where its users and data are, and the network connections between components.
AI adds pressure around data location and response time
AI workloads can involve large training datasets and repeated access to data during inference. Cloudian’s 2026 survey, conducted with Centiment, found that 55% of respondents said cloud could not consistently meet their AI-inference latency requirements, while 52% said AI-training data needed to remain on-premises for security or compliance.
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These are respondents’ reported constraints, not universal limits of cloud infrastructure. Accelerator availability, data architecture, model-serving design, and user location all affect placement. For some organizations, the practical answer may be to keep data or inference close to local systems and use cloud resources for other stages.
How widespread is repatriation?
Several surveys indicate substantial interest, but their results should not be added together or treated as one market-wide estimate: sponsors, samples, geographies, and question wording differ. VMware’s figures distinguish organizations considering a move from those that have already moved some workloads. Rackspace Technology’s 2025 survey likewise reported that 69% were considering repatriation, but that is not a measure of completed migrations.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsCitrix reported in February 2024 that 42% of surveyed U.S. organizations were considering or had already moved at least half of their cloud workloads back on-premises. The combined figure includes both consideration and action; it does not mean that 42% had completed such a move. Citrix also reported unexpected security issues (41%) and high project expectations (29%) as leading drivers for moving some cloud workloads back.
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Uptime Institute has characterized cloud repatriation as overstated, a useful caution against interpreting survey interest as a mass return to self-managed IT. The evidence here supports selective relocation and mixed environments, not a universal reversal of cloud adoption.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which workloads are stronger candidates to move?
Start with workload characteristics rather than a blanket preference for one deployment model. A workload may merit evaluation for private cloud, colocation, or on-premises operation when it has one or more of these traits:
- Sensitive data or regulatory obligations that require tighter control over location, access, or processing.
- High, stable utilization that makes owned capacity worth comparing with recurring consumption charges.
- Large datasets or frequent data movement that create material transfer cost or delay.
- Strict latency needs or close dependencies on local users, equipment, networks, or systems.
- Integration requirements that make a public-cloud migration costly or operationally complex.
Conversely, public cloud can remain a strong fit when demand is unpredictable, rapid scaling matters, users are distributed, or a managed service materially reduces operational work. Placement can differ by application, and even by component within an application.
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Compare deployment options before deciding
“On-premises” is not the only alternative to public cloud. A private cloud may be operated by the organization or a provider; colocation puts company-owned equipment in a third-party facility. Compare the actual service and responsibility model, not just the label.
| Decision axis | Questions to answer |
|---|---|
| Security and regulatory control | Who controls encryption keys, access, data location, logging, and incident response? What obligations apply to the data and workload? |
| Cost shape | How do variable consumption charges compare with capital investment and fixed operating costs? Include egress, staffing, facilities, and refreshes. |
| Performance | What latency and throughput are required? Where are users, data, and accelerators located? |
| Integration | What dependencies exist on local systems, identity, networks, or specialized hardware? |
| Resilience | How will redundancy, backup, disaster recovery, geographic distribution, and recovery objectives be delivered and tested? |
| Operational capability | Can the organization support patching, monitoring, procurement, incident response, and coverage outside business hours? |
| Portability | How difficult would it be to move the workload and its data again? What provider-specific services or interfaces does it depend on? |
A measured way to decide whether to repatriate
- Inventory workloads and dependencies. Record data sensitivity, latency needs, utilization patterns, integration depth, recovery requirements, data movement, and portability for each candidate.
- Compare total cost for equivalent service. Model cloud consumption and transfer costs against the full cost of private or owned infrastructure, including staffing, resilience, security, facilities, and migration.
- Check operational readiness. Confirm that the team can operate the proposed environment, maintain it, and meet recovery and security requirements—not merely acquire the hardware.
- Pilot one or two candidates. Measure cost, performance, reliability, and operational effort against explicit targets before committing to a broader move.
- Keep workloads where they deliver clear value. Retain public-cloud services when elasticity, global reach, or managed capabilities outweigh the reasons to relocate.
Hybrid deployment is best treated as a workload-placement strategy: choose the location that meets each application’s requirements, then manage the resulting environment as a coherent system.
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