Companies are moving selected workloads from public cloud to private cloud, colocation facilities or their own data centers—not abandoning cloud across the board. The reasons are workload-specific: security and compliance, data location, more predictable costs, integration with existing systems, latency, and the demands of AI data and inference. For many organizations, the practical answer is a mix of environments rather than a wholesale return to self-managed IT.
What “moving back to on-premise” means
Cloud repatriation describes moving a workload out of public cloud into infrastructure a company owns or operates, such as its own data center, a colocation facility or a privately operated cloud. It can also mean moving only certain applications or datasets while keeping other services in public cloud.
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That distinction matters: considering a move is not the same as completing one, and a workload returning to private infrastructure does not mean the company has reversed all of its cloud adoption. Uptime Institute has characterized repatriation as overstated, while vendor surveys report different proportions of organizations considering or completing selective moves.
Why companies are reconsidering where workloads run
Security, compliance and data sovereignty
Control over sensitive data is a leading reason organizations consider repatriation. Broadcom’s 2025 survey of 1,800 senior IT decision-makers identified security as the leading driver and found that 66% were very or extremely concerned about public-cloud compliance (Broadcom, Private Cloud Outlook 2025). The survey result reflects respondents’ concerns; it does not establish that public cloud is inherently less secure.
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Organizations may also need tighter control over where data is stored, processed and accessed. Keeping a workload in a private environment can make it easier to align infrastructure and access practices with particular regulatory or internal requirements, though the organization still has to operate and secure that environment effectively.
Costs that are hard to predict
Public cloud can suit variable demand because capacity can scale without buying hardware in advance. But persistent workloads, large storage estates and frequent data transfers can make bills harder to forecast. Rackspace Technology’s 2025 survey found cost savings were a stated repatriation rationale for 44% of respondents; that is a reported reason, not proof that on-premises is cheaper overall (Rackspace Technology, 2025).
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A fair comparison has to include more than cloud charges versus server prices. For private or on-premises infrastructure, count hardware refreshes, facilities, power, staffing, software licenses, backup, disaster recovery, security tools and migration effort. Cloud comparisons should include storage, data transfer and the operational work needed to manage the services. Savings depend on the workload, utilization and the full cost of running each option.
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Moving a large dataset or repeatedly transferring it between a cloud service and local systems can add cost and operational friction. Applications connected closely to factory equipment, local networks, specialized hardware or existing systems may also work better near those dependencies.
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VMware’s 2025 survey reported that workloads considered for repatriation were often described as having high security or compliance concerns (51%), being data-intensive (46%), needing integration with other systems (41%) or being latency-sensitive (36%) (VMware, Private Cloud Outlook 2025). These are survey findings about workload characteristics, not universal thresholds for choosing infrastructure.
AI workloads and data gravity
AI can intensify the pressure to keep data and computing close together. 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 be on-premises for security or compliance (Cloudian/Centiment, 2026).
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Those responses describe surveyed organizations, not a general engineering limit on cloud AI. The right placement still depends on the model, dataset, latency target, accelerator availability, architecture and security requirements. Some organizations may keep training data on-premises while using cloud resources for other parts of an AI workflow.
How widespread is repatriation?
Survey results suggest that repatriation is a real consideration, but they do not support a single, comparable estimate of how much of the market is moving. In VMware’s 2025 findings, 69% of enterprises were considering moving workloads from public to private cloud, while 35% said they had already repatriated some workloads. Rackspace Technology’s 2025 survey likewise reported 69% considering repatriation of at least part of their workloads; respondents cited data security and compliance (50%), integration (48%) and cost savings (44%) as reasons.
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Citrix 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. In that survey, unexpected security issues (41%) and high project expectations (29%) were reported as leading drivers for moving some cloud-based workloads back to on-premises infrastructure (Citrix, February 14, 2024).
These figures should not be combined into a market-wide rate: the surveys differ in sponsor, sample, geography and question wording. They indicate interest and selective movement, not a synchronized exodus from cloud.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which workloads are stronger candidates to move?
Repatriation is most plausible when a workload has specific needs that a company can meet more effectively in a private environment. Consider evaluating workloads that have one or more of these characteristics:
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- Large, frequently accessed datasets where transfers to and from cloud create substantial cost or delay.
- Stable, sustained utilization that may be economical to operate on appropriately sized infrastructure.
- Low-latency requirements or close dependencies on local systems, equipment or networks.
- Integration needs that are difficult or costly to satisfy across environments.
- Recovery requirements the organization can meet with its own facilities and tested procedures.
These are prompts for analysis, not automatic reasons to move. A workload may also depend on cloud services, elastic capacity or geographic reach that would be costly or difficult to replace.
How to compare public cloud, private cloud, colocation and on-premises
| Decision factor | Questions to ask |
|---|---|
| Security and regulatory control | Who controls encryption keys, access, data location, logging and incident response? |
| Cost shape | How do variable consumption, capital investment, fixed operating costs, staffing and data-transfer charges compare? |
| Performance | What latency, throughput and accelerator access does the workload need, and where are its users and data? |
| Integration | How closely does the workload depend on local identity, networking, specialized hardware or other systems? |
| Resilience | Can the environment provide the required redundancy, backup, geographic distribution and tested recovery objectives? |
| Operational capability | Does the organization have the skills, staffing and support coverage for patching, monitoring and procurement? |
| Portability | How difficult would it be to move the workload and its data again, and how dependent is it on a particular provider? |
A measured way to make the decision
- Inventory workloads and dependencies. Record where each application’s data lives, which systems it connects to and which cloud services it uses.
- Classify each workload. Assess data sensitivity, latency, utilization stability, integration depth, recovery requirements and portability.
- Compare full operating costs and capabilities. Include facilities, hardware, staffing, software, data transfer, security and recovery—not only infrastructure charges.
- Pilot one or two candidates. Measure total cost, performance, reliability and operational effort in the destination environment.
- Keep workloads in cloud where its advantages are clear. Elastic capacity, global reach or managed services may still make public cloud the better fit for particular applications.
Hybrid deployment is best understood as a placement strategy: put each workload where its requirements and economics are met most effectively, and preserve the ability to use different environments when needs change.
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