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Yes. Enterprises are moving some compute and storage workloads from public cloud to private, dedicated, or on-premises environments, but the evidence points to selective placement decisions—not a mass exit from cloud. In IDC’s March 2024 survey, 81% of IT professionals expected some compute-resource repatriation and 83% expected some storage-resource repatriation in the following 12 months; only about 7% expected to repatriate all workloads.
What “cloud repatriation” means
Cloud repatriation is the movement of a workload, data, or application component out of a public-cloud environment and into another location, such as a dedicated or private cloud, a colocation facility, or an organization’s own data center. It can also mean changing where a workload runs at a particular stage of its lifecycle.
That distinction matters: moving a database, backup copy, or processing component does not necessarily mean moving the whole application. IDC’s 2025 cloud-trends analysis describes organizations taking a “right-fit” approach to where applications, workloads, and data reside. The practical pattern is to choose placement workload by workload, while continuing to use public cloud where it fits.
How widespread is the shift?
IDC’s Server and Storage Workloads survey, completed in March 2024 with 2,250 IT professionals, found that 81% expected some compute-resource repatriation and 83% expected some storage-resource repatriation in the next 12 months. Those figures indicate an expectation of moving at least some resources; they do not mean that those shares had already completed a move. About 7% expected complete workload repatriation.
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The broader cloud picture is continued use of multiple deployment models. IDC reported in 2025 that, among cloud buyers surveyed in Q3 2024, 88% were deploying or operating hybrid cloud and 79% were using multiple cloud providers. Hybrid and multicloud adoption can coexist with repatriation: an organization may move one workload closer to its data while retaining other applications in public cloud or with another provider.
IDC also reported that close to half of cloud buyers spent more than expected in 2023, while 59% anticipated similar overruns in 2024. The latter is an expectation reported in 2024, not a measured result for the full year. Separately, a CDW survey summary published in 2024 said 84% of respondents had moved workloads to cloud and later moved some back on premises, and 68% cited security concerns. These are secondary industry figures, and the survey populations and wording differ from IDC’s, so they should not be treated as directly comparable estimates.
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Why move a workload out of public cloud?
Costs that are hard to predict
Consumption-based services can be convenient, but bills may rise when usage, storage, data transfer, or supporting services exceed forecasts. A workload with steady, high utilization may be less expensive on owned or dedicated infrastructure over time. That is not automatic: the comparison needs to include migration, data egress, software licensing, facilities, power, staffing, maintenance, and realistic utilization—not just a cloud invoice versus a server purchase.
Performance, latency, or specialized hardware
Applications that exchange large volumes of data, require predictable response times, or rely on hardware acceleration may benefit from running nearer to users, equipment, or the data they process. AI workloads can be especially sensitive to the cost and performance of moving data between storage, training, and inference environments. Keeping a particular processing stage local may be more practical than relocating an entire AI stack.
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Regulated or sensitive information can bring requirements around access, auditability, processing controls, and where data is stored. A dedicated environment may make some controls or evidence easier to manage, while a public-cloud service may still meet the organization’s requirements. IDC reported in 2025 that 50% to 70% of cloud buyers across regions wanted control over data location and digital infrastructure; this describes buyer preference, not proof that every such workload must leave public cloud.
Operational control and resilience
Organizations may want more direct control over maintenance windows, backup policies, recovery design, or infrastructure changes. Local and dedicated environments can support those goals, but they also transfer more responsibility to the organization or its service provider. Staffing, patching, capacity planning, hardware replacement, and recovery testing remain necessary.
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Which workloads are more likely to move?
Repatriation is usually more plausible for a specific workload or component whose cost or requirements are poorly matched to its current location. IDC’s 2024 analysis associated dedicated cloud particularly with CRM, ERM, human-capital, and backup workloads. That is a reported placement pattern, not a universal recommendation: the right location depends on the organization’s architecture, contract, controls, and operating capacity.
- Backup and disaster recovery: A separate or locally controlled copy may support recovery objectives, provided it is isolated appropriately and recovery is tested.
- Data-heavy or steady-use processing: Consistent demand and substantial data movement can make dedicated infrastructure worth evaluating against ongoing public-cloud costs.
- Latency-sensitive or compute-intensive services: Applications with strict response-time needs or specialized hardware requirements may benefit from a closer or more predictable environment.
- AI lifecycle components: Data preparation, training, or inference may have different placement needs; moving only the relevant stages can avoid treating the whole lifecycle as one indivisible workload.
- Applications with sovereignty or compliance constraints: A local, private, or dedicated option may be considered when geography or control requirements materially shape the design.
How the main placement options compare
These models are not interchangeable, and “private cloud” can describe different architectures and service arrangements. The useful comparison is based on the workload’s requirements and the full cost and operating model.
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| Option | Potential fit | Trade-off to assess |
|---|---|---|
| Public cloud | Elastic or variable demand, managed services, and workloads that benefit from provider-scale infrastructure. | Consumption can be difficult to forecast; data transfer, licensing, and service dependencies can affect total cost and portability. |
| Dedicated or private cloud | Workloads needing more isolation, control, or a defined infrastructure environment without necessarily operating a fully owned data center. | Compare the service and contract model with the control actually required; dedicated capacity may reduce flexibility and still entail provider dependence. |
| Colocation | Organizations seeking to place their own systems in a third-party facility, potentially closer to networks, users, or partners. | The organization retains responsibility for hardware and much of the platform operation; facility, connectivity, staffing, and procurement costs matter. |
| On-premises | Workloads for which direct control, local integration, or specific operating requirements justify running infrastructure at the organization’s site. | Facilities, power, hardware lifecycle, specialist staffing, capacity utilization, and disaster-recovery arrangements all affect cost and resilience. |
How to decide whether repatriation makes sense
- Define the workload boundary. Identify the application components, data, dependencies, and users in scope. Decide whether the candidate is a whole application, a database, backup data, or one stage of processing.
- Set measurable requirements. Record expected demand, utilization, latency, recovery objectives, security controls, compliance obligations, and any data-location rules. Distinguish mandatory requirements from preferences.
- Model five-year total cost. Compare current cloud costs with the proposed destination, including egress and migration, licensing, facilities, hardware, connectivity, staffing, support, and expected utilization. Include transition and parallel-running periods rather than assuming an instant move.
- Test operational readiness. Determine who will patch, monitor, secure, scale, back up, and recover the system. Account for procurement lead times and whether the team has the skills to run it reliably.
- Check portability and reversibility. Identify provider-specific services, data formats, network dependencies, and contractual restrictions. Preserve a feasible exit or alternate-placement plan where the risk justifies it.
- Move in a controlled scope. For a justified candidate, use a staged migration with validation of performance, security, cost, and recovery before expanding the pattern to other workloads.
What to take away
IDC’s figures support a real but selective movement of workloads back from public cloud. The more useful question is not whether a company is “leaving the cloud,” but whether each workload’s cost, performance, governance, and operating requirements are better served by public cloud, dedicated or private infrastructure, colocation, or on-premises systems. Repatriation is one part of that placement strategy; continued cloud adoption is another.
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