There is no universal winner in the data center vs. cloud decision. The six IT experts featured here favor choosing a home for each workload based on its costs, technical requirements, risk, and strategic value—not adopting a blanket cloud-first or data-center-first rule. For many organizations, the practical answer is a mix of public cloud, on-premises infrastructure, colocation, and dedicated as-a-service options.
What the six experts say about choosing where workloads belong
The six viewpoints were published on 3 May 2018. They remain useful as decision principles, but their comments should be read alongside more recent coverage of hybrid infrastructure and repatriation—not as current pricing or product guidance.
Jason Waxman: divide the portfolio by workload
Jason Waxman, then Intel corporate vice president and general manager of Data Center Solutions, said that CIOs typically have a strategy for what goes to public cloud and what stays on-premises. The implication is to assess applications individually rather than assume every workload should move in the same direction.
Ron Batra: start with the business and the applications
Ron Batra, an executive architect at ServiceNow, advised organizations to look at both the balance sheet and the business strategy. He said to begin with the requirements of homegrown and third-party applications, rather than starting with a comparison between Azure and another provider. He also noted that cloud providers may offer application-tier innovation, security, and engineering capabilities that an enterprise IT team cannot deliver alone.
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Rick Drescher: align operations, technology, and finance
Rick Drescher, managing director of Critical Facilities Group at Savills Studley, emphasized aligning operational, technical, and financial goals across on-premises, cloud, colocation, or a combination. He warned that network connectivity can add costs that are absent from an on-premises baseline. Organizations whose core business is not operating data centers should consider whether colocation or public cloud is a better fit than owning facilities.
Jonathan Levine: treat utilization as a clue, not a pricing law
Jonathan Levine, then CTO of Intermedia, offered a rule of thumb: a burstable application running fewer than about 60 hours a week may be better suited to cloud, while an application running at a steady CPU load may cost less in-house if the organization already has a data center or colocation arrangement. This is not a universal break-even point. Actual costs depend on the workload, cloud rates and usage pattern, connectivity, and the infrastructure already in place.
Brian Green: price failure and preserve room to grow
Brian Green, then VP and Global Lead of Data Center Engineering & Operations at Visa, said organizations need flexibility because they cannot know what will happen five to ten years ahead. He recommended understanding the real business cost of failure, comparing it with the cost of resilience, and connecting infrastructure metrics to business value.
Mark Thiele: do not confuse ownership with the outcome
Mark Thiele, then CIO and CSO at Apcera, cautioned against becoming attached to the idea that an organization must own a data center. Infrastructure is a means to provide the services the business needs; ownership by itself is not the objective.
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Compare the workload on the factors that change the answer
On-premises and cloud are not fixed packages with a single winner. The right placement depends on the application’s operating pattern, its dependencies, the consequences of failure, and the organization’s ability to run the environment.
Total cost of ownership
Compare the full cost over the period you expect to operate the workload. For on-premises infrastructure, include servers, power, cooling, space, maintenance, staffing, refresh cycles, and resilience. For cloud, include consumption, storage, network connectivity and data transfer, licensing, support, migration, and the engineering needed to configure and govern the service.
Dedicated capacity may be economical when use is steady and predictable, especially if facilities and infrastructure are already available. Cloud elasticity can avoid buying capacity for brief peaks or uncertain demand, but variable usage and networking can erode expected savings. A fair comparison models realistic workload patterns and the costs of operating each option, rather than comparing a cloud invoice with only the purchase price of a server.
Performance, latency, and data movement
Locally hosted systems can suit applications that depend on low-latency access to nearby users, equipment, or data. Cloud can provide broad capacity, but application performance depends on network paths and the way services are connected. Moving large volumes of data can also introduce transfer costs and operational complexity. Map application dependencies and data flows before choosing a destination.
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Security, privacy, and compliance
On-premises deployment gives an organization direct physical and administrative control, but the organization also carries the responsibility for securing facilities, hardware, software, identities, and operations. Cloud providers offer security tools and specialist expertise, but customers still need to configure identity, data, and network policies correctly.
Neither location makes a workload secure or compliant by default. For regulated or sovereignty-sensitive data, determine which placement, contractual terms, and controls meet the applicable requirements. In a mixed environment, governance and ownership need to be clear across distributed data. StateScoop’s 4 December 2024 public-sector coverage quoted Mississippi CIO Craig Orgeron on the importance of sensitivity and compliance requirements, and GDIT’s Bo Reese on governance and ownership. AWS’s Morgan Reed stressed the need for consistent controls across on-premises and cloud environments.
Scale and demand variability
On-premises capacity requires buying, installing, and integrating equipment ahead of need. Cloud resources can generally be provisioned and scaled more quickly, which can help with peaks or uncertain growth. The trade-off is that elastic capacity needs active cost and resource management: resources left running or oversized can turn flexibility into recurring spend.
Operations, skills, and resilience
Operating a data center requires facilities, hardware, networking, systems, database, and security capabilities. Cloud reduces the facilities burden but calls for cloud architecture, identity management, automation, cost management, and vendor-management skills. Whichever model you choose, resilience depends on design, governance, and tested recovery—not on the name of the environment. Model the business impact of downtime and compare it with the cost of redundancy and recovery.
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When hybrid, colocation, or dedicated services make sense
A choice between owned infrastructure and public cloud is not exhaustive. Colocation can provide access to a facility without requiring the organization to operate its own building. Private or dedicated environments delivered as a service can offer cloud-like management while keeping infrastructure dedicated. Edge services, content delivery networks, and specialized GPU clouds can also address needs that do not fit a simple all-on-premises or all-public-cloud plan.
CIO’s 22 July 2024 coverage describes hybrid as a common destination and emphasizes workload-specific placement, FinOps optimization, and selective movement. It names HPE GreenLake and Dell APEX Private Cloud as dedicated-environment options, and Cloudflare and GPU-cloud services as alternatives that can complement public cloud. These are examples of categories and providers, not a claim that any one service is right for a particular workload.
For public-sector readers, StateScoop reported an AWS-supported example in which a state saved $10 million by consolidating 88 server rooms. That is a named case reported in 2024, not a typical savings estimate or a forecast for other migrations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you repatriate workloads from the cloud?
Repatriation means moving a workload, or part of one, from public cloud to an on-premises or dedicated environment. It can be worth evaluating when costs, control, privacy, latency, or performance no longer meet expectations. But moving back can recreate the original problem and introduce migration work, downtime, refactoring, security obligations, and compliance tasks.
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CIO’s 22 July 2024 article, citing IDC repatriation research, reports that fewer than 10% of respondents said they had repatriated entire workloads. That supports treating repatriation as a selective workload decision, not a blanket reversal of cloud adoption.
TechTarget’s 28 August 2023 coverage reports that an IDC survey conducted in 2022 found 71% of respondents expected to move public-cloud workloads partly or fully into a dedicated IT environment by the end of 2023. This is historical survey evidence about expectations at that time—not a measure of what organizations did, nor a forecast for 2026. TechTarget also identifies potential repatriation drivers such as cost, security and compliance, supplier failure, latency, data gravity, and poorly planned migrations.
Evaluate the workload before moving it
- Identify the specific cost, performance, control, or compliance problem you want to solve, and confirm it is caused by the current placement.
- Compare the full cost of the current environment with the destination, including migration, connectivity, staffing, and ongoing operations.
- Map dependencies, data movement, and recovery requirements so the move does not undermine adjacent systems.
- Check whether the organization has the skills and controls to operate the destination securely and reliably.
- Move selectively and validate the outcome against the business measures that justified the change.
A practical placement process
- Inventory applications and dependencies. Record workload patterns, data sensitivity, user and system dependencies, performance needs, and current failure risks.
- Define business requirements. Set the required performance, availability, recovery, compliance, growth, and cost outcomes before comparing environments.
- Model total cost under realistic demand. Include facility and staffing costs, cloud consumption, connectivity, data movement, migration, licensing, and resilience.
- Compare feasible destinations. Evaluate public cloud, existing on-premises capacity, colocation, dedicated as-a-service, and hybrid designs where relevant.
- Assign operational ownership. Decide who manages identity, data, network, patching, monitoring, recovery, compliance evidence, and spending in each environment.
- Reassess as conditions change. Review placement when demand, business strategy, application architecture, costs, or requirements change—not just when a provider contract comes up for renewal.
What the evidence does—and does not—establish
TechTarget’s 19 January 2024 comparison describes on-premises strengths including infrastructure control, upfront cost visibility, potential long-run savings for predictable use, low latency, and access without an internet connection. It also identifies reliability, scalability, ownership cost, maintenance, security burden, and specialist skills as liabilities. Its cloud comparison notes that managed services can abstract hardware administration and provide elastic capacity and security tooling, while configuration and governance remain necessary.
Taken together, the expert views and later comparisons support workload-by-workload placement, but they do not establish a universal cost threshold, security winner, or migration outcome. Those depend on the organization’s workload and operating conditions. The useful decision is not simply “cloud or data center?” but where each workload can meet its requirements at an acceptable cost and risk.
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