There is no single infrastructure model that fits every AI workload. CIOs and IT leaders should weigh cloud’s cost and scale against the value it enables, the control it allows, data-location rules, security needs, energy use, and whether some work belongs closer to users or devices. For many organizations, the practical answer will combine public cloud, on-premises systems, and edge computing.
1. Compare cloud spend with the value it enables
Cloud cost is only one part of the decision. Assess whether the service lets your teams operate effectively today and supports the scale and capabilities your AI strategy may require next. A lower bill can be a poor result if it limits useful workloads or slows development; a higher bill needs a clear connection to operational or strategic value.
Questions to ask
- Which workloads and capabilities does the spend make possible, and how important are they to the organization?
- Can costs be understood and managed as usage and AI ambitions grow?
- What operational effort, capacity planning, or scaling constraints would another deployment model add?
Compare options using total cost and value, not a single cloud rate. Include the people and infrastructure required to run each approach, while avoiding forecasts that assume AI usage or demand will remain fixed.
2. Weigh built-in AI services against control and flexibility
Cloud platforms increasingly incorporate generative and agentic AI capabilities. Using services built into an existing platform may offer a convenient way to adopt AI, but the fit depends on how much control the organization needs over its technology stack and how comfortable it is with provider dependence.
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When platform integration may fit
Built-in services may suit teams that value integration and a direct route to trying or expanding AI capabilities. Evaluate how well the services meet the intended workload rather than treating availability as proof of suitability.
When greater control may matter
Organizations with specific requirements for technology choice or flexibility should consider how a provider’s services affect their ability to change components or providers later. Compare convenience and continuity with the possibility of dependency; the right balance depends on the organization’s priorities.
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3. Let sovereignty and compliance shape placement
Data-location rules and industry requirements can constrain where data is stored and where workloads run. The relevant obligations depend on geography and sector, so a multinational or regulated organization should map its applicable requirements before choosing an architecture. This is an infrastructure planning consideration, not jurisdiction-specific legal advice.
Regional or industry cloud offerings, on-premises systems, and hybrid designs are possible ways to address placement constraints. None is automatically the right answer: assess each against the actual rules, the data involved, and the workload’s operating needs.
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4. Assess security across the full workload
AI workloads may span cloud services, data centers, and endpoint devices. Evaluate the protections and responsibilities at each layer rather than assuming that a cloud or on-premises label establishes security. Relevant topics include distributed-workload protection, compliance, trust, and how the organization will monitor and respond to threats.
Security properties to evaluate
- Threat detection: Determine how AI-enhanced detection fits into monitoring and response, and what evidence is available about its effectiveness for your environment.
- Confidential computing: Assess whether protected processing is needed for the workload and whether a proposed service’s properties match that need.
- Encrypted collaboration: If multiple parties must process or collaborate on sensitive data, establish what encryption protections are required during processing and whether the proposed approach meets them.
- Compliance and trust: Validate specific controls and obligations against your requirements. The availability of a security feature does not by itself prove that a service satisfies a particular standard or use case.
5. Include energy and sustainability in the comparison
Storing and processing data consumes energy, and AI demand may make energy cost and availability more consequential. Include energy and sustainability in infrastructure reviews alongside cost, performance, control, and compliance. The available evidence does not establish that cloud, on-premises, or edge is inherently greener, so compare the relevant workloads and locations rather than assuming one deployment model has lower impact.
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6. Decide whether workloads belong at the edge
Edge computing brings selected processing closer to where data is created or used. AI PCs and other endpoint devices may be candidates for particular functions, and local execution can offer potential modularity or security advantages. Enterprise use cases are still developing, however, so an AI PC is an option to assess—not a default destination for AI workloads.
Questions for an edge candidate
- Does the task need to run on or near the device, rather than in a central cloud or data center?
- Can the endpoint handle the function while meeting the organization’s performance, security, and operational requirements?
- How will the task fit into the wider system if other data or processing remains in cloud or on-premises infrastructure?
Compare deployment models against the same criteria
Public cloud, on-premises infrastructure, and edge are not mutually exclusive choices. Use the same decision criteria for each candidate workload, then choose a placement or combination that meets the organization’s needs.
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| Decision axis | What to compare |
|---|---|
| Total cost and value | What the workload costs to operate, what capability or growth that spend enables, and how the cost may change with use. |
| Performance and scale | Whether the deployment can meet workload needs now and accommodate expected growth. |
| Data location and compliance | Where data and processing may reside under the organization’s geographic and industry requirements. |
| Security and control | Required protections, operational responsibilities, and the degree of control over the technology stack. |
| Sustainability and energy | Energy use and sustainability implications for the relevant workload and location. |
| Vendor dependence and flexibility | How platform choices affect continuity, portability, and the ability to change services or providers. |
Matt Egan’s CIO feature captures the central trade-off: “There is no perfect solution for all organizations.” The decision is therefore best made workload by workload, with a broader architecture that can use cloud, on-premises, and edge resources where each is appropriate.
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