Vertiv Holdings Co. (NYSE: VRT) has published a planning guide that links enterprise AI workloads to the power, cooling, connectivity and operating capacity needed to run them in production. For CIOs, IT leaders and facilities teams, the framework turns a broad cloud-versus-on-premises debate into a workload-level decision about where each AI service should run and what infrastructure it will require.
The guide, developed by Vertiv with STL Partners and introduced alongside Deloitte Center for Integrated Research, starts with the intended business outcome and the AI workload rather than a preferred hardware or hosting model. The company said that approach is meant to help organizations identify constraints before committing to an infrastructure plan and reduce the risk of overbuilding or under-provisioning capacity.
Workload Requirements Drive the Deployment Choice
Vertiv divides the planning process into four steps: define the business outcome and workload; assess its scale, performance and operating requirements; determine an appropriate location; and match the workload and location to a physical infrastructure model and reference design. The method recognizes that a company can operate more than one kind of AI workload and does not need a single deployment answer for all of them.
The company identifies six enterprise AI workload archetypes spanning model development, retraining and four categories of production use. It then maps those workloads to four physical infrastructure models: Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster. The guide does not prescribe a universal configuration; it offers reference points that enterprises can adapt through site assessment and detailed engineering.
That distinction matters because exploratory work, sustained production inference and business-critical services can impose materially different requirements. Vertiv said exploratory workloads may remain in the cloud, while workloads that need faster response times, greater control, data proximity or particular model performance may fit hybrid, enterprise-managed, edge or other controlled environments.
Facilities Capacity Becomes a Production Gate
The practical value for enterprise teams is a common way to connect application priorities with physical limits. Vertiv said available power, cooling, network connectivity, operational capacity and room for growth can determine whether an AI workload fits in an existing facility or requires a different deployment model. Assessing those elements early can surface limits before they delay a production rollout.
The framework also gives business, IT and facilities leaders a shared planning language. AI deployment decisions can involve operating ownership, governance, performance, resilience and the economics of a sustained service alongside rack-level and site-level requirements. Bringing those factors into the same planning sequence can help teams evaluate an AI initiative as an operating workload rather than only as a model or software project.
Why Right-Sizing Changes the AI Cost Equation
Vertiv positions the guide as a way to choose among public cloud, colocation, enterprise-owned capacity, hybrid environments and edge infrastructure according to each workload’s needs. The company said the resulting reference designs address power, cooling, controls, connectivity, resilience, operational support and future growth, giving enterprises a starting point for technical validation.
For decision makers, the benefit is earlier visibility into the trade-offs that can shape cost, deployment timing and operational responsibility. It does not replace capacity planning, security review or engineering validation, but it provides a structured path for making those activities workload-specific.
The broader competitive shift is away from legacy infrastructure planning that treats AI capacity as one centralized, one-size-fits-all build. As production AI becomes a continuous business service, enterprises will need to weigh physical constraints and operational ownership alongside model choice. Vertiv’s guide gives infrastructure teams a framework for doing that before a promising pilot becomes an expensive deployment bottleneck.