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Arista vs. Vertiv: which AI Infrastructure business has the bigger profit cushion? Published October 17, 2026 at 6:11 pm EDT Arista and Vertiv rank tenth and ninth in our 10 Best AI Infrastructure Stocks to Buy for the Long Term. The list shows which eight infrastructure businesses attracted more hedge-fund holders. Both benefit when AI campuses become operational, but their profit cushions differ sharply. Arista Networks, Inc. (NYSE:ANET) supplies data-center networking; Vertiv Holdings Co (NYSE:VRT) supplies power and cooling. They are alternatives for an infrastructure allocation, with different products and economics. High margins change the cost of disappointment. Arista's August 4 results showed second-quarter revenue of $3.036 billion, up 37.7%, and a 45.4% GAAP operating margin. Vertiv's July 29 release reported $3.274 billion of sales, up 24%, and a 19.5% GAAP operating margin. Vertiv's organic growth was 18%; acquisitions and currency contributed the rest. Subtract five percentage points from each margin while holding revenue constant. Arista's operating profit falls 11.0%; Vertiv's falls 25.6%. This isolates sensitivity to margin compression. It does not assume that networking and thermal equipment face identical shocks: customer pricing, manufacturing costs and project timing can affect them differently. Arista's software-supported networking model currently retains more operating profit per sales dollar. That cushion can help absorb investment and price pressure, but large-customer concentration makes order timing important. Our Cisco comparison asks how much of Arista's stronger economics investors are already paying for. Cooling growth has to become recurring cash. Vertiv's lower margin also gives it room to improve as volume rises and operations become more efficient. Greater computing density increases cooling and power requirements. Its risk is that customers delay campuses or expansion costs arrive before revenue. Q2 adjusted free cash flow reached $925 million, with favorable working-capital timing helping the result; extrapolating that quarter would overstate certainty. October 15 trailing cash-flow multiples were about 45 for Arista and 28 for Vertiv. Arista's larger margin cushion therefore comes with a higher cash-flow price. That premium needs durable growth and collections, while Vertiv must preserve cash generation after investment. These current multiples do not establish comparable future shareholder returns. Insider Monkey counted 91 Arista holders in Q2 2026, up from 85, and 112 Vertiv holders, up from 96. Arrowstreet cut Arista shares 10%, while AQR increased Vertiv shares 1%. September 30 short interest was 0.97% of float for Arista; Vertiv had 14.38 million shares sold short. Those snapshots cannot explain later trading moves. Arista has the larger demonstrated margin buffer. Vertiv's operating upside depends more heavily on improving execution as demand expands. The choice turns on whether Arista sustains its economics or Vertiv closes part of the profit gap while converting growth into cash. Today's margin difference answers the resilience question, not which stock will deliver the higher return.

Vertiv helps enterprises move from AI pilots to production. VERTIV Holdings Co. (NYSE: VRT), a global leader in critical digital infrastructure, and Deloitte Center for Integrated Research introduced a collaboration to help enterprises address a critical challenge in moving artificial intelligence from experimentation to production: determining where AI workloads should run and whether the available physical infrastructure can support them. The new Vertiv Designing Infrastructure for Enterprise AI Guide, developed by Vertiv and STL Partners, a digital infrastructure research firm, provides business, IT and facilities leaders with a workload-based framework for making those decisions before committing to an infrastructure plan. Starting with the intended business outcome and AI workload, organizations can evaluate deployment location, infrastructure requirements and facility readiness, then align each workload with an appropriate physical infrastructure model and reference design for further planning and technical validation. The underlying research highlights a critical consideration as enterprises move AI workloads into sustained production: infrastructure readiness can determine where workloads can be deployed and how quickly they can move into production. Available power, cooling, connectivity and operational capacity can determine whether a workload can be supported in an existing facility or requires a different deployment model. The guide translates these considerations into a four-step process: (1) define the intended business outcome and AI workload, (2) evaluate the workload's scale, performance and operating requirements, (3) determine the most appropriate deployment location and (4) match the workload and location to a physical infrastructure model and reference design. "The next phase of enterprise AI will be shaped not only by models and computing, but also by whether organizations can provide the power, cooling, connectivity and operating environment each workload requires," said Giordano Albertazzi, CEO of Vertiv. "By starting with the workload, enterprises can make infrastructure decisions earlier, identify constraints sooner and build a more credible path from AI ambition to production." "The economics and operating requirements of AI change as workloads move into production," said Nicholas Merizzi, principal, Silicon2Service U.S. Lead, Deloitte Consulting LLP. "Organizations need to consider business value, governance, operating ownership and infrastructure together. Bringing those questions into the planning process earlier gives leaders a stronger basis for deciding where AI workloads should run and how they should be supported over time." The process recognizes that enterprise AI is a portfolio of workloads rather than a single infrastructure decision. Exploratory workloads may remain in the cloud, while sustained or business-critical workloads may favor hybrid infrastructure or capacity managed by the enterprise. Workloads requiring faster response times, greater control, data proximity or specific model performance may need to run at the edge or in another controlled environment. The workload-specific approach allows public cloud, colocation, enterprise-owned capacity, hybrid environments and edge infrastructure to each play a role. Rather than advocating broadly for or against the cloud, the guide provides a consistent method for determining the appropriate operating environment for each AI workload. The research categorizes enterprise AI workloads into six archetypes covering model development, retraining and four categories of production use. These archetypes help organizations identify workloads with similar performance, operating and infrastructure requirements, creating a more consistent basis for planning. Each workload can then be aligned with one of four physical infrastructure models: Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster. The four models provide a starting point for decisions involving power, cooling, controls, connectivity, resilience, operational support and future growth. Corresponding reference designs can then be adapted based on site assessment, workload requirements and detailed engineering. Facility readiness is critical across all four models. The guide helps enterprises evaluate whether a proposed location has sufficient power, cooling, connectivity, operational capacity and room for growth. Conducting this assessment early can help identify infrastructure constraints before they become barriers to deployment. The full guide, Designing Infrastructure for Enterprise AI, details the six workload archetypes, four physical infrastructure models and a four-phase roadmap for planning, deploying and evolving enterprise AI infrastructure. It is available at Vertiv.com.ant challenges facing today's data centers, communication networks and commercial and industrial facilities with a portfolio of power, cooling and IT infrastructure solutions and services that extends from the cloud to the edge of the network. Headquartered in Westerville, Ohio, USA, Vertiv does business in more than 130 countries. For more information, and for the latest news and content from Vertiv, visit Vertiv.com. Forward-looking statements This release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27 of the Securities Act, and Section 21E of the Securities Exchange Act. These statements are only a prediction. Actual events or results may differ materially from those in the forward-looking statements set forth herein. Readers are referred to Vertiv's filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10-K and any subsequent Quarterly Reports on Form 10-Q for a discussion of these and other important risk factors concerning Vertiv and its operations. Vertiv is under no obligation to, and expressly disclaims any obligation to, update or alter its forward-looking statements, whether as a result of new information, future events or otherwise. For media inquiries, please contact: Ruder Finn for Vertiv E: [email protected]

Vertiv (VRT) has forecast third-quarter 2026 sales of $3.75 billion at the midpoint, up 40% from a year earlier. The quarter has ended, but the company has not yet reported results. The forecast marks a sharp acceleration from 24% growth in the second quarter, when large-project delays pushed some sales out. Executives cite faster project progress, new capacity in Johor, Malaysia, and five plant expansions in the Americas, plus strong backlog coverage. Vertiv also raised its 2026 sales forecast by $250 million, to a $14 billion midpoint. Its stock trades at 52.7 times earnings, against 22.5 for the S&P 500, and has fallen 22% in three months. The key question is whether Vertiv can deliver its large projects on time.