5 Fast Facts: Vigilent’s AI for Cooling Plant Optimization

We sat down with Dr. Cliff Federspiel, founder, president & CTO at Vigilent, to talk about compute efficiency in the AI era—specifically, how smarter cooling optimization can keep SLAs tight as rack densities rise. From sensing to predictive control, Cliff explains why letting an on-prem AI continuously tune the cooling plant beats static setpoints, helping operators protect reliability while cutting energy and carbon.

The company builds an on-prem, vendor-agnostic AI control layer for data-center cooling. A sensor network and machine learning generate an Influence Map® of how each AHU affects rack inlet temperatures; the system then adjusts fans and unit states only where needed. Vigilent integrates with BMS/DCIM and also monitors and controls chillers, which allows for optimization across the entire cooling plant. Guardrails and fail-safes—including snap-to-full-cooling—ensure resilience while delivering measurable efficiency gains.

Q1: Vigilent has been applying AI/ML to cooling optimization for 17 years—well before today’s wave of AI-driven data center demand. How has this early market entry shaped your leadership position as operators now face power densities and cooling challenges beyond the limits of traditional air cooling?

A1: Data centers are highly variable in how they’re configured and operated. There are different cooling technologies from different vendors. Control strategies also vary. For example, temperature vs. pressure. Facility layouts are different, for example, raised floors vs. slab and varying types of containment. As you say, Vigilent has been around for a long time, and that means we’ve seen all of these configurations and learned how to optimize cooling in each case. Years ago, we scratched our heads when seeing something new, but by now we’ve pretty much seen it all, including the complexities associated with optimizing across the air-side and chiller plant.

Increasing IT power densities just add another layer of complexity to the cooling challenge, and we’ve got the experience and gray hairs, and software and engineering talent, needed to deal with it.

Q2: Most traditional cooling systems rely on static setpoints, while Vigilent uses machine learning to understand the influence of each cooling unit on IT equipment across a facility. How does this dynamic approach transform cooling efficiency compared to conventional methods, and what measurable outcomes—like SLA compliance, energy use, or equipment longevity—can operators expect?

A2: Machine learning enables Vigilent’s AI to empirically understand exactly what’s going on in a data center. If a fan is ramped up or down, or a cooling unit is turned on or off, where will temperatures go up, and where will they go down? Machine learning allows the AI to create a predictive model, basically knowing in advance the effects of any actions it takes. This enables the AI to deliver very high SLA compliance with a bunch of other benefits. One colocation operator went from about 94% SLA compliance to 99.96% compliance. In the same data hall, this operator reduced PUE by 10% and energy and carbon emissions by 32%. Since cooling is only used when and where it’s needed, there has been a big reduction in wear and tear, which means their cooling infrastructure will last longer and there are fewer replacement parts. They are now using the AI as a competitive differentiator vs. other colocation operators.

Q3: With over 1,000 deployments across 35 countries and partnerships with companies like Schneider Electric, NTT, and Siemens, how does Vigilent’s global footprint enhance your AI models, and what unique advantages does this breadth of operational data provide to new customers, particularly those entering high-density AI computing environments?

A3: To address the diversity in data center designs and operations, Vigilent has developed platform capabilities that complement our core AI technology. We integrate with any type of cooling infrastructure, whether it be air cooling, liquid cooling, or the chiller plant, and also with BMS and DCIM systems plus other assets like power equipment. We also rapidly deliver bespoke capabilities with a tool called Vigilent Studio. And we have an information layer in our platform called Vigilent Insights, which uses the data captured and generated by Vigilent’s AI to provide facility staff with guidance about how to improve resilience or operate more efficiently.

Our strategic partners are global leaders in providing infrastructure and services to data center operators. This has positioned us well for the increased densities we’re seeing now. For example, Schneider Electric acquired the liquid cooling company Motivair and collaborates with NVIDIA on designs for AI data centers.

Q4: As rack densities push past 100 kW and liquid or hybrid cooling strategies become mainstream, where do you see AI-driven optimization playing the biggest role? How can it accelerate both sustainability and carbon reduction goals for next-generation infrastructure?

A4: Joint optimization of the air-side and water-side of the cooling plant will be increasingly important as rack densities rise and liquid cooling is used to remove some, but in most cases, not all, of the IT heat load. Higher densities shorten ride-through times in the event of a cooling failure, and hybrid cooling increases the complexity of the cooling plant. There is plenty of research showing that AI-driven vehicles are safer and more fuel-efficient than human-driven vehicles. Similarly, AI-driven optimization not only improves the efficiency and sustainability of complex data centers, but it also improves their reliability.

Q5: For operators considering AI-driven cooling optimization, what are the top three factors they should prioritize in evaluating solutions? And where can readers learn more about Vigilent’s approach to dynamic cooling management?

A5: People can be understandably nervous about letting AI control data center cooling, just as people are nervous getting into a self-driving car. But with proper safeguards, they can ensure data center resilience and be rewarded with significant benefits. What are those safeguards?

• First, make sure the AI operates within the premises, within the corporate firewall. This will avoid security risks associated with cloud-based applications.

• Second, make sure the AI has guardrails that protect against hallucinations, and fail-safes that ensure full cooling if there is ever a problem with the AI.

• Third, make sure the AI is proven. Has it been deployed in other mission-critical environments? How many? What were the results?

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