Questions to ask when evaluating AI-ready data center providers

The new reality of AI data center selection

Picking an AI data center provider is no longer mostly a technical procurement decision with sustainability considerations tacked on later. These decisions now require risk assessment that spans GPU performance, power and cooling capacity, environmental impact, regulatory pressures and community opposition.

The most expensive GPU in the world creates no value while waiting for the rest of the system to catch up.

At the same time, AI-ready marketing claims are outpacing the infrastructure behind them. Providers might present planned capacity as if it already exists, while downplaying environmental concerns and local opposition. Assessing these providers means moving beyond broad claims to determine if the infrastructure is already operational, what still depends on future approvals or upgrades, and where long-term risks exist.

Businesses that ask the right questions early protect more than just technology investments. They're also reducing the risk of future expansion delays, operational disruptions and reputational issues associated with controversial infrastructure projects. Ultimately, the goal isn't simply to secure access to GPUs but to ensure the surrounding infrastructure is available when needed and can support those needs efficiently and at scale.

A clear sign of infrastructure problems is idle hardware rather than system failures, said Satyam Dhar, a software engineer at Galileo, an AI evaluation and observability platform company owned by Cisco. "The most expensive GPU in the world creates no value while waiting for the rest of the system to catch up."

In many cases, the success of an AI strategy depends as much on having the right information about a data center's power, cooling, networking, planned infrastructure and provider transparency as on the AI technology itself.

Technical questions to ask data center vendors

Technical qualification is the first step when establishing whether a facility can physically support AI workloads at the required scale. Technical questions to ask include the following:

1. Power density and capacity planning

Power is often the biggest factor in determining whether AI infrastructure can successfully scale. One of the first questions enterprise executives should ask data center vendors is how much rack power density its facilities support and whether it aligns with their company's current and future AI workload requirements.

Other important considerations include how additional capacity will be provided for over time and whether expansion depends on utility approvals, transmission upgrades or infrastructure projects still in development.

"Providers should be clear about what's secured and what's still planned, rather than presenting future capacity as existing capacity," said Sam V. Tabar, CEO of WhiteFiber, an AI infrastructure provider that offers GPU cloud services and AI-focused data center capacity.

Businesses should also examine whether the facility's energy mix relies on renewables, natural gas, coal, nuclear or a combination of sources. The mix can affect both operational resilience and sustainability goals. For example, facilities that rely on a diverse set of energy sources or have backup generation strategies might be better positioned to maintain operations during grid disruptions or periods of energy constraints.

Sustainability claims should be backed by independent verification; that's a standard that applies across all vendor conversations, not just power-specific ones.

2. Cooling architecture

Traditional air-cooling systems were designed for lower-density CPU workloads and aren't sufficient for modern GPU deployments. As AI rack densities increase, operators are turning to liquid cooling to sustain performance and manage heat.

The power side of the data center has decades of layered defense in depth. The cooling side often has one layer.

Liquid cooling is no longer a premium feature, WhiteFiber's Tabar said. "Liquid cooling should be viewed as a baseline requirement, not an upgrade option for modern GPU clusters," he said.

Businesses should look beyond whether liquid cooling exists and evaluate how resilient the cooling architecture remains under sustained load, Flex's Morton said. The greatest operational risk in many AI environments isn't the power infrastructure but the cooling infrastructure, especially the coolant distribution systems and secondary fluid networks that serve multiple racks simultaneously.

Electrical systems are typically built with multiple layers of redundancy, but cooling systems often have fewer backup mechanisms, Morton explained. "The power side of the data center has decades of layered defense in depth," he said. "The cooling side often has one layer."

Power failures are often localized and recoverable, Morton added, whereas cooling failures can affect multiple racks and GPU clusters simultaneously, potentially increasing downtime and recovery times. Organizations should evaluate how providers design redundancy, maintenance protocols and failure response scenarios into their cooling systems.

Beyond system reliability, resource inputs are also becoming a consideration. Facilities that rely heavily on freshwater for cooling might face greater exposure to drought conditions, water restrictions and regulatory scrutiny, making water use an important factor in site selection.

3. Network and interconnect performance

AI training workloads are far more sensitive to network delays than traditional enterprise applications. Businesses should evaluate how the data center manages latency in distributed AI environments, what high-bandwidth networking options are available and how communication among GPU nodes is optimized.

Such limitations usually become most noticeable when organizations scale up training across large GPU clusters, Galileo's Dhar said. During this stage, networking bottlenecks can lead to slower training cycles, uneven data processing speeds or situations where adding more GPUs no longer delivers expected performance improvements, he said.

Businesses using hybrid or multi-cloud environments must also assess the availability and performance of cloud connectivity options that enable fast, reliable data movement across platforms.

Many of the networking and latency challenges within individual AI data centers are being addressed through AI-specific networking architectures, Morton said. The bigger challenge now is shifting to coordinate across multiple facilities as businesses scale beyond single-site deployments.

"The AI factory of the next decade isn't a single building; it's a fabric of buildings," Morton said.

As a result, the focus is shifting from individual rack- or facility-level performance toward intercampus connectivity and distributed architectures capable of supporting large-scale AI training environments.

4. Scalability and deployment risk

Businesses should assess how fast a provider can bring on additional capacity online and what factors could delay expansion. Future growth often depends on permits, zoning decisions, utility approvals and infrastructure upgrades that are still in progress. When these factors are in play, capacity projections should be treated as contingent rather than guaranteed.

The industry still designs every data center as a custom project.

These dependencies aren't unusual, but rather they reflect the long timelines required for grid expansion, including transmission upgrades and transformer availability, according to Brad Johnson, director of electric utilities at Bentley Systems, an infrastructure engineering software company.

Permit complexity is increasing as AI infrastructure evolves faster than existing regulatory frameworks, particularly for emerging technologies, such as high-density cooling systems and alternative power architectures, Morton said. External infrastructure constraints are only part of the challenge, and many deployment delays stem from a more structural issue in how data centers are designed.

"The industry still designs every data center as a custom project," Morton said. This approach requires each facility to be individually engineered, built and commissioned, which limits the ability to scale capacity quickly even when demand is strong, he added.

As a result, business leaders evaluating providers should look at whether deployments use standardized, repeatable designs or highly customized builds. More standardized infrastructure can reduce risk and make timelines more predictable.

Sustainability questions to ask data center vendors

Power, cooling and networking tend to dominate AI infrastructure discussions. However, some of the most material long-term risks are less visible at the procurement stage. Energy availability, water access and environmental constraints can directly affect a facility's ability to scale.

Many of the infrastructure and sustainability constraints … are often not analyzed holistically by enterprise buyers.

These risks aren't hidden, but they often require looking beyond a vendor's marketing materials, Bentley System's Johnson said. "Many of the infrastructure and sustainability constraints are visible in public utility filings and regulatory processes but are often not analyzed holistically by enterprise buyers," he said.

Key infrastructure and sustainability considerations for enterprises evaluating AI data center vendors include the following:

1. Utility and power stability

Basic power redundancy is a baseline expectation for data centers supporting AI workloads. The more important question is whether the surrounding grid can keep up with rising AI-driven energy demand.

"Many of today's bottlenecks stem from grid physics, transmission limits, transformer availability, material shortages and engineering complexity," Johnson said.

Energy availability has already become one of the most significant constraints on AI infrastructure growth, Morton said. AI operators are increasingly exploring alternatives, such as behind-the-meter generation, fuel cells and other forms of dedicated power infrastructure to reduce dependence on utility timelines and improve deployment certainty.

For enterprises, this means evaluating not only a provider's current power capacity but also how future growth will be supported if regional grid expansion fails to keep pace with AI demand.

It's also worth examining how a provider's energy mix aligns with enterprise sustainability goals. Facilities that provide independently verified energy and emissions data typically offer greater transparency and long-term reliability than those relying mainly on renewable energy credits while still operating on fossil-fuel-heavy grids.

2. Water availability and cooling sustainability

Water consumption is becoming one of the most important and often overlooked risks in AI infrastructure. A typical data center can use an enormous amount of water per day for cooling, exposing it to drought restrictions, regulatory scrutiny and community opposition.

At scale, the numbers are significant: Enterprise data centers consume an average of 300,000 to 500,000 gallons of water per day, while large hyperscale facilities can draw between 1 million and 5 million gallons, comparable to the water needs of a small town. A 2025 International Energy Agency study found that a typical 100 MW U.S. data center can require up to 2 million liters of water per day when accounting for both on-site cooling and electricity generation.

Looking ahead, MSCI's analysis of roughly 14,000 global data center sites projected that about one in four could face increasing water scarcity risks by 2050.

As a result, the design of cooling systems is increasingly important. Liquid cooling, especially in closed-loop configurations, can significantly reduce water consumption compared with more traditional cooling approaches while also supporting higher-density AI workloads, WhiteFiber's Tabar noted.

Businesses should evaluate how dependent a facility is on local water supplies, how much water its cooling systems use at full capacity and whether drought conditions or water restrictions could affect long-term operations or trigger regulatory scrutiny.

3. Environmental constraints and the transparency gap

An initial step in the evaluation process is to assess whether a formal environmental impact assessment has been completed for the existing facility and planned expansions. While the absence of one isn't necessarily a red flag, it can limit visibility into longer-term environmental risks, including emissions and water- and land-use constraints that could affect future operations.

Transparency is another key issue. Businesses should check whether sustainability and emissions data have been independently verified, as standards vary across providers.

These transparency challenges extend beyond individual data center providers to the broader AI ecosystem. Many tech companies emphasize renewable energy credits and clean energy investments, even as overall emissions continue to rise.

A 2025 report by the International Telecommunication Union found that indirect emissions from major AI-focused technology companies, including Amazon, Microsoft, Alphabet and Meta, increased by an average of 150% between 2020 and 2023, driven by rapid AI infrastructure expansion. The issue highlights a broader transparency challenge: Businesses need clearer ways to evaluate the full environmental impact of AI systems, including the infrastructure required to operate them.

That gap is often reflected in how providers frame their own environmental performance. The issue isn't measurement itself, WhiteFiber's Tabar said, but how it's communicated externally. "What matters is that communities understand the actual resource footprint of these projects rather than relying on broad sustainability messaging," he said.

It's also important to consider how emerging environmental regulations could affect costs and compliance requirements. New disclosure rules on energy and water use are being introduced in many regions.

4. Long-term infrastructure durability

Businesses should evaluate whether a facility can realistically support AI demand over the next five to ten years. That means understanding how much of the provider's future capacity plans depend on infrastructure projects that aren't yet in place.

Enterprises should distinguish between secured capacity and announced capacity, Morton said. "A large portion of announced AI infrastructure capacity remains dependent on permits, utility approvals and transmission upgrades," he said.

The key issues are whether providers can identify which capacity is already available and which remains contingent on external approvals, and how they would respond if critical infrastructure projects are delayed.

Questions to ask about political and community risks

AI data center expansion is increasingly becoming a public policy issue. Some local governments have introduced stricter zoning rules, development moratoriums and longer environmental review processes that can significantly delay projects and cause financial disruptions.

Operators who treat [opposition] as a permitting problem, rather than a legitimate community concern, tend to make it worse.

For enterprises, these issues are no longer abstract policy debates; they're operational and reputational risks. Businesses that align their AI strategies with specific infrastructure providers can become indirectly exposed to the controversies surrounding those projects.

Companies looking for a data center provider must discern if a facility or planned development has faced community opposition related to land use, power consumption, water use, noise or other operational effects, and how those concerns were or are being handled. A provider's response to these challenges can offer insight into its operational maturity and ability to sustain long-term expansion.

"Operators who treat [opposition] as a permitting problem, rather than a legitimate community concern, tend to make it worse," Bentley System's Johnson said. "Many concerns are legitimate, and the industry is going to have to find solutions. Transparency and education are critical to helping projects move smoothly."

Businesses should also evaluate whether a provider's planned expansions depend on unresolved permitting decisions, regulatory approvals or local infrastructure reviews. Delays in these processes can affect project timelines and reveal whether providers have effective strategies for managing community and government relationships.

Businesses should ask how providers respond when community concerns, permitting challenges or regulatory reviews delay planned expansions. A provider's ability to communicate risks, engage stakeholders and adjust its plans can indicate how prepared it is to support long-term infrastructure growth.

It's also worth looking at whether past disputes or local pushback have affected permitting timelines, expansion plans or relationships with utilities.

Kinza Yasar is a technical writer for Informa TechTarget's AI and Emerging Tech group and has a background in computer networking.