The artificial intelligence industry seems to be hitting a bit of a bottleneck. Hyperscale data centers — the massive, multi-billion-dollar facilities designed to train AI models and run global cloud platforms — take years to build, and place enormous strain on local power grids. That may be okay for tech giants like Google or Amazon, but it leaves smaller companies eager to participate in the AI boom without the capital, capacity or access needed to do so.
Instead, many are adapting an old infrastructure idea for a new era: modular data centers. Using onsite, shipping-container-style pods, these turnkey systems can be up and running in a matter of weeks or even days. And they can handle localized AI and high-performance compute workloads closer to where it’s actually needed rather than on distant campuses in rural regions.
What Are Modular Data Centers?
A modular data center is a prefabricated, self-contained unit — often housed in a customized enclosure that resembles a shipping container — that integrates all the necessary power, cooling and IT infrastructure in order to localize computing power closer to the origin of data creation.
As demand for AI compute accelerates and power constraints tighten, modular data centers are emerging as a more practical solution. “The bulk of any workload is happily sitting in 10- to 15-kilowatt racks, and that is either in your own data center or a rented colocation space or a hyperscaler,” Ian Jagger, who leads services marketing at Hewlett Packard Enterprise, told Built In. But generative AI in particular is pushing energy demands toward 150 kilowatts per rack — a jump that many traditional data centers, constrained by legacy cooling and rigid power infrastructure, simply cannot accommodate.
“Enterprise companies and hyperscalers alike are not sitting on their hands,” Jagger said. “Their models are migrating … into more locale-based, more boutique-type [deployments]. That’s where modular data centers come in.”
What Is a Modular Data Center?
A modular data center is a type of computing facility built from prefabricated components that are assembled at or near the site of deployment. Instead of being constructed entirely from the ground up, these units arrive with their core infrastructure already integrated, including server racks, power distribution, direct liquid-cooling systems and networking components, as well as use-case-specific configurations suited to the clients’ needs. These customizations are determined by how much compute power is desired, ideal rack density, layout design and cooling strategy for optimal performance specific to whatever applications it will run.
Many modular data centers are housed in shipping-container-like enclosures, often referred to as “pods,” which can be transported by truck to a customer site. Smaller formats, known as “micro data centers,” can be compact enough to fit in offices or closets. And then there are portable, military-grade modules, which are designed to be transported from one rugged location to another.
Once delivered, a modular data center can be installed as a standalone facility or plugged into an existing structure as an add on. Depending on the vendor and configuration, pods may be fully operational within a couple of days. In these cases, the equipment inside is typically preassembled and pre-cabled, allowing users to deploy them quickly, while additional infrastructure — such as expanded power management or backup systems — can be integrated over time to create a fully operational data center.
Hyperscale vs. Modular
Hyperscale data centers, such as those operated by companies like Google or Amazon, are massive, resource-intensive facilities designed for high-density workloads and long-term scalability. Equipped with tens of thousands of GPUs, they continuously process and move enormous volumes of data, dynamically optimizing compute, storage and network resources at a global scale. These days, some hyperscale facilities exceed a gigawatt of total capacity, and are the beating heart of the AI industry, where the most advanced AI models are trained and the large-scale inference workloads behind tools people use every day are run.
Modular data centers, meanwhile, operate on a very different scale. Individual modules typically support about 50-to-200 kilowatts of capacity, and are best known for how quickly they can be deployed. Instead of going the traditional route of finding land, obtaining building permits and waiting behind red tape to be greenlit for grid connection, modular data centers are shipped directly to where power connectivity already exists, then integrated on-site. They’re ideal for edge computing, data repatriation, temporary projects or other work in regions where traditional data center construction is impractical.
“We are building these massive data centers to reflect the current AI reality, and companies are hedging on the idea that we’ll continue to need massive amounts of GPUs and the power to run them,” Daniel Ward, an assistant professor and program coordinator of IT and cybersecurity at Southern New Hampshire University, told Built In. “But if any of our current realities change — maybe we don’t need GPUs in the future, or maybe we need 20-times as many — they will not be well-positioned to reconfigure those facilities in a timely manner to keep up with those changes.”
It’s sort of like how the contemporary hotel was built at Walt Disney World, Ward explained, where prefabricated rooms were constructed off-site and then slid into place within a larger frame. In the case of modular data centers, the core structure would provide power, cooling and connectivity, while the removable pods could be added or removed as needed. “We could build an A-frame in every town or city with appropriate power, cooling and connectivity, rather than centralizing resources in a handful of massive facilities in the middle of nowhere,” he said.
But at least for the foreseeable future, most AI compute will continue to live in large, centralized data centers with high barriers to entry. Training large models and operating massive inference fleets still requires the scale and efficiency only hyperscale infrastructure can provide.
“Modular data centers are best viewed as complementary,” Saurabh Gayen, the chief solutions architect at Baya Systems, told Built In. “The forces driving today’s massive and centralized AI data centers are not going away, but modular approaches are emerging alongside them to add flexibility and reach.”
Who Is Using Modular Data Centers?
Cloud providers and AI-focused tech firms are turning to modular data centers to handle heavy-duty compute. Crypto and blockchain companies are another group buying in as they pivot toward AI workloads. Any enterprise that relies on edge computing — from large industrial operators to global tech firms — are also in business to cut latency and process data in real time. And their support for sovereignty makes modular data centers especially appealing to government sectors, particularly defense and other security-sensitive agencies, because they keep infrastructure under direct control, rather than in shared cloud environments.
“Not every organization wants to be fully dependent on [centralized] providers for all of their AI needs,” Gayen said. “Many are willing to accept worse total-cost ownership if it means owning and controlling their own infrastructure.”
Examples of Modular Data Centers
The following are some top examples of modular data centers that can handle AI workloads.
HPE Performance Optimized Datacenter (POD)
Hewlett Packard Enterprise invented one of the earliest and most influential modular data center designs in its AI mod “POD,” a term that has since become industry shorthand. Built into standard 20‑ and 40‑foot shipping containers that arrive ready to run, these pre‑configured units include racks, cabling, power and cooling, and can support thousands of nodes in a portable footprint. Designed to plug into existing power and networks, PODs let organizations deploy compute rapidly for remote sites, disaster recovery or quick capacity expansion within days of rolling off the truckbed.
Vertiv MegaMod HDX
Vertiv’s MegaMod HDX accelerates AI deployment by packing direct-to-chip liquid cooling, power distribution and turnkey infrastructure into a single, ready-to-go module. These prefabricated units can be tailored to run the platforms of top AI compute providers, and scaled from hundreds of kilowatts up to multiple megawatts. They can also be used as standalone sites or clustered together for bigger workloads. Because its modules are built and tested offsite, Vertiv claims that it can halve delivery time compared to traditional builds.
Schneider Electric’s EcoStruxure Modular Data Centers
Schneider Electric’s EcoStruxure modules are factory‑built, fully integrated units that can support hundreds of kilowatts to multiple megawatts of compute capacity. Made with integrated liquid cooling and rear-door heat exchangers, high-density racks, hot-aisle containment and intelligent power distribution, they’re engineered to handle the extreme thermal and power demands of modern AI clusters. Because these pods arrive pre-configured and tested, organizations can deploy them next to existing facilities or in urban and edge environments where space and power strategy matter most.
Benefits of Modular Data Centers
Amid the AI gold rush, modular data centers are gaining traction for three primary reasons: speed to market, flexibility and energy efficiency, according to Jagger.
Most workloads still run on standard, lower-power infrastructure, which means they can operate in enterprise data centers, colocations or hyperscale cloud — ultra-dense AI setups are still in their infancy and remain in the minority when it comes to data center builds.
Speed to Market
Traditional data centers are built step by step on-site — permits, foundations, electrical, cooling — which can take anywhere from one to three years. Modular data centers flip that model. The infrastructure is built and integrated in a factory while site work happens at the same time, cutting deployment as early as six months from the time the module was ordered. Jagger referenced one of HP's past projects, including the multi-container AI-mod pod deployment that now powers one of Europe's fastest supercomputers, Isambard-AI, at the University of Bristol in the United Kingdom. Built with 5,400 NVIDIA superchips, the data center was developed in six months, then switched on just two days after delivery.
A shorter timeline matters especially in AI, where hardware generations evolve quickly and waiting too long can mean deploying yesterday’s tech.
Flexibility
Modular systems follow a pay-as-you-grow approach. Instead of committing to a massive campus that may sit partially empty, operators can add capacity in phases, matching real demand pod by pod. That means installing high-density AI racks only when training or inference workloads justify it, avoiding capital tied up in unused space and power.
Energy Efficiency
Modular data centers are engineered specifically for dense compute. This is where direct liquid cooling, closed-loop cooling systems and tightly controlled airflow right at the factory stage come into play. Because cooling, power and IT gear are designed as one system inside a sealed footprint, many modular deployments achieve power usage effectiveness levels of 1.2 or lower. That’s a meaningful efficiency gain compared to the standard set by air-cooled legacy data centers at 1.56.
Latency
Another obvious win is placement. Modular data centers can sit right next to wherever data is being generated, whether that’s inside cities, near industrial parks or at telecom aggregation points. Shorter physical distance results in lower network latency, which is critical for live AI inference across applications like robotics, autonomous vehicles and medical diagnostics, to name a few.
Portability
These aren’t fixed, permanent structures. Modules can be relocated if power prices change, grid capacity shrinks or a better site becomes available. That impermanence gives operators a hedge against shifting energy markets, unpredictable regulatory changes or even real estate constraints.
Sovereignty
Modular data centers give companies control over where their data lives, making it easier to comply as national laws catch up with tech. This is especially relevant in Europe, where regulations like General Data Protection Regulation (GDPR) and the EU Data Act push organizations to avoid conflicts that can arise when using foreign-owned cloud providers subject to laws such as the U.S. CLOUD Act.
“Right now, the nations have a conflict in terms of who has to do what and comply, because the standards in Europe are different to those in the U.S.” Jagger said. “Hyper scalers may not be going as far with very localized micro data centers, but modular approaches are definitely being deployed, even in classified sites.”
He pointed to Germany as a growing hub due to its geographic position for distributing compute across neighboring countries. One example from the UK is Carbon3.ai, a waste management subsidiary that’s investing £1 billion to convert legacy industrial and energy sites into a nationwide network of modular, renewable-powered data centers. These facilities will in turn create a sovereign-by-design private cloud AI infrastructure that’s fully operating under British legal jurisdiction.
Frequently Asked Questions
How much faster is it to deploy a modular data center compared to a traditional hyperscaler?
Modular data centers can be built and operational in a matter of weeks or a few months because they’re built off-site in a factory and assembled quickly once delivered on location. Comparatively, traditional hyperscalers usually take one to three years, from planning through commissioning, until they’re live.
Can modular units handle high-density AI workloads and liquid cooling?
Yes; modern modular infrastructure is explicitly engineered to support high-density compute as well as the advanced, liquid-cooling systems required by such workloads. Vendors now sell prefabricated modules built with 50 kilowatt to 200 kilowatt racks to handle AI and high-performance computing.
Are modular data centers more expensive than traditional facilities?
Not at all; Modular data centers are more cost efficient, ranging between $400,000 up to $1 million, according to State Tech Magazine. Traditional facilities run about $10 million per megawatt, collectively racking up trillions worldwide.
