AI vs. traditional cloud workloads: What enterprises need to know

AI vs. traditional cloud workloads: What enterprises need to know

AI is reshaping enterprise infrastructure decisions at a pace most planning cycles weren't built for. While traditional cloud workloads haven't gone away — and won't — AI introduces a fundamentally different set of requirements around power density, cooling, latency, and location that most existing infrastructure wasn't designed to handle.

For enterprises, hyperscalers, and neoclouds deciding where and how to deploy AI workloads in production, the stakes are high. Capacity is limited, costs are rising, and the right architecture choices made now will define competitive positioning through the end of the decade. This article breaks down what's different, what it costs, and how to choose the right approach.

What are traditional cloud workloads?

Traditional cloud workloads are the backbone of modern enterprise IT: databases, web applications, storage systems, business software, and general-purpose compute that organizations have been running in colocation data centers and public cloud environments for the past two decades.

These workloads were built around CPU-based compute, processors designed for broad versatility across many task types. The infrastructure that supports them was engineered accordingly: moderate power densities of 5–10 kW per rack, air-cooled facilities, and centralized campuses located where land and power were cheap.

This model still works well for what it was designed to do. AI applications depend on traditional cloud for storage, databases, APIs, and supporting services. The challenge is that AI workloads layer on top of this foundation with requirements that most traditional cloud infrastructure was never built to handle.

What makes AI workloads unique

AI workloads don't just require more of what traditional cloud provides. They require something architecturally different across three dimensions.

Compute: GPUs vs. CPUs

While traditional cloud runs on CPUs, AI runs on GPUs — chips with thousands of parallel processing cores optimized for the matrix mathematics that underpin machine learning. A single GPU consumes 10–15 times as much energy as a CPU, and a training cluster may contain tens of thousands of them. The power and cooling implications are transformative, not incremental.

Two workload types with opposite infrastructure needs

AI can be split into two fundamentally different categories. Training — developing and refining AI models — is compute-intensive, power-hungry, and latency-insensitive; it can be located remotely where power is cheap. Inference — running trained models in production — powers every AI application a user touches. It's latency-sensitive, geographically distributed, and needs to sit close to end-users.

By 2030, inference is forecast to account for 80% of all AI-critical IT load globally, making it the dominant enterprise infrastructure decision of the decade.

Rapid hardware evolution

Training clusters scaled from roughly 40 MW in 2020 to projected 1–5 GW systems before 2030. The pace of GPU architecture change means infrastructure decisions today need to accommodate hardware generations that don't yet exist — putting a premium on modular, scalable design over fixed, purpose-built builds.

Infrastructure requirements compared

The table below maps the key variables across traditional cloud, AI training, and AI inference — the three workload categories shaping data center strategy through 2030.

Sources: Structure Research Global Markets Report 2025; Iron Mountain Building at Scale Whitepaper. Traditional cloud power density reflects general industry benchmarks.

A rack drawing 7 kW in a traditional enterprise environment will draw 80–100 kW in a GPU-dense AI deployment. That's not a configuration change — it's a facility redesign. Air cooling cannot efficiently manage that heat output, which is why liquid cooling is now a baseline requirement, not an optional upgrade.

Location requirements diverge just as sharply. Training can go where power is plentiful. Inference must be in densely populated, connectivity-rich markets close to end-users — putting pressure on exactly the markets where capacity is most constrained.

For a detailed look at how these requirements are reshaping facility design, see How AI Is Reshaping Data Center Design on the Iron Mountain Data Centers blog.

Cost and performance tradeoffs

The cost dynamics of AI infrastructure are unlike anything the data center industry has encountered before.

Supply constraints are already priced in

Colocation prices rose 35% between 2020 and 2023 — before the full weight of AI demand hit the market. Vacancy rates in Northern Virginia dropped below 1% in 2024. Hyperscaler capex is projected at $375 billion in 2025, a 36% year-over-year increase, absorbing a disproportionate share of available power, construction capacity, and engineering resources.

For enterprises and neoclouds competing for the same capacity, this means paying more, waiting longer, or accepting suboptimal locations.

Lower AI costs drive higher demand, not less

The cost of running the cheapest capable large language model dropped approximately 10x per year between 2022 and 2024. But lower unit costs don't reduce infrastructure demand — they increase it. Economists call this the Jevons Paradox: more affordable AI means more inference events, more training runs, and more infrastructure required to support it all.

Colocation vs. public cloud: The cost case for AI

For production-scale AI inference, purpose-built colocation increasingly outperforms hyperscaler public cloud on cost. Public cloud GPU instances carry significant on-demand premiums. Colocation with reserved capacity offers more predictable pricing and greater control.

The tradeoff is upfront commitment and longer planning horizons, which is why organizations in the experimental phase of AI are often better served starting in public cloud before migrating to colocation as workloads mature.

Deployment considerations for enterprise

For enterprises deploying AI in production, infrastructure decisions are no longer theoretical. They are active procurement choices with material implications for delivery timelines, budget, and competitive positioning.

Inference belongs close to your users and your existing cloud

The most common deployment error enterprises make is treating inference like training — centralizing it in a single facility chosen for cost. Production AI requires geographic distribution and low latency. Inference infrastructure needs to sit next to cloud on-ramps and carrier-neutral interconnects.

Enterprise AI colocation revenue is forecast to grow at an 84.9% CAGR over the next five years (Structure Research), meaning the decisions made now need to scale with that trajectory.

Plan for the hybrid period

Most enterprise environments won't move entirely to AI-optimized infrastructure anytime soon. The realistic picture is a mix: traditional CPU-based workloads continuing in existing cloud contracts alongside AI inference in purpose-built colocation. Infrastructure partners that support both within the same campus ecosystem significantly reduce integration complexity.

Security and compliance

AI workloads introduce new compliance considerations beyond standard IT — including physical security for proprietary model weights, chain-of-custody for training data, and certifications like ISO 27001 and SOC 2. Purpose-built colocation in BREEAM-certified facilities is designed to meet these requirements, reducing audit burden and protecting valuable AI assets.

Neoclouds — purpose-built GPU compute providers like CoreWeave, Nscale, Nebius, and Lambda Labs, which rent AI infrastructure to enterprises rather than building their own — have additional requirements on top of these: speed to deployment, high power density, and global reach across the 25 markets forecast to exceed 1 GW capacity by 2030.

Choosing the right architecture

The correct architecture depends on workload type, scale, geography, and where an organization is in its AI deployment journey. Two questions drive the decision.

Which workloads go where?

• Experimental or low-volume AI: Public cloud GPU instances are appropriate — flexibility outweighs cost efficiency at this stage.
• Production AI inference at scale: Purpose-built colocation in AI-ready, carrier-neutral facilities delivers better performance, more predictable costs, and greater control.
• Large-scale AI training: Primarily a hyperscaler or neocloud-led activity. Most enterprises consume training outputs via APIs rather than operating training infrastructure directly.

What should you look for in a colocation partner?

• Power density roadmap — support for 60–160 kW/rack today, with a clear path to higher densities as GPU architectures evolve.
• Liquid cooling available now — direct-to-chip and rear-door solutions operational and planned.
• Global footprint — inference needs geographic distribution across AI hub markets; a single facility is not a strategy.
• Verified sustainability credentials — 100% renewable energy and a credible decarbonization roadmap. Iron Mountain Data Centers has delivered 100% renewable energy since 2017 and is the only global provider committed to 24/7 carbon-free power by 2040.
• Security and compliance — ISO 27001, SOC 2, and regional data sovereignty frameworks for proprietary model weights and training data.
• Timing — grid interconnection queues run 3–5 years in major markets, and demand is projected to exceed supply by up to 500% by 2030. The right partner has capacity available now or planned to come online in the next 12 months, not just in principle.

Iron Mountain Data Centers operates a 1.3 GW global portfolio with active development across North America, Europe, and Asia Pacific. Visit ironmountain.com/data-centers or download the Generative AI Customer Solution Brief.

Frequently asked questions

Q: Is AI just another cloud workload?

No, and treating it as one is a common mistake in infrastructure planning. Traditional cloud runs on CPUs at 5–10 kW per rack in air-cooled facilities. AI runs on GPUs at 60–160 kW per rack, requires liquid cooling, and splits into two workload types — training and inference — with almost opposite infrastructure requirements. The underlying cloud architecture remains important as a foundation, but AI demands a separate, purpose-built layer on top of it.

Q: Do AI workloads always require GPUs?

For large-scale training and high-performance inference, yes. GPUs are the dominant compute architecture for AI because of their parallel processing efficiency. Lighter inference tasks can run on CPUs for smaller models or low-volume applications, but as complexity and volume grow, GPU infrastructure becomes necessary for performance and cost efficiency at scale. Emerging AI accelerator chips are also entering the market but are primarily accessed via cloud APIs rather than deployed directly in enterprise colocation.

Q: When does it make sense to move AI workloads from public cloud to colocation?

The inflection point is when AI workloads move from experimental to production scale — where volume is predictable, latency requirements are firm, and cost efficiency matters. Public cloud GPU instances carry on-demand premiums that are hard to justify as workloads grow. Colocation with reserved capacity delivers more predictable pricing and greater control. Most organizations benefit from starting in public cloud and migrating to colocation as the business case becomes clear.

Q: What should enterprises prioritize when evaluating a data center partner for AI?

Beyond uptime and pricing, AI-ready evaluation should cover: power density roadmap (30–160+ kW/rack), liquid cooling available now, a global footprint across key inference markets, verified renewable energy sourcing, and compliance certifications for proprietary AI assets. Timing is also critical — the right partner has capacity in the markets you need, not just on paper. Learn more at ironmountain.com/data-centers.