Neoclouds Are Gathering: Infrastructure for the AI Age

The compute-intensive nature of AI is giving rise to the next generation of cloud: neocloud. Designed to handle demanding AI workloads, neocloud is a solution for companies that want to outsource their AI infrastructure in the same way they utilize the hyperscale clouds today for their enterprise IT infrastructure.

AI Drives GPU Rush

AI requires specialized infrastructure. Consequently, the global AI infrastructure market is on track for unprecedented growth, poised to surpass $200 billion in spending by 2028, according to the Worldwide Quarterly AI Infrastructure Tracker published by IDC. Organizations increased spending on compute and storage hardware infrastructure for AI deployments by 97% year-over-year in the first half of 2024, reaching $47.4 billion.¹

“Behind this AI revolution is an important piece of hardware: the Graphics Processing Unit or GPU,” Google Cloud explains. “Originally created for graphics rendering, GPUs have become increasingly important for AI, enabling the training and deployment of complex AI models that were once impossible to imagine.”²

Grand View Research adds that AI applications “often require substantial computational power, which graphics processing units (GPUs) are well-suited to provide. GPU as a service (GPUaaS) provides scalability, allowing users to adjust their computational resources based on their specific requirements. As more businesses and researchers adopt AI and ML technologies, the demand for GPUaaS has risen accordingly.”³

What Are Neoclouds?

“To address the growing demand for AI infrastructure, a new category of cloud providers has emerged, specializing in AI infrastructure as a service,” says Dr. Owen Rogers, Senior Research Director for Cloud Computing, Uptime Institute. “Known as neoclouds, these providers focus on offering GPU-backed servers and virtual machines, often at prices more affordable than those of the hyperscalers.”⁴

Neoclouds are an evolution of cloud for the AI era. While the hyperscalers like AWS, Microsoft Azure and Google offer a suite of cloud offerings, neoclouds focus on GPUaaS optimized for AI workloads. Neoclouds are not necessarily competing with hyperscalers, but rather providing complementary services that fit into a multicloud strategy. For example, an organization could train a large language model (LLM) on a hypersale cloud and then deploy a corresponding AI application on a neocloud.

Neocloud can be used for LLM training, AI inferencing, enterprise AI applications, and other high performance computing use cases, and it is growing in popularity. New data from Synergy Research Group shows that neocloud revenues passed the $5 billion mark in Q2 – growing 205% from last year – and are on track to exceed $23 billion in 2025. Synergy forecasts that the neocloud market will reach almost $180 billion in 2030, growing by an average 69% per year.⁵

Neocloud Advantages: Cost-Effective AI Infrastructure

“Neoclouds have carved out a niche by specializing in GPU-accelerated infrastructure tailored for AI and machine learning workloads,” explains Melissa Farney, Editor at Large for Data Center Frontier (DCF). “Their agility, focus, and deep understanding of AI developers’ needs allow them to offer cost-effective, high-performance solutions that challenge the broader, premium-priced offerings of hyperscalers.”⁶

The following are the primary advantages neocloud offers to AI users:

• Lower Costs: A neocloud typically costs less than a hyperscaler for compute-intensive uses like AI. For example, Uptime Institute found a 66% cost savings when certain GPUs were purchased from a neocloud vs. a hyperscaler.⁴
• “By specializing in high-performance, cost-effective infrastructure, neocloud providers are not only enabling the AI revolution, but also reshaping the economics of cloud computing,” asserts Farney from DCF.⁶
• Rapid Scalability: Voltage Park, a neocloud provider, says a neocloud can scale from just a few GPUs to thousands in less than 15 minutes, without waitlists or over-provisioning.⁷
• This rapid scalability provides crucial time-to-market advantages for businesses needing rapid AI development, according to a neocloud report from JLL.⁸
• High Performance: Since neoclouds do not use a virtualization layer, which is more typical of traditional cloud providers, the direct access to dedicated “bare metal” enables the lower latency and high performance needed to train LLMs.
• Next-Gen AI Hardware: Unlike the hyperscalers, neoclouds specialize in the latest AI-optimized GPUs. “Neocloud providers are laser-focused on providing access to the newest and most powerful GPUs – often before hyperscalers can make them widely available,” Farney from DCF explains.⁶

Supporting Neocloud with Colocation

CoreSite colocation data centers can be ideal environments for neocloud, delivering the must-have capabilities required for AI workloads, including:

• Power Density: GPUs designed to handle AI – the foundational elements of neocloud – require enormous amounts of power, and CoreSite data centers are designed to support these extreme power density demands.
• Liquid Cooling: The power consumed by GPUs – and the excess heat generated by these processors – make leading-edge cooling techniques vital for neocloud. CoreSite enables customers to deploy advanced liquid cooling solutions to support AI-driven workloads in facilities such as the recently completed NY3 site in Secaucus, New Jersey.
• Low-Latency Connectivity: AI tasks such as inferencing require low-latency connections to real-time applications. This is a key competitive differentiator for CoreSite, offering a variety of robust network interconnections to support neocloud and other AI initiatives.
• Guaranteed Availability: AI applications often crunch numbers 24 hours per day, making infrastructure uptime absolutely essential. With this in mind, CoreSite data centers are purpose-built to ensure maximum availability and redundancy, backed by CoreSite's 100% uptime SLA.
• Scalable Resources: As mentioned earlier, scalability is a prime advantage of neocloud, and CoreSite provides easily scalable on-demand space, bandwidth, power and cooling.

Know More

To delve even deeper into how CoreSite supports cutting-edge AI, check out a new guest blog by Sabur Mian, CEO and Co-Founder of STN, detailing how STN’s neocloud hosts the Skild Brain – a general-purpose operating system that manages inferences and directs robot actions, leveraging the first-ever truly intelligent foundational model – in CoreSite’s CH2 data center.

References

1.  Artificial Intelligence Infrastructure Spending to Surpass the $200Bn USD Mark in the Next 5 years, International Data Corporation (IDC), February 18, 2025 (source)
2. What is a GPU and its role in AI?, Google Cloud (source)
3. GPU As A Service Market Size To Reach $12.26 Billion By 2030, Grand View Research, April 2025 (source)
4. Neoclouds: a cost-effective AI infrastructure alternative, Dr. Owen Rogers, Senior Research Director for Cloud Computing, Uptime Institute, February 26, 2025 (source)
5. Neoclouds Currently Growing by Over 200% per Year; Will Reach $180 Billion in Revenues by 2030, Synergy Research Group, October 13, 2025 (source)
6. Deep Data Center: Neoclouds as the ‘Picks and Shovels’ of the AI Gold Rush, Melissa Farney, Editor at Large, Data Center Frontier (DCF), April 18, 2025 (source)
7. NeoClouds: The Next Generation of AI Infrastructure, Voltage Park, May 2025 (source)
8. The rise of Neocloud in the AI landscape, JLL, 2025 (source)