The rapid expansion of generative artificial intelligence (AI), large language models and advanced computing workloads is driving the emergence of a new category of cloud providers known as “neoclouds”, as businesses seek infrastructure specifically designed to support increasingly complex AI applications.
According to GlobalData’s Neoclouds Trend Analysis report, neocloud providers are becoming a critical layer in the AI ecosystem by offering GPU-focused cloud environments built for intensive AI training, inference and deployment workloads that traditional cloud platforms were not originally designed to handle.
Unlike conventional hyperscale cloud providers that offer broad enterprise computing services, neoclouds are focused on AI-native infrastructure, combining high-performance GPUs, accelerated computing, low-latency networking, distributed storage and specialised environments for running large-scale AI operations.
The report noted that investment momentum around AI infrastructure accelerated significantly, reaching $47 billion in 2025, including a $40 billion investment by MGX in Aligned Data Centers, reflecting growing demand for the physical and digital infrastructure required to power the next generation of AI systems.
GlobalData said the expansion of foundation models, generative AI applications and large-scale inference workloads has increased demand for specialised computing environments capable of handling massive AI workloads efficiently.
“Neoclouds are emerging as a distinct class of cloud providers focused on delivering AI workloads more efficiently at scale,” the report stated, adding that they occupy a position between traditional hyperscale cloud platforms and privately managed on-premise systems.
The growing market is being driven by several factors, including limited availability of advanced GPUs, rising demand for cost-efficient AI computing, and the need for infrastructure capable of supporting real-time AI applications.
Companies such as NVIDIA, CoreWeave, Lambda, VAST Data and Crusoe are among the organisations contributing to the development of the neocloud ecosystem.
The report highlighted that enterprises are increasingly adopting hybrid AI infrastructure models, distributing workloads across traditional hyperscalers and specialised providers depending on factors such as computing performance, cost, latency requirements and GPU availability.
While hyperscale cloud companies continue to maintain strong positions because of their global infrastructure, enterprise relationships and integrated services, GlobalData noted that neoclouds are increasingly becoming important for compute-intensive AI workloads where specialised infrastructure offers performance advantages.
Beyond cloud services, innovation across the broader AI infrastructure ecosystem is also supporting neocloud growth. Advances in networking, liquid-cooled data centres, AI orchestration software and high-throughput storage are helping providers build more efficient environments for large AI models.
However, the report noted that the sector faces challenges, including dependence on GPU suppliers, high infrastructure costs, energy requirements and growing competition from established cloud giants expanding their own AI capabilities.
The future of neoclouds, according to GlobalData, will depend on their ability to maintain cost advantages, secure access to critical computing resources and support the growing demand for multimodal and agentic AI applications.
As organisations move from experimenting with AI to deploying AI systems at scale, specialised infrastructure providers are expected to play a larger role in determining how businesses build, train and operate next-generation AI models.
