Cloud AI refers to the use of artificial intelligence services through cloud computing platforms, allowing businesses to access technologies such as machine learning, data analysis, speech recognition, image processing, and language translation over the internet.
Instead of building and maintaining expensive infrastructure, companies can use platforms like Google Cloud AI, Microsoft Azure AI, and AWS AI, which are supported by large data centres. Since the early 2010s, cloud computing has made AI more affordable, scalable, and accessible through a pay-as-you-use model.
As a result, businesses can innovate faster, reduce costs, and scale their operations efficiently. Cloud AI is widely used to power applications such as chatbots for customer service, fraud detection in banking, predicting customer behavior in retail, optimising supply chains, and delivering personalised marketing.
Globally, Cloud AI adoption is increasing, with North America leading due to strong technological infrastructure and investment, while Europe is expanding its use in sectors like finance and healthcare. The Asia Pacific region is experiencing the fastest growth, driven by rapid adoption in countries such as India, China, and Singapore, indicating that Cloud AI is becoming a standard technology worldwide.
Enter neoclouds
Cloud AI proved to be suitable for delivering AI services and AI workloads.
But, these days it is starting to show limitations when handling very large and complex workloads, especially as more users depend on AI services at the same time. To meet these growing demands, neoclouds were introduced as an advanced type of cloud infrastructure designed specifically for high-performance AI tasks. They use powerful GPUs, and services for training and running AI and other compute-intensive workloads.
All of this is usually at a lower and more predictable cost than what big hyperscalers offer.
For example, if a company is building an advanced chatbot the likes of ChatGPT, it requires processing huge amounts of data quickly to respond in real time. Flexible providers offer neoclouds which can handle this workload more efficiently than standard cloud platforms that were built to handle general purpose IT.
So, neoclouds are advanced cloud platforms built specifically to handle AI workloads more efficiently than traditional cloud systems. A few ways they differentiate themselves is by offering GPU-as-a-service (GPUaaS) and AI workloads, while focusing on high-performance compute (HPC), generative AI and large-scale ML training and inference with near bare-metal access to GPUs like Nvidia’s H100/B100.
A new class of compute
So neoclouds, a new class of AI-first cloud providers that emerged in the early 2020s.
Currently, how hyperscalers and neoclouds differ is that hyperscalers offer broad, multi-service platforms for almost any workload from databases to ERP. As these hyperscalers use expensive cutting-edge GPUs, it can be costly as well as capacity-constrained to run certain types of AI workloads.
Neoclouds however is optimised for specific AI workloads, they focus narrowly and deeper, by optimising data centres, networking, and software stacks for AI performance. They also tend to offer lower latency and easier access to GPUs; pricing can be up to 85-percent cheaper than hyperscalers for certain configurations of resources.
Because of how specific and narrow the workloads they are optimised for, they often integrate with existing multicloud setups.
Data centre cooling vendors like Motivair also think neocloud architectures require denser GPU campuses, advanced cooling, power planning, and network fabric that is tuned for AI training clusters.
The horizon
Increased demand for generative AI and large models means there is a gap that flexible providers can fill. McKinsey reports that neoclouds emerged as stopgaps to address GPU shortage, and recognised that these flexible providers have the potential to move up the stack into AI-native services.
According to their report, “(Neoclouds’) future, however, likely lies not in rivaling hyperscalers but in securing positions in enduring niche markets, such as sovereign compute and specialised workloads, while also compounding the early footholds they’ve built with AI start-ups.”
Setting up an AI-oriented GPU cloud service is easier due to mature open-source tooling and off-the-shelf hardware. As a result many VC-backed players have entered to serve unmet demand worldwide.
Analysts like Synergy Research also identified that beyond the main players that are generating revenues and impact like OpenAI, Nebius, Crusoe, CoreWeave, and Lambda, there is a long tail of companies that are in early stages of launch like Altair, Applied Digital, Bitdeer, Fluidstack, Humain, and more.
Synergy Research also wrote, “Neoclouds are either relatively new start-up companies, or crypto miners that are transitioning to being providers of high-performance computing services.”
Data shows that neoclouds revenue grew over 200-percent year-on-year reaching nearly USD5 billion in Q2 of 2025. There are forecasts that this figure will hit USD180 billion by 2030, which makes neoclouds one of the fastest-growing sectors in the data centre ecosystem.