AI in Cloud Infrastructure and the Rise of “Neocloud” Economies

Artificial intelligence is no longer just a software layer on top of the internet. It is reshaping the very foundations of digital infrastructure. As machine learning models become more complex and data-hungry, traditional cloud platforms are evolving to meet new demands. At the same time, a new generation of specialised providers, often called "neocloud" platforms, is emerging to serve the growing market for AI-specific workloads.

Understanding how this shift is happening is crucial for any business that wants to plan its technology strategy for the years ahead.

What Is Changing in Cloud Infrastructure to Support AI?

Cloud computing was originally built to host websites, applications and data. AI workloads have changed that model. Training a modern AI model can involve thousands of graphics processing units (GPUs), petabytes of data, and weeks of continuous processing. These requirements stretch traditional infrastructure beyond its original purpose.

To meet these challenges, the major providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud are re-engineering their systems. They are investing in purpose-built data centres, high-speed networking, and specialised chips like Google’s Tensor Processing Units (TPUs). These upgrades reduce training times, improve performance and allow companies to handle AI projects that were previously too large to run in the cloud.

Why Are New Players Like CoreWeave Gaining Ground?

The cost and complexity of AI workloads are creating opportunities for new providers to compete with the cloud giants. One of the most prominent is CoreWeave, a company focused entirely on GPU-accelerated computing. Because they are not trying to serve every type of workload, neocloud companies can offer tailored infrastructure that is faster to deploy and often more cost-effective.

This is especially attractive for AI startups, research organisations, and mid-sized companies that need high-performance computing but cannot justify the premium pricing or long-term contracts of the hyperscale providers.

Neocloud platforms often offer:

• Quick access to high-performance GPU clusters designed for training and inference

• Transparent, usage-based pricing that makes budgeting easier

• Access to the latest hardware before it is available on mainstream platforms

Related reading: The Sustainability and Diversity Gap in AI Advancements

How Are Cost Pressures Changing the Cloud Market?

AI is expensive. Training a large language model can cost millions of pounds in compute time alone. As adoption grows, organisations are under pressure to make these projects financially viable.

Several trends are emerging:

• Shift from capital expenditure to operational expenditure: Businesses are renting cloud capacity instead of building their own infrastructure.

• New pricing models: Providers are introducing AI-specific billing, such as per-second GPU pricing or discounts for long-running training jobs.

• Hybrid strategies: Many companies are mixing hyperscale and neocloud capacity to balance cost, performance and availability.

For small and medium-sized businesses, these trends mean that access to advanced AI infrastructure is no longer out of reach. As competition increases, costs are expected to fall, making powerful AI workloads more accessible.

Related reading: AI Tools Your SME Can Actually Use Without Breaking the Budget

How Will Neocloud Economies Change Business

Strategy?

The rise of neocloud providers is forcing businesses to think differently about their infrastructure. Instead of committing to one cloud platform, many are adopting a multi-cloud approach that assigns different workloads to different environments.

For example:

• Intensive model training might be handled by a neocloud provider like CoreWeave.

• Deployment and global distribution might remain with a hyperscaler like Azure or AWS.

• Sensitive workloads subject to regulatory requirements might run on private infrastructure.

This more flexible approach allows businesses to optimise performance and cost while staying compliant and scalable.

Related reading: Why IT Shouldn’t Be an Afterthought: Building Tech into Your Growth Plans

What Should Businesses Do Next?

The shift to AI-driven infrastructure is accelerating. Companies that prepare early will be better positioned to compete. Here are some practical steps:

• Audit your existing workloads and identify those that could benefit from GPU acceleration.

• Compare pricing and capabilities across hyperscale and neocloud providers.

• Negotiate flexible contracts to adapt to evolving AI needs.

• Invest in internal expertise to manage multi-cloud environments effectively.

Comparison: Traditional Cloud vs. Neocloud

Feature	Traditional Cloud	Neocloud Providers
Focus	General-purpose workloads	AI and GPU-intensive workloads
Pricing	Broad service bundles	Usage-based and GPU-focused
Flexibility	Broad geographic reach	Specialised performance optimisation
Deployment speed	Standard provisioning	Rapid GPU deployment
Best suited for	Web apps, databases, simple AI	Large-scale AI training and deployment

AI is transforming the cloud landscape. The emergence of neocloud providers is not a minor development but a structural shift that reflects how digital infrastructure is evolving. Businesses that understand and adapt to this new environment will find themselves with more options, better performance, and stronger cost control as AI continues to shape the future of technology.