Neoclouds vs Hyperscalers—The Battle Reshaping Enterprise IT

Highlights
20/01/2026

The dominance of AWS, Azure, and Google Cloud seemed unassailable. Then came the neoclouds. DTN Technology’s Timur Büyük unpacks the competitive landscape that’s giving enterprises genuine choice for the first time in years.

In technology, dominance rarely lasts forever. IBM ruled enterprise computing until it didn’t. Microsoft owned desktop software until the cloud arrived. And now, the seemingly unshakeable position of hyperscale cloud providers faces its first serious challenge.

Not from each other—AWS, Microsoft Azure, and Google Cloud have been competing vigorously for years. But from a new category of specialist provider that’s rewriting the rules of cloud infrastructure.

Welcome to the era of neoclouds.

The Hyperscaler Playbook

To understand why neoclouds matter, we need to understand what hyperscalers do brilliantly—and where their model struggles.

AWS, Azure, and Google Cloud built empires by offering everything. Storage, compute, databases, networking, analytics, machine learning tools, IoT platforms, blockchain services, quantum computing access. The value proposition: one provider, integrated ecosystem, massive scale.

For most businesses, most of the time, this works beautifully. Migrate your infrastructure to AWS, and suddenly you have access to hundreds of services without managing physical hardware. The integration between services—S3 storage feeding into Lambda functions triggering SageMaker models—creates genuine operational efficiency.

“Hyperscalers democratised enterprise-grade infrastructure,” notes Timur Büyük, cloud specialist at DTN Technology. “A startup in Shoreditch can access the same computational resources as a FTSE 100 company. That’s genuinely revolutionary.”

But this breadth comes with trade-offs. When you serve every use case, you optimise for none. The infrastructure supporting a simple WordPress site and a complex AI inference deployment is fundamentally the same—general-purpose, flexible, but not tailored to specific high-performance needs.

For years, this didn’t matter. Hyperscalers’ scale advantages—purchasing power, engineering resources, global datacenter networks—outweighed any specialisation benefits.

Then AI inference workloads exploded. And suddenly, the gaps became visible.

Where Hyperscalers Struggle

The challenge isn’t capability. AWS, Azure, and Google Cloud all offer GPU instances. They all support AI frameworks. They all have customers successfully deploying inference at scale.

The challenge is structural.

GPU provisioning times at hyperscalers can stretch to weeks or months. Not because they’re incompetent, but because global GPU demand vastly exceeds supply, and their general-purpose infrastructure wasn’t designed for the specific demands of AI workloads.

Pricing structures, optimised for diverse workloads, often prove suboptimal for sustained GPU usage. An instance type perfect for occasional machine learning experiments becomes expensive when running inference 24/7.

Configuration complexity grows. Setting up high-performance GPU infrastructure on a hyperscaler often requires navigating dozens of services, configuring networking, managing storage, optimising data pipelines. The breadth that’s usually an advantage becomes cognitive overhead.

“We see enterprises spending weeks architecting their AI infrastructure on hyperscalers,” Büyük observes. “Not because the platform is bad—it’s excellent—but because it’s built for flexibility across use cases rather than turnkey optimisation for one specific workload.”

This created an opening. A gap between what hyperscalers offered and what AI-focused businesses actually needed.

The Neocloud Response

Neoclouds like CoreWeave, Crusoe, Lambda Labs, and others took a radically different approach: specialisation over generalisation.

Instead of offering hundreds of services, they focused on one thing—GPU-centric infrastructure optimised specifically for AI workloads, particularly inference.

The value proposition is simple: faster provisioning, better performance, lower cost. All achieved by ruthlessly optimising for a narrow use case rather than trying to serve everyone.

Provisioning that takes weeks on hyperscalers? Days or hours on neoclouds, because their entire infrastructure is GPU-focused rather than general-purpose.

Configuration complexity? Dramatically reduced, because the platform is purpose-built for AI workloads. You’re not assembling dozens of services—you’re deploying on infrastructure designed specifically for what you’re doing.

Pricing? Often significantly lower for sustained GPU usage, because neoclouds’ business model centres on GPU workloads rather than amortising datacenter costs across diverse services.

“Neoclouds aren’t better at everything,” Büyük emphasises. “But for GPU-intensive AI inference, they’re often significantly better at the specific things that matter—availability, performance, cost.”

Early adoption came from AI-native companies—startups building products around large language models, computer vision applications, generative AI services. These businesses needed GPU infrastructure immediately, couldn’t wait months for provisioning, and found neocloud offerings compelling.

Increasingly, though, traditional enterprises are exploring neoclouds as part of hybrid strategies.

The Strategic Choice

This creates a genuinely new decision point for IT leaders.

Previously, cloud strategy was relatively straightforward: choose between AWS, Azure, and Google Cloud based on existing relationships, specific service needs, or pricing. Competition existed, but within a fairly narrow band.

Now, businesses face a more nuanced question: general-purpose hyperscaler or specialised neocloud? Or, increasingly, both?

“The smart play isn’t either/or,” Büyük argues. “It’s strategic matching. Use hyperscalers for what they do brilliantly—integrated services, broad capabilities, global reach. Use neoclouds for GPU-intensive workloads where specialisation delivers clear advantages.”

This hybrid approach requires more sophisticated infrastructure management. Data needs to move between platforms. Security must span multiple providers. Billing becomes more complex. Vendor relationships multiply.

But the performance and cost benefits can be substantial. A financial services firm might run its core applications on Azure whilst deploying fraud detection inference on a neocloud. A retailer might keep its e-commerce platform on AWS whilst running personalisation algorithms on specialised GPU infrastructure.

The key is treating cloud infrastructure as a portfolio rather than a monolithic choice.

What Hyperscalers Are Doing About It

AWS, Azure, and Google Cloud aren’t standing still. All three have expanded GPU offerings, improved provisioning times, introduced new pricing models for AI workloads.

Microsoft’s partnership with OpenAI gives it unique advantages in deploying cutting-edge AI. Google’s TPUs (Tensor Processing Units) offer alternatives to traditional GPUs. AWS continues expanding its custom silicon efforts with Trainium and Inferentia chips.

But structural advantages persist for neoclouds. They’re smaller, more focused, built from scratch around AI workloads rather than retrofitting general infrastructure.

“Hyperscalers will remain dominant for most enterprise IT,” Büyük predicts. “But neoclouds have carved out a legitimate niche. The question isn’t whether they’ll replace hyperscalers—they won’t. It’s whether enterprises will adopt hybrid strategies that use both.”

The UK Opportunity

For British businesses, this competitive landscape creates options.

London has emerged as a European AI hub. UK enterprises across finance, healthcare, retail, and professional services are deploying AI at scale. Access to appropriate infrastructure directly impacts these initiatives’ success.

Neoclouds provide an alternative to the “wait and see” approach that limited GPU availability might otherwise force. Projects that couldn’t proceed due to hyperscaler provisioning delays can now move forward using specialist providers.

This matters for competitiveness. AI advantage often accrues to first movers. Being six months ahead of competitors in deploying effective AI can create durable market advantages. Infrastructure availability increasingly determines who moves first.

“UK businesses have been understandably cautious about cloud diversity,” Büyük notes. “The operational simplicity of single-provider strategies is attractive. But as AI becomes central to competitive strategy, infrastructure flexibility becomes essential. The winners will be organisations treating their cloud architecture as a strategic asset, not just a procurement decision.”

Looking Forward

The hyperscaler vs neocloud dynamic will continue evolving. Hyperscalers will improve AI-specific offerings. Neoclouds will expand capabilities and scale. Consolidation seems inevitable—either through acquisitions or market shakeout.

But the fundamental dynamic—tension between general-purpose breadth and specialised optimisation—will persist. Different workloads genuinely benefit from different infrastructure approaches.

For enterprises, this means the era of simple cloud decisions is over. “Just use AWS for everything” was never optimal, but it was defensible. Increasingly, it isn’t.

The complexity is worth it. Because on the other side of that complexity lies better performance, lower costs, and faster deployment of business-critical AI capabilities.

The cloud revolution isn’t about replacing one dominant provider with another. It’s about enterprises finally having genuine strategic choice—and the sophistication to deploy it effectively.

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