Few decisions are as fundamental to the future of an AI-driven business as where and how to run compute workloads. The rise of artificial intelligence (AI), particularly high-performance applications like generative AI, large language models (LLMs) and real-time data processing, has reignited the long-running debate: hyperscale cloud, colocation, or on-prem infrastructure?
It’s a long running debate and each model has its benefits, but AI’s relentless demand for compute power, efficiency and scalability is quickly shifting the goalposts. The question isn’t just one of cost – it’s about performance, control and long-term strategic advantage.
The Shift to Hybrid Infrastructure
The reality is that few organisations today rely on a single infrastructure model. The concept of hybrid IT has become essential. Businesses are already blending on-prem, colocation, and hyperscale cloud to allow resilience, cost efficiency and performance optimisation. The rise of hybrid data centre architectures – integrating core, regional, and edge facilities – means workloads are now being placed where they function best, rather than where legacy infrastructure dictates.
At the heart of this shift is the growing divide between traditional workloads and AI-driven processing. Standard enterprise applications continue to run efficiently in conventional cloud or colocation environments, but AI workloads demand specialist infrastructure. This is leading to a split model – AI-factory data centres, purpose-built for high-density graphics processing unit (GPU) clusters, often separate from traditional IT estates. The industry is moving towards a multi-hybrid approach, where infrastructure choices vary not just by business need, but by application type.
Hyperscale: The AI Powerhouse
The hyperscale cloud has been an undeniable force in the AI revolution over the last year or so. The big hyperscalers have fuelled an explosion of AI development by offering vast, on-demand infrastructure, paired with sophisticated machine learning frameworks.
For AI start-ups and the enterprises which are scaling fast, the benefits are instant access to GPUs and tensor processing units (TPUs), seamless scalability, and AI-specific services like managed model training and inference at the edge. Hyperscale providers also excel in global availability, meaning AI workloads can be deployed closer to users and data sources.
But convenience comes at a cost. Many enterprises have learned the hard way that running AI in the cloud can be financially unpredictable. Cloud pricing models – particularly for AI workloads – can be complex, with steep charges for compute, storage, and, critically, egress fees (the cost of moving data out of the cloud).
The lock-in risk is also significant – once a business has optimised its AI stack for a specific provider’s ecosystem, switching becomes complex. Furthermore, data gravity – the difficulty of moving massive datasets between clouds – has become a major hurdle for businesses handling AI at scale.
Is Colocation the Best of Both Worlds?
Colocation has emerged as an increasingly attractive alternative, particularly for businesses looking to balance cost efficiency with performance. Rather than building an entire data centre from scratch, colocation allows organisations to house their own servers within a third-party facility, benefiting from enterprise-grade power, cooling and connectivity without the burden of maintaining an entire site.
For AI workloads, colocation offers a compelling middle ground: businesses maintain control over specialist hardware (custom AI chips, proprietary architectures) while avoiding the sky-high operational costs of hyperscale. Crucially, data sovereignty and security concerns – which are growing as AI models process ever more sensitive information – are easier to manage in a colocation environment. Many providers also offer direct cloud on-ramps, allowing a hybrid strategy where AI workloads can shift dynamically between on-prem and cloud resources based on cost and performance needs.
But colocation isn’t a silver bullet. While lowering operational costs compared to cloud, it still requires significant CapEx investment in hardware, as well as skilled teams to manage the infrastructure. And while interconnectivity is improving, latency-sensitive AI applications might still struggle without the direct, high-bandwidth integrations that hyperscalers provide natively.
On-Prem: The Control-First Approach
On-prem data centres were once the default choice for enterprises with significant IT needs. Today, they remain the preferred model for AI-focused organisations that prioritise control, compliance and customisation – particularly those handling proprietary, high-stakes workloads.
AI training is hardware-hungry, requiring not just GPUs, TPUs, and Field Programmable Gate Arrays (FPGAs), but also fine-tuned power and cooling strategies to maintain efficiency. For businesses running continual, high-load inference models, owning the infrastructure outright can offer major savings in the long term – avoiding cloud cost shocks and eliminating reliance on external providers. Advances in liquid cooling and immersion cooling are also helping on-prem data centres remain competitive with hyperscale efficiency.
But the challenges of on-prem remain significant. The upfront investment is immense, requiring long-term infrastructure planning. Organisations must also build and retain highly skilled teams – a daunting task as data centre talent shortages grow. And scaling up isn’t as simple as spinning up more cloud instances; it requires careful planning around physical space, power draw and failover strategies.
Hybrid and Multi-Hybrid Will Win
Ultimately, AI infrastructure isn’t an either / or decision. For most organisations, a hybrid or multi-cloud approach is the most pragmatic solution – balancing cost, control and performance. Hyperscale remains essential for burst capacity and experimentation, colocation provides cost-efficient, high-performance alternatives and on-prem is indispensable for high-security, ultra-low-latency workloads.
Looking ahead, interoperability will define AI infrastructure strategies. Providers are already making cloud-to-cloud and on-prem integrations smoother, and AI workloads will increasingly shift dynamically between environments based on real-time cost, performance, and compliance needs. The rise of sovereign AI cloud solutions, edge AI computing, and AI-specific data centre designs will add further complexity to the landscape, forcing businesses to rethink rigid cloud strategies in favour of true AI workload portability.
It all means that success won’t come from choosing a single model but from mastering the art of flexibility. The world has gone hybrid – and for AI, it’s going multi-hybrid.
