For the past decade, enterprises have been guided by a prevailing assumption. In the 2010s, conventional wisdom became that the future of IT infrastructure belonged to hyperscale cloud providers. The argument was compelling – unmatched scalability, rapid deployment and reduced capital expenditure. But as artificial intelligence (AI), high-performance computing (HPC) and cost volatility fundamentally reshape the landscape, enterprises are shifting from a cloud-first mindset to a more nuanced approach, blending public cloud with private and colocation solutions.
This is not a retreat from hyperscale cloud providers but rather an evolution in enterprise strategy. Businesses are now recognising that no single approach fits every workload. Instead, they are focusing on aligning workloads with environments that offer the best combination of cost, performance and control.
The Changing Economics of Cloud and AI Workloads
Public cloud made financial sense when workloads were dynamic and unpredictable, and when enterprises sought to avoid the capital outlays of on-premise infrastructure. However, the cost dynamics are shifting, especially for sustained, compute-intensive applications such as AI training and inference.
Hyperscale providers offer AI-optimised instances. However, enterprises are discovering that ongoing AI workloads incur high operational costs compared to predictable, long-term investments in private infrastructure or colocation. As a result, many organisations are evaluating hybrid models. These models use colocation for cost-predictable, high-performance workloads. At the same time, they leverage the public cloud for burst capacity and distributed applications.
Beyond cost, latency and data gravity, regulatory considerations are making private and hybrid environments more attractive. When data volumes are large and constantly processed – such as in AI model training, real-time analytics or financial trading – keeping workloads closer to their data sources in private or collocated infrastructure can improve efficiency and compliance.
Reassessing Private Infrastructure
The resurgence of private and hybrid cloud does not mean a return to outdated models of IT ownership. Instead, it reflects a growing emphasis on performance-driven infrastructure decisions.
Enterprises are leveraging colocation and private cloud for several reasons:
- Workload optimisation: Not all applications benefit from the shared infrastructure model of public cloud. High-performance AI training, real-time applications and compliance-heavy workloads often require dedicated, optimised resources.
- Operational predictability: Cloud pricing models, with their unpredictable egress costs and variable compute rates make budgeting challenging for enterprises running sustained workloads. In contrast, colocation and private cloud offer greater cost predictability.
- Regulatory compliance: As data sovereignty laws tighten, enterprises need to ensure data locality and compliance without sacrificing flexibility. Private environments provide greater control over infrastructure security and governance.
This shift is not about replacing hyperscale cloud, it’s about refining its role in enterprise IT. Organisations are recognising that different workloads require different environments. The future belongs to a hybrid strategy where cloud, private infrastructure and colocation work in tandem.
The Role of Colocation in AI and High-Density Computing
Colocation is evolving beyond traditional space-and-power offerings. With the rise of AI, high-performance computing, and latency-sensitive applications, modern colocation providers are becoming strategic partners in hybrid IT deployments. Some of the key developments include:
- AI-optimised infrastructure: Enterprises are deploying high-density graphics processing unit (GPU) clusters in colocation facilities designed for liquid cooling and high-power density.
- Cloud interconnection hubs: Many colocation providers offer direct on-ramps to hyperscale clouds, enabling businesses to integrate public and private infrastructure seamlessly.
- Energy and sustainability considerations: While cost and performance are primary drivers, enterprises are also under pressure to meet sustainability targets. Colocation providers are investing in renewable energy sourcing, waste heat reuse, and water-efficient cooling to align with corporate Environmental, Social and Governance (ESG) goals.
Strategic Workload Placement
Instead of debating whether public cloud or private infrastructure is better, leading enterprises are taking a more pragmatic approach – placing workloads where they perform best. The options to be considered, include:
- High-performance AI and HPC: Dedicated infrastructure in private or collocated environments for AI model training, large-scale simulations and mission-critical analytics.
- Cloud-native applications: Public cloud for applications requiring global scalability, rapid development cycles and dynamic elasticity.
- Regulated and sensitive data: Private cloud or colocation to ensure compliance, security, and data locality.
- Hybrid cloud interplay: Seamless movement of workloads between private and public environments, ensuring both efficiency and flexibility.
Emerging Challenges and Considerations
As enterprises adopt hybrid strategies, new challenges arise. Managing a mix of cloud, colocation and private infrastructure requires advanced orchestration tools, workload automation and robust security measures. Businesses must also invest in skills and training to enable IT teams to navigate the complexities of multi-environment management effectively.
Another growing concern is the increasing pressure on data centre power grids. AI workloads are driving up energy demands, making efficiency and sustainability critical factors. Enterprises are increasingly looking for colocation providers with strong commitments to energy efficiency and innovative cooling solutions.
Looking Ahead
The past decade’s cloud-first narrative is giving way to a more practical, workload-driven approach to IT infrastructure. The future is not about choosing between public cloud, private cloud, or colocation – it’s about using all three in the right proportions.
Enterprises that embrace this hybrid approach will benefit from performance optimisation, cost control and regulatory compliance while still retaining the agility to scale where needed.
The hyperscale cloud remains an essential part of enterprise IT, but it is no longer the default answer for every workload. Instead, businesses are moving towards a strategic, workload-optimised infrastructure model that blends cloud, colocation and private environments for maximum flexibility and performance.
As AI and high-performance computing redefine what’s possible, enterprises must think beyond infrastructure decisions in isolation. They need to consider how data flows, how latency impacts decision-making, and how evolving regulations will shape the future of IT architecture. Those who build their infrastructure strategies with adaptability in mind – prioritising flexibility, security and resilience – will not only future-proof their operations but will also be positioned to lead in a rapidly evolving technological landscape.
With technology evolving at an unprecedented rate, the enterprises that will thrive are those that embrace infrastructure as a competitive advantage, not just an operational necessity. The focus is shifting from merely accessing scalable compute power to crafting an interconnected, high-performance IT ecosystem that aligns with business goals. Those that approach infrastructure decisions strategically – rather than defaulting to one model – will be best placed to navigate the complexities of AI, high-performance computing, and the new economics of cloud.
- Data & AI
- Infrastructure & Cloud