For over a decade, IT professionals have largely adopted a cloud-first approach to digital transformation. This strategy made sense; during the early days of cloud technologies, the cloud's flexibility, scalability, and ubiquity were truly groundbreaking. However, in recent years, other factors have emerged that have encouraged many organisations to consider hybrid strategies, utilising the best of on-prem and cloud infrastructure.
Today, much of this shift is driven by the compute- and cost-intensive demands of AI. Still, there are additional considerations, such as data sovereignty, in which data is subject to the laws and regulations of the country in which it’s stored or processed.
Beyond this, the old adage of finding the right tool for the job remains as important today as it ever was. The question now is: Where should AI actually run?
Is it AI Appropriate?
Instead of adopting a blanket cloud-first approach, many IT leaders are shifting toward use-case-appropriate infrastructure, onboarding options most suitable to the workload, often running several in parallel.
Leading this change are the unpredictability of egress fees for moving data out of a cloud provider's network, networking costs, governance requirements, and storage costs. It's not just the costs associated with AI training; AI inference workloads require vast cloud computing resources, and this surging demand has driven up GPU prices. While cloud providers are absorbing many of these costs, customers completing tasks such as lengthy AI model training might choose to diversify their data storage to reduce the financial burden.
And then there's the growing demand for data sovereignty.
Regulatory pressure around data residency and on-site governance is escalating globally. Several companies have developed sovereign cloud solutions to address these issues, but some IT leaders prefer to keep tighter control over sensitive data in-house. However, there is no definitive answer. It's all about awareness, knowing the benefits of different approaches, and weighing up those benefits to suit the use case.
Full Circle
While the cloud revolution certainly upended the status quo by shifting services away from on-premises infrastructure, the upcoming AI revolution is turning the tables again, leading companies to seek more diverse setups.
These alternatives include private data centres, colocation, and hybrid cloud architectures. Today, many organisations are matching workloads to the most suitable environment. For example, while a public cloud is a great choice for speed and quick deployment, private cloud infrastructure excels in predictability and control.
Some companies are opting to return to private data centres. Here, it is often much easier to predict costs, as infrastructure, such as compute and GPUs, storage and networking, can be dedicated solely to the organisation's needs. Hardware can be optimised for specific models, and in some cases, private data might be the easiest way to ensure data sovereignty. However, companies don't have to bear the brunt of building out a data centre from scratch.
Colocation is another option, where companies own their servers and hardware but lease space, power, and cooling in a data centre owned by another company. This has become one of the most viable options for businesses seeking the security, governance, and sovereignty of a private data centre without the massive investment required to build their own.
The fundamental rule of matching infrastructure to workload requirements has taken on a new level of complexity. Driven by AI demands, data sovereignty, and fluctuating cloud costs, businesses are moving away from one-size-fits-all strategies.
Navigating this landscape requires a precise, hybrid mix of public cloud, private infrastructure, and colocation tailored to balance cost, privacy, performance, and governance. Achieving this level of precision, however, is no simple feat.
Ultimately, IT leaders need to go back to the roots of their digital transformation effort, an effort often centred on cloud architecture and assess where it is and where it isn't appropriate for increasingly complex AI workloads.
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