For the last decade, the story has been simple: if you’re serious about cloud, you pick a hyperscaler and go all-in. These providers became the pillars for scaling any enterprise workload.
But AI has changed the rules.
Despite the prevailing narrative, many enterprises are now rethinking a hyperscaler-only approach. Concerns over data sovereignty, unpredictable costs, and access to resources are forcing organisations to diversify. 95% of UK IT leaders are concerned about data sovereignty, and more than half are working to reduce their reliance on US public cloud providers.
The biggest pain point is access to compute power. Performance bottlenecks are slowing projects. Compliance demands are raising red flags across the C-Suite. And when your multimillion-pound AI programme is waiting in a queue for compute, you stop caring about cloud branding and start caring about results.
The cracks in the “hyperscaler for everything” model
Hyperscalers built their infrastructure for the broadest possible customer base. That’s great for general workloads, but AI is a different animal.
Training large language models and running high-volume inferencing isn’t just another app deployment. It’s high-density, high-throughput, high-stakes computing. If you don’t have guaranteed GPU availability, you miss product deadlines. If your data residency controls are vague, you risk non-compliance. And if your cost model spikes unpredictably during model training, your CFO will notice.
Regulation adds another layer of pressure. Healthcare, engineering, public sector and financial services AI projects can’t afford to figure out the data location later. If you can’t guarantee exactly where the data sits, you can’t meet GDPR standards. The compliance clock doesn’t stop ticking because your provider is juggling billions of other workloads at the same time as yours.
Why AI-first platforms are winning boardroom approval
Enter the independent providers. These are platforms purpose-built for such workloads, designed without the baggage of retrofitted general-purpose infrastructure.
They offer dense GPU clusters optimised for AI training, ultra-low-latency, and, crucially, the transparency to show you exactly where your workloads run. That’s not a “nice to have” – it’s the difference between your CCO passing and failing a compliance audit.
They’re also vendor-neutral. With hyperscalers, the deeper you go into their proprietary tools, the harder it is to leave. Lock-in might seem fine when you’re scaling fast, but it becomes a risk when pricing changes, capacity tightens, or regulations shift. AI-first providers tend to lean on open source and open standards, giving enterprises the freedom to adapt their tech stacks as needs evolve.
And yes, performance matters. AI leaders know that shaving hours or even minutes off training cycles can mean hitting the market before your competitor. AI-first platforms optimise for throughput in ways generalist providers simply can’t prioritise at scale.
The boardroom pitch isn’t hard: predictable performance, compliance confidence, and flexibility without lock-in. In the current market, that’s an easy “yes.”
The future is hybrid, but not in the way you think
The old definition of hybrid cloud was “some on-prem, some hyperscaler.” In the AI era, it’s evolving into “some hyperscaler, some AI-first specialist.” That’s a healthier, more strategic balance.
If you want to compete in the AI arena, you need infrastructure partners who live and breathe AI workloads. Enterprises are realising that the path to AI success isn’t paved by loyalty to one vendor, but by assembling a best-of-breed stack. That stack will be multi-cloud by design and flexible enough to change as the markets and regulations inevitably shift to more secure frameworks.
The companies acting on this now have the performative edge, the compliance headroom, and predictable costs to innovate without fear. Those who cling to a hyperscaler-only model may find themselves waiting in a very long GPU queue while their competitors race ahead, bringing new products to market.
The choice is simple: build your AI future on infrastructure optimised for yesterday’s workloads or assemble a platform strategy that’s purpose-built for tomorrow’s breakthroughs. The C-Suite has already decided.
About the Author
Kevin Cochrane is Chief Marketing Officer at Vultr. A 25+ year pioneer of the digital experience space, Kevin has co-founded and led multiple category-defining companies. At Vultr, he is building the brand’s global presence as a leader in the independent cloud platform market, helping organisations worldwide unlock the full potential of AI and composable infrastructure.
