For the last decade, most organizations have treated cloud infrastructure as a solved problem. You picked a hyperscaler, built your architecture around its core services, and let the platform’s service catalog expand around you. If you needed something specialized, you bolted on a smaller provider at the edge.

That model worked when the cloud’s job was mostly to run general-purpose workloads, such as web tiers, internal apps, analytics pipelines, databases, and development environments. But AI changes the whole equation.

Enterprises are realizing that the old cloud model can’t run production AI in a way that stays governable as the system grows. That’s why a new provider category is emerging: the alternative hyperscaler.

Hyperscalers are optimized for a different era

The hyperscalers built the modern cloud. They created the expectation of global availability, elastic compute, managed services, and near-infinite scale. That is still valuable. However, the AI era has created a mismatch between what hyperscalers are optimized for and what enterprises now need.

AI workloads don’t behave like traditional cloud compute. They don’t scale cleanly through short bursts of elasticity. AI training and inference require sustained access to specialized hardware, and they need cost structures that can be forecasted over time.

In hyperscaler environments, those costs are often hard to predict because you’re not just paying for GPUs; you’re paying for the layers around them, such as data movement, networking, storage, monitoring, logging, security, orchestration, and governance. Each layer has its own pricing model.

At enterprise scale, cost predictability becomes a constraint. And it’s only the beginning. AI also raises the stakes on vendor lock-in and makes governance harder to enforce across regions.

AI makes lock-in more expensive

Vendor lock-in has always been part of cloud computing, but it’s much more expensive in the AI era.

AI systems evolve much faster than traditional application stacks. Models are replaced within quarters and governance requirements shift continuously. At the same time, entirely new architectural layers (agents, retrieval systems, evaluation pipelines, etc.) can emerge in months, not years.

The pace of change in AI means enterprises must be able to swap components without rebuilding the entire system beneath them. Yet hyperscaler roadmaps increasingly emphasize vertical integration. Services are designed to work best inside a single ecosystem, which creates dependency.

The more AI systems rely on these proprietary layers, the harder it will be for them to evolve without having to replatform. And enterprises are responding accordingly. IDC projects that 75% of enterprise AI workloads will be deployed on hybrid, fit-for-purpose infrastructure by 2028 because organizations will need alternatives to one-size-fits-all cloud strategies.

AI scales globally. Governance doesn’t.

The other major issue hyperscalers struggle with is governance. As AI moves into production, it operates across jurisdictions with different requirements for privacy, residency, accountability, and auditability.

Enterprises therefore have to run AI in a way that can be enforced locally and explained globally. But that becomes difficult when platforms treat regions as availability zones rather than governance boundaries, or when service boundaries and data flows are too opaque to support consistent enforcement.

The risks are not hypothetical. Gartner even predicts AI regulatory violations will drive a 30% increase in legal disputes for tech companies by 2028. As scrutiny rises, infrastructure choices become legal choices.

So why not just use niche AI providers?

Over the last two years, a wave of specialized providers has emerged. They offer everything from GPU access and inference acceleration to verticalized platforms and managed orchestration layers. These services can be useful, especially early in the lifecycle.

But the moment AI becomes production-critical, the weakness of the niche model shows up: integration.

In production, AI has to fit into the rest of the enterprise. That includes networking, storage, monitoring, security, governance, procurement, support, and consistent operations across teams and regions. A specialized provider can solve one part of that environment, but not the whole.

As a result, enterprises often find themselves caught between two extremes. On one side are hyperscalers that can feel rigid and vertically integrated. On the other are niche providers that solve narrow problems but leave the integration burden to the customer.

The alternative hyperscaler fills this structural gap.

A new cloud category emerges

Alternative hyperscalers offer full public cloud capability – compute, storage, networking, and global availability – while also delivering AI-native infrastructure and an open, composable operating model. The defining feature is that the platform is built for end-to-end production AI, without forcing enterprises into a vertically integrated stack.

In practice, alternative hyperscalers:

• Behave like a real public cloud. Teams can run their full environment, not just an AI experiment.
• Treat AI as first-class infrastructure. GPU-backed compute, inference support, and modern workflows are core, not add-ons.
• Stay composable. Enterprises can choose models, frameworks, and orchestration layers without being trapped in a proprietary ecosystem.
• Make costs predictable. Transparent pricing and operational visibility enable cost governance at scale.

Why 2026 is the turning point

This shift has been building for several years, but 2026 is the point where it becomes hard to ignore.

• AI workloads are moving decisively into production. That means longer commitments, higher reliability expectations, and a demand for operational maturity.
• GPU economics are reshaping who can compete. AI infrastructure is capital-intensive, and sustained global capacity isn’t something every provider can offer.
• Multicloud is becoming table stakes. Enterprises are reducing dependence on a single platform for resilience, cost control, and regulatory compliance. AI accelerates this trend by raising both financial exposure and governance risk.

The alternative hyperscaler category is no longer theoretical. In 2026, enterprises will either adopt cloud platforms built for production AI or continue forcing AI onto infrastructure that was never designed for it. The difference will show up quickly in cost, control, and speed.

For a more in-depth look at the rise of alternative hyperscalers, read the full report.