The private cloud returns, for AI workloads

A North American manufacturer spent most of 2024 and early 2025 doing what many innovative enterprises did: aggressively standardizing on the public cloud by using data lakes, analytics, CI/CD, and even a good chunk of ERP integration. The board liked the narrative because it sounded like simplification, and simplification sounded like savings. Then generative AI arrived, not as a lab toy but as a mandate. “Put copilots everywhere,” leadership said. “Start with maintenance, then procurement, then the call center, then engineering change orders.”

The first pilot went live quickly using a managed model endpoint and a retrieval layer in the same public cloud region as their data platform. It worked and everyone cheered. Then invoices started arriving. Token usage, vector storage, accelerated compute, egress for integration flows, premium logging, premium guardrails. Meanwhile, a series of cloud service disruptions forced the team into uncomfortable conversations about blast radius, dependency chains, and what “high availability” really means when your application is a tapestry of managed services.

The final straw wasn’t just cost or downtime; it was proximity. The most valuable AI use cases were those closest to people who build and fix things. Those people lived near manufacturing plants with strict network boundaries, latency constraints, and operational rhythms that don’t tolerate “the provider is investigating.” Within six months, the company began shifting its AI inference and retrieval workloads to a private cloud located near its factories, while keeping model training bursts in the public cloud when it made sense. It wasn’t a retreat. It was a rebalancing.

AI changed the math

For a decade, private cloud was often framed as a stepping-stone or, worse, a polite way to describe legacy virtualization with a portal. In 2026, AI is forcing a more serious reappraisal. Not because public cloud suddenly stopped working, but because the workload profile of AI is different from the workload profile of “move my app server and my database.”

AI workloads are spiky, GPU-hungry, and brutally sensitive to inefficient architecture. They also tend to multiply. A single assistant becomes dozens of specialized agents. A single model becomes an ensemble. A single department becomes every department. AI spreads because the marginal utility of another use case is high, but the marginal cost can be even higher if you don’t control the fundamentals.

Enterprises are noticing that the promise of elasticity is not the same thing as cost control. Yes, public cloud can scale on demand. But AI often scales and stays scaled because the business immediately learns to depend on it. Once a copilot is embedded into an intake workflow, a quality inspection process, or a claims pipeline, turning it off is not a realistic lever. That’s when predictable capacity, amortized over time, becomes financially attractive again.

Cost is no longer a rounding error

AI economics are exposing a gap between what people think the cloud costs and what the cloud actually costs. When you run traditional systems, you can hide inefficiencies behind reserved instances, right-sizing tools, and a few architectural tweaks. With AI, waste has sharp edges. Overprovision GPUs and you burn money. Underprovision and your users experience delays that make the system feel broken. Keep everything in a premium managed stack, and you may pay for convenience forever with little ability to negotiate the unit economics.

Private clouds are attractive here for a simple reason: Enterprises can choose where to standardize and where to differentiate. They can invest in a consistent GPU platform for inference, cache frequently used embeddings locally, and reduce the constant tax of per-request pricing. They can still use public cloud for experimentation and burst training, but they don’t have to treat every inference call like a metered microtransaction.

Outages are changing risk discussions

Most enterprises know complex systems fail. The outages in 2025 did not show that cloud is unreliable, but they did reveal that relying on many interconnected services leads to correlated failure. When your AI experience depends on identity services, model endpoints, vector databases, event streaming, observability pipelines, and network interconnects, your uptime is the product of many moving parts. The more composable the architecture, the more failure points.

Private cloud won’t magically eliminate outages, but it does shrink the dependency surface area and give teams more control over change management. Enterprises that run AI close to core processes often prefer controlled upgrades, conservative patching windows, and the ability to isolate failures to a smaller domain. That’s not nostalgia; it’s operational maturity.

Proximity matters

The most important driver I’m seeing in 2026 is the desire to keep AI systems close to the processes and people who use them. That means low-latency access to operational data, tight integration with Internet of Things and edge environments, and governance that aligns with how work actually happens. A chatbot in a browser is easy. An AI system that helps a technician diagnose a machine in real time on a constrained network is a different game.

There’s also a data gravity issue that rarely receives the attention it deserves. AI systems don’t just read data; they generate it. Feedback loops, human ratings, exception handling, and audit trails become first-class assets. Keeping those loops close to the business domains that own them reduces friction and improves accountability. When AI becomes a daily instrument panel for the enterprise, architecture must serve the operators, not just the developers.

Five steps for private cloud AI

First, treat unit economics as a design requirement, not a postmortem. Model the cost per transaction, per employee, or per workflow step, and decide which are fixed costs and which are variable, because AI that works but is unaffordable at scale is just a demo with better lighting.

Second, design for resilience by reducing dependency chains and clarifying failure domains. A private cloud can help, but only if you deliberately choose fewer, more reliable components, build sensible fallbacks, and test degraded modes so the business can keep moving when a component fails.

Third, plan for data locality and the feedback loop as carefully as you plan for compute. Your retrieval layer, embedding life cycle, fine-tuning data sets, and audit logs will become strategic assets; place them where you can govern, secure, and access them with minimal friction across the teams that improve the system.

Fourth, treat GPUs and accelerators as a shared enterprise platform with precise scheduling, quotas, and chargeback policies. If you don’t operationalize accelerator capacity, it will be captured by the teams who are the loudest but not necessarily the most critical. The resulting chaos will appear to be a technology problem when it’s really a governance problem.

Fifth, make security and compliance practical for builders, not performative for documents. That means identity boundaries that align with real roles, automated policy enforcement in pipelines, strong isolation for sensitive workloads, and a risk management approach that recognizes that AI is software but also something new: software that talks, recommends, and occasionally hallucinates.