Why AI Needs a Different Kind of Cloud

AI is changing how enterprises design cloud infrastructure, but this trend is not necessarily about moving away from general-purpose cloud. Instead, enterprises are beginning to place workloads according to their compute intensity, latency tolerance, data sensitivity, governance needs and operating patterns.

“Neysa’s view is that this shift is creating a specialised AI infrastructure layer alongside traditional cloud estates.” 

ERP, CRM, databases, data pipelines, and applications deeply tied to managed cloud services may remain on general-purpose cloud infrastructure. Training, fine-tuning, and sustained production inference, however, have different infrastructure requirements and may benefit from AI-native environments.

“Enterprises are not leaving the cloud. They are recognising that AI workloads have different physics.”

The distinction is becoming more relevant as enterprises move AI from pilots into production. Gartner expects worldwide spending on AI-optimized infrastructure as a service (IaaS) to grow 96% through 2026, reaching $42 billion.

“This growth is driven by continued demand for infrastructure to support large language model training and the rapid operationalisation of AI across enterprise applications and workflows,” said Hardeep Singh, Senior Principal Research Analyst at Gartner.

Training Started the Shift; Inference Could Make it Permanent

Training exposed the infrastructure gap first. Inference turns it into a day-to-day operating challenge.

Training was the first workload to expose the limits of general-purpose infrastructure because it needs large clusters of accelerators, high-speed networking and specialised storage. At scale, cluster interconnect, scheduling efficiency, storage throughput, and failure recovery influence both time to train and the effective cost of each run.

Inference presents a different operating problem. Unlike training, it is triggered by application use and often needs to meet latency and availability expectations. For agentic systems, copilots, and customer-facing applications, demand can be continuous, variable, and difficult to predict.

Deloitte’s 2026 Tech Trends report supports the case for a more hybrid approach to AI infrastructure, highlighting how rising inference costs, scalability demands and latency requirements are forcing enterprises to rethink how and where AI workloads are run.

“For many enterprises, the operational expense of AI has become a catalyst for change,” the company said in its report.

It added that organisations are discovering their existing infrastructure strategies aren’t designed to scale AI to production-scale deployment. 

The emerging model is increasingly hybrid, combining elastic cloud capacity for experimentation and training, dedicated infrastructure for sustained workloads, and edge processing for applications where low latency is critical.

“Training made the cost visible. Production inference makes the operating model visible,” Neysa, a full-stack AI acceleration cloud, says.

That changes the economics of infrastructure. The relevant metric is no longer simply the hourly price of a GPU. Enterprises need to consider tokens generated per rupee, power consumption, latency, utilisation and the cost of keeping capacity available. The right metric varies by workload: cost per training run, time to train, cost per million tokens, latency at a target throughput, utilisation, and the cost of reserved but idle capacity.

For short-lived experimentation, burst demand, or low-volume inference, general-purpose cloud may remain the simpler and more economical choice. Specialised AI infrastructure becomes more compelling when demand is sustained, performance-sensitive, regulated, or expensive to run inefficiently.

Fine-Tuning Turns Model Weights into Enterprise IP

Fine-tuning sits between training and inference, but its implications extend beyond compute. When an enterprise fine-tunes a model using proprietary information, the resulting weights can become an important piece of intellectual property. That raises questions about where those weights are created, where they are stored and who has access to them.

For many enterprises, the question is not only where data is stored. It is whether they can trace which dataset, base model, code version, evaluation process and access policy produced a deployable model artefact.

“Once you fine-tune on proprietary data, the resulting model weights become enterprise IP. Most organisations have not yet decided where that IP should live, who can access it, or how it will be governed.” Neysa says.

The infrastructure decision therefore becomes tied to governance. Enterprises need visibility into model versions, data access, model artefacts and production responses, rather than simply knowing where a GPU is located.

Production AI platforms commonly require GPU scheduling, observability, model versioning, controlled rollouts, tenant isolation, and policy controls.

Sovereignty Goes Beyond Data Residency

India adds another dimension to the infrastructure debate.

Keeping data in an Indian data centre addresses location. Sovereignty is broader: it concerns who operates the infrastructure, who controls administrative access, where the control plane sits, which jurisdiction applies, and whether an enterprise can govern its data, models, logs, and spend.

“Residency is a pin on a map. Sovereignty is control over data, models and infrastructure,” Neysa says.

The distinction is becoming relevant as India expands domestic AI capacity. The government announced plans in February to add 20,000 GPUs to the more than 38,000 already available under its AI infrastructure programme, according to the Ministry of Electronics and IT release published by the Press Information Bureau. 

Organisations are already moving beyond localisation. BHASHINI, for instance, moved its AI workloads to an Indian neo cloud and GPU infrastructure, keeping its datasets, models and citizen interactions within India. Such deployments illustrate why location, infrastructure control, and governance increasingly enter AI architecture decisions.

Not every AI workload needs sovereign infrastructure. The appropriate placement depends on regulatory obligations, latency, data sensitivity, expected utilization, model IP, integration dependencies, and the organization’s own risk appetite.

The Hard Part Is Governing Work Across Environments.

This makes hybrid infrastructure less of a compromise and more of an architectural model.

General-purpose cloud can continue to serve systems of record, data services, applications with deep managed-service dependencies, and bursty experimentation. Specialised AI infrastructure is most relevant for sustained workloads where accelerator density, latency, utilisation, model governance, or India-resident operations materially affect outcomes.

The difficult part is coordinating the two. Running a hybrid estate is not simply a procurement decision. Teams need to govern identity, data access, model versions, routing, observability, policy, cost allocation, and incident response across environments.

The inference and orchestration layer can determine which model handles a request, what data it can access, where the request is processed and what gets logged. Industry reference architectures increasingly treat routing, tenant isolation, lifecycle management, telemetry, and security as connected operating concerns.

“Hybrid is easy to announce. It is hard to operate. The control layer determines where work runs, what data it can access, how it is governed, and who remains accountable,” Neysa says.

For enterprise buyers, the question is therefore shifting from which cloud to use to which workload belongs where.

That decision will increasingly depend on six questions: How compute-intensive is the workload? How sensitive is the data and model IP? What latency and availability does it require? Is demand bursty or sustained? What managed-service dependencies exist? Who needs operational and administrative control?

The future is unlikely to be one cloud replacing another. It is an architecture in which each environment performs the job it is best suited for. The enterprise advantage will come from placing workloads deliberately, then retaining control over the data, models, performance, and economics that matter.”

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