Three Ways to Run AI. Which Is Yours?

Should You Run AI On-Prem, in the Cloud, or via GPU-as-a-Service? 

Three ways to run AI, and three different right answers. The mistake is picking one as a default for everything. The honest decision depends on two questions about each workload: how steady is it, and how sensitive is its data? Answer those and the path usually picks itself. 

Should You Run AI On-Prem, in the Cloud, or via GPU-as-a-Service? 

In short: cloud for bursty and experimental work, GPU-as-a-Service for variable workloads you want to own less of, and on-premise for steady, high-utilisation or sensitive workloads. Cloud and GPU-as-a-Service let you start fast and pay as you go; on-premise costs more up front but less over time once usage is sustained, and keeps data inside your control. Most enterprises end up running a blend. 

When Does Cloud AI Make Sense? 

When the work is bursty, experimental, or moving faster than a procurement cycle. The cloud lets you start in hours, scale up and down, and pay only for what you use, so you avoid sinking capital into hardware that might sit idle. For prototyping, occasional training and unpredictable demand, it is usually the right and cheapest choice. The trade-offs are cost that climbs as usage becomes constant, and data that leaves your environment. 

When Does GPU-as-a-Service Make Sense? 

When you want owned-style capacity without building or running a facility, and your demand varies. GPU-as-a-Service rents you GPU capacity by the hour, sitting between the flexibility of cloud and the control of on-premise. It suits enterprises scaling up AI before they know their steady-state demand, or those without the power and cooling to host dense GPU hardware. The considerations are cost at sustained scale, and data residency, which depends on the provider and region. 

When Does On-Premise AI Make Sense? 

When workloads are steady and sustained, or the data is sensitive enough to require control. Once a GPU cluster runs at high utilisation, owning it tends to cost less than renting, because the hardware amortises while pay-as-you-go pricing does not. On-premise also keeps data and models inside your boundary, which matters for regulated workloads. The constraints are the up-front cost and the need for a facility that can power and cool dense GPU racks. 

What About Data Residency? 

For Indian enterprises, residency can override the cost calculation. If a workload touches personal, financial or regulated data, India's DPDP framework and sector rules such as RBI's can make where it runs a compliance question, not just an economic one. In those cases on-premise or a sovereign deployment that keeps data in-country may be the right choice even when cloud or GPU-as-a-Service looks marginally cheaper. The saving rarely justifies a residency exposure. 

How Do You Decide? 

Classify each workload by how steady its utilisation is and how sensitive its data is, then place it where it fits: bursty and non-sensitive to cloud or GPU-as-a-Service, steady and sensitive to on-premise or sovereign. Most enterprises run a hybrid, owning the steady, sensitive base and renting the rest. For the cost side specifically, model it on your own numbers over three years rather than assuming, because the break-even depends on your utilisation. 

From Quick Answer to a Real Plan 

The quick decision gets you to the right shortlist; turning it into a costed, workload-by-workload plan is the next step. Proactive Data Systems designs AI infrastructure across on-premise, hybrid, GPU-as-a-Service and sovereign models for Indian enterprises, so the recommendation follows your workloads rather than a single answer. We are a Cisco Preferred Cloud and AI Partner, Dell Platinum Partner and NetApp Preferred Partner, with 35 years in enterprise IT, more than 1,500 organisations served, and a 24/7 service desk in India. To plan yours, you can ask Proactive for an AI hosting assessment.