Updated: June 30, 2026
Most enterprises assume serious AI means the public cloud. The economics say otherwise. Once a GPU cluster runs above roughly 60% sustained utilisation, owning it tends to cost less than renting it, and the gap widens every month the hardware keeps earning. For the steady training and inference workloads that move from pilot to production, that is exactly the pattern you end up with.
Proactive Data Systems designs, builds and runs AI infrastructure for Indian enterprises, on-premises, in hybrid models, and as sovereign or private AI. 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. We build the AI stack on NVIDIA-accelerated servers from Dell, HPE, Cisco and Lenovo, with the storage and fabric to match, sized to your model and kept inside your control.
AI infrastructure, often called an AI factory, is the stack built to train and run AI models at scale. It pairs GPU-accelerated compute with storage fast enough to feed the GPUs, low-latency networking between them, and the power and cooling density dense racks demand. It is a specialised tier, not a server with a graphics card added, and it is what separates a data center that runs business applications from one that can run real AI workloads.
The national direction tells you how fast this is arriving. India's public AI compute pool has already crossed 38,000 GPUs and is targeting 100,000 by the end of 2026. The capacity is being built. The question for your enterprise is where yours should live.
It depends on three things: how sensitive your data is, how steady your workloads are, and how predictable you need the cost to be. On-premises gives control, data residency and falling unit cost for sustained work. Public cloud suits bursty or experimental workloads. GPU-as-a-Service sits between them. Many enterprises run a blend, and the job is to size the mix to the workload rather than to a default.
| Model | Best for | Cost behaviour | Data control |
|---|---|---|---|
| On-premises | Steady, high-utilisation training and inference; sensitive or regulated data | High up-front, falling unit cost as hardware amortises | Full; data and models stay in your boundary |
| Hybrid | A predictable base on-prem, with cloud for spikes | Mixed; you own the floor, rent the peaks | Strong for the on-prem base |
| GPU-as-a-Service | Bursty, seasonal or experimental work | Pay-as-you-go; no efficiency payoff over time | Depends on provider and location |
The honest recommendation: put the steady base where it runs cheapest and stays compliant, and keep the cloud for what genuinely flexes. Where does your AI workload actually sit on that line, today and a year from now?
Four things, designed as one system. GPU-accelerated compute, on NVIDIA-accelerated servers from Dell, HPE, Cisco and Lenovo. Storage with the throughput to keep those GPUs fed rather than idling. A low-latency east-west fabric so the cluster scales instead of choking. And the power and cooling, increasingly liquid cooling, to carry rack densities that have climbed well past what air cooling alone can handle.
Get one layer wrong and the expensive layer suffers. A GPU cluster fed by slow storage is a fast car in a traffic jam. We design the four together, then confirm the facility can power and cool what you are about to rack, before anything is installed.
You start from the model, not the catalogue. The size of the models you intend to train or serve sets the GPU count; the GPU count sets the storage throughput and network bandwidth needed to keep them busy; and all of that sets the power and cooling envelope. Size from the workload outward and you buy what you need. Size from a vendor's reference rack inward and you tend to over-buy the hardware and under-build the facility.
Proactive sizes the GPUs to the model, the storage so it will not starve them, the fabric so the cluster scales, and then checks the building can carry it. For an existing data center, we make it AI-ready in stages, not a forklift rebuild.
A GPU server is easy to buy and easy to strand. The harder, more valuable work is the design and the operations around it, and that is where a lifecycle partner earns its place over a reseller.
Proactive is multi-OEM by design, so the platform is chosen for the workload, not a quota. We design, build, migrate and manage as one lifecycle. Our credentials are independently held, Cisco Preferred Cloud and AI Partner, Dell Platinum Partner, NetApp Preferred Partner, ISO 9001:2015 certified, and our support is local, with a 24/7 service desk on 1800 202 6711. For enterprises with data-residency obligations, we deliver sovereign or private AI that keeps training and inference inside your borders.
Tell us the models you intend to run and the workloads behind them, and we will size the GPUs, storage, fabric and power to them. Ask us for an AI-readiness assessment. Write to [email protected]
Disclaimer: Cost and break-even figures on this page are general guidance, not a quote or a financial projection for your environment. Actual economics depend on your workloads, utilisation, electricity and facility costs. Obtain a formal assessment before committing budget.
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