Every enterprise AI programme reaches the same fork. The data science team wants GPUs under the desk, where iteration is instant and nothing sensitive leaves the building. Finance wants the hyperscaler contract the company already has. And somewhere in between sits a newer option most Indian enterprises are only now evaluating seriously: the neo cloud, a class of provider built for one job — running AI workloads on dense, current-generation GPU infrastructure. At L&T Vyoma, we operate in that third category, and we’ll be direct about our view: the question “where should our AI run?” has no single answer. It is a portfolio decision. But for the workloads that decide whether an AI programme ships — training, fine-tuning, and production inference — the centre of gravity is moving to the neo cloud, and in India it is moving to the sovereign kind. Our AI Factory is built for exactly that.
Three Tiers, Three Different Jobs
Strip away the vendor noise and an enterprise has three places to put an AI workload: a workstation it owns, a global hyperscaler region, or a specialised GPU cloud. None of them is wrong. Each is wrong for something.
The market is already voting on where the growth sits. Gartner projects that neo cloud providers will capture 20% of a USD 267 billion AI cloud market by 2030 (Gartner), and neo cloud revenues hit USD 9 billion in the fourth quarter of 2025 alone, up 223% year over year (Synergy Research Group). Enterprises are not abandoning hyperscalers. They are unbundling AI from them.
The Workstation Under the Desk
Local hardware earns its place. For early prototyping, a capable GPU workstation gives a data scientist the fastest possible loop: no queue, no network hop, no per-hour meter running. It also keeps genuinely sensitive experiments inside the building, which for some proofs of concept is the whole point.
Then the ceiling arrives, and it arrives early. A workstation holds a few GPUs; a serious fine-tune wants dozens and a frontier training run wants thousands. Memory runs out before ambition does. The machine that felt fast in week one becomes the bottleneck by month three, and the capital sits idle between runs. Local hardware is where enterprise AI should start. It is rarely where it can scale.
The Hyperscaler Default
Global hyperscalers remain superb at what they were built for: breadth. If your application is welded to a particular cloud’s managed services, running its AI components nearby is a defensible choice, and the biggest platforms offer GPU capacity that few can match in absolute depth.
The friction shows up in the specifics. Current-generation GPU capacity is often queued or region-locked. Costs compound quietly — egress fees, ecosystem services billed around the GPU, pricing set in dollars. And for Indian enterprises there is a harder problem underneath: jurisdiction. Data in a foreign-operated region answers to foreign law, whatever the contract says. For BFSI workloads under RBI’s payment-data localisation mandate, for health records under the DPDP Act and ABDM, and for anything touching defence or the public sector, that exposure is not a detail. It is frequently disqualifying.
The Neo Cloud: Purpose-Built for the Work That Matters
A neo cloud does one thing: AI infrastructure, delivered as a service, on the newest silicon, without the ecosystem wrapped around it. Ours runs on the fleet that modern workloads actually ask for. NVIDIA Blackwell B200 systems carry 192 GB of HBM3e for frontier-scale training, and at rack scale the GB200 NVL72 binds 72 GPUs into one liquid-cooled domain. Hopper H200s, with 141 GB of HBM3e, carry high-throughput training and large-model inference on a mature software stack. For inference, visualisation and agentic workloads, the RTX PRO 6000 Blackwell Server Edition pairs 96 GB of GDDR7 with MIG partitioning into up to four isolated instances per card. All of it sits in Tier III facilities engineered for beyond 100 kW per rack, liquid-cooled because at that density air stops working.
The consumption model is the other half. A team sizes a cluster for a training run and releases it when the run ends. No 6–12 month procurement queue, no stranded capex, and the Cloud Calculator shows the spend before a rupee is committed. This is what the neo cloud category was invented to do, and it is why the analysts’ growth numbers look the way they do.
The India Layer: Why Sovereign Changes the Answer
For an Indian enterprise, the tier comparison has a dimension the global analysis misses. Our infrastructure runs in L&T-operated campuses in Mumbai and Chennai, so data residency and DPDP-aligned compliance are properties of the architecture, not clauses in a contract. A bank can train fraud models on payment data without that data crossing a border. A hospital network can run diagnostic imaging AI inside Indian jurisdiction. A public-sector programme can build on sovereign compute with no foreign-jurisdiction exposure at all. The hyperscalers cannot offer this by construction; a workstation offers it but cannot scale. The sovereign neo cloud is the only tier that does both.
A Decision Framework That Holds Up
So where should enterprise AI actually run? Match the workload, not the vendor pitch. Prototype locally, where iteration speed and privacy matter most and scale doesn’t. Keep ecosystem-bound applications near their cloud, because moving them costs more than it saves. And put the heavy, recurring work — training, fine-tuning, batch and production inference — on a neo cloud, where the GPUs are current, the pricing is transparent, and the capacity releases when the job ends. For regulated Indian enterprises, add the sovereignty filter on top, and the neo cloud tier narrows to the kind built on Indian soil.
The Three Tiers Side by Side
|
Dimension |
Local Workstation |
Global Hyperscaler |
Sovereign Neo Cloud (Vyoma) |
|
Best for |
Prototyping, small fine-tunes, sensitive PoCs |
Global apps tied to one ecosystem |
Training, fine-tuning and inference at scale |
|
GPU ceiling |
A few cards per box |
Deep, but often queued |
Blackwell, Hopper & RTX fleets on demand |
|
Cost model |
Capex, idle between runs |
Opex, plus egress and ecosystem costs |
Opex, pay-as-you-go, priced in INR terms |
|
Elasticity |
None |
High |
High — scale up for a run, release after |
|
Data location |
On premises |
Foreign jurisdiction, varies by region |
Indian soil, DPDP-aligned |
|
Time to capacity |
Weeks to procure |
Instant to months, region-dependent |
Provisioned as a service |
|
Ops burden |
Yours entirely |
Shared, complex billing |
Managed, with local support |
Run It Where It Belongs
The honest answer to “workstation, hyperscaler or neo cloud?” is: yes, in that order of scale, with the neo cloud carrying the load that determines whether your AI programme actually reaches production. That is the tier we built. Explore the AI Factory, model a cluster on the Cloud Calculator, or talk to our team about where your workloads belong.