India’s GPU Cloud Market Has Three Sellers and One Quiet Winner

The India GPU cloud market now runs on three names: Yotta, E2E Networks, and Neysa. Each sells the rented compute that trains and runs AI models for Indian banks, hospitals, telecoms, and startups, and each buys its chips from the same vendor, Nvidia. As Nvidia has begun packaging the software, the orchestration layer, and the reference design above those chips, the edges each of these three resellers once owned are getting harder to defend.

Take Gnani, a Bengaluru-based voice-AI startup whose speech models now handle over 30 million calls a day for enterprise customers such as Razorpay. When the product team says a telecom deal means another 256 H100s by Monday, procurement routes the request to whichever provider can ship the chips fastest.

If tomorrow our product team says, we just signed a telecom customer, and we need another 256 H100s by Monday, I honestly don't care if somebody else is 20% cheaper six weeks from now.

Three Clouds, One AI Compute Market

An ML engineer at Gnani broke the calculus down. Production traffic stays with Yotta, the biggest, most certified name in the space. Model retraining, which needs eight GPUs sitting next to each other on a fast interconnect for three uninterrupted days, goes to Neysa whenever capacity happens to be free that week. An engineer's afternoon experiment, like testing a new accent, lands on E2E Networks, India's first listed cloud provider, spun up through a self-serve portal in minutes.

Multiply that pattern across every bank, hospital, e-commerce platform, and AI startup in India and the shape of the market comes clear. Each of the three GPU clouds sells roughly the same silicon on roughly the same racks, and each tries to win customers on something other than the chip itself. Yotta leads on scale and certification. Neysa leads on a bundled software stack. E2E Networks leads on the lowest friction to spin something up.

The split survives because the three workloads are different. Production traffic cannot tolerate the failure an experimental sandbox would shrug off. Retraining bursts want co-located GPUs on a tight interconnect for a few days at a time. Each company picked one bet to lead with, and Nvidia's expanding stack is closing in on all three.

Yotta Built the Largest GPU Fleet First

Yotta went first and went largest. Its Shakti Cloud platform runs on NVIDIA H100 Tensor Core GPUs, with more than 16,000 H100s in service today, according to a case study behind Shakti Cloud's H100 fleet. The same page describes Yotta as India's first NVIDIA Cloud Partner (NCP), a tier Nvidia reserves for providers that meet its reference architecture across hardware, software, and networking. Five cloud providers worldwide hold that designation today. Among them Yotta is selling the status directly into its enterprise and government pitch.

Sunil Gupta, Yotta's co-founder, MD, and CEO, said in an interview on India AI Summit GPU demand that Yotta now controls 60% to 70% of India's installed GPU capacity. The company is lining up a $1.2 billion to $1.5 billion pre-IPO round to keep buying chips. Gupta's framing was that Indian demand is now outrunning supply, and the local data center buildout will keep being a multi-year story.

• 16,000+ H100 GPUs in service on Shakti Cloud today
• 32,768 GPUs targeted for Yotta's Telangana AI infrastructure buildout
• NVIDIA Cloud Partner status, one of five reference platform NCPs globally
• Customer Sarvam AI runs large language models in 10 Indian languages on the Yotta stack

The pitch to enterprise and government buyers is sovereignty: Indian racks, Indian contracts, Indian rupee billing, with the Nvidia stack wired in through NVLink and Quantum-X800 InfiniBand so the local data never leaves. Yotta also takes advantage of NVIDIA Cloud Functions, the serverless inference API built on top of its GPU fleet, and runs Sarvam AI's multilingual LLMs inside its data center. The Nvidia AI Enterprise suite handles the orchestration layer under the hood. For buyers that need a sovereign-certifiable AI cloud on Indian soil, the package is hard to replicate.

Scale, certification, and the NCP designation are what Yotta leans on when two other sellers sit in the same conversation. Yotta has the largest GPU fleet in the country today and the largest single share of installed capacity. Gupta is using the pre-IPO round to push that lead wider ahead of a planned listing.

Neysa Raised $1.2 Billion for a Turnkey Stack

Neysa opened in 2023 under CEO Sharad Sanghi, and built Velocis as a single AI Acceleration Cloud system that wraps GPU-as-a-Service, a platform layer, inference, MLOps, and security into one product. Pricing on its Velocis GPU cloud pricing page claims up to 40% to 60% lower unit economics than global hyperscalers running the same GPUs. The product page also shows bare-metal clusters of L40S, H100, H200, and AMD MI300 silicon alongside on-demand VMs and commit plans running from one to thirty-six months. The differentiator is the software wrapping around the silicon.

The capital arrived in February 2026 in the definitive $1.2 billion financing agreement. Blackstone and co-investors committed up to $600 million in equity and unlocked an equal slice of debt, with participation from Teachers' Venture Growth, TVS Capital, 360 ONE Assets, and Nexus Venture Partners. The money funds deployment of more than 20,000 GPUs in India, putting Neysa on a path to roughly the same order of magnitude as Yotta's current H100 fleet. In a market where most Indian enterprises still don't have a dedicated machine-learning platform team, that scale lets Neysa pitch itself as the shortest path from a model idea to a working endpoint.

E2E Networks Sells the Quickest Spin-Up

E2E Networks is the smallest of the three by GPU count but owns a specific slot in the workflow. Its self-serve portal lets a developer click a few options and have an H100 running in minutes, with no procurement forms, sales calls, or paper contracts in between. Long-term contract or sales-team involvement only kicks in once a customer scales past a few hundred GPUs.

That speed matters most for experimental workloads, where the question is whether the model works at all. An afternoon accent test or a quick fine-tune usually lasts hours, not weeks, so waiting on a procurement cycle would eat the iteration budget. E2E's self-serve door shrinks the unit of work down to a few clicks and an instant H100 rental. Many AI labs in Bengaluru and Hyderabad run their first models on E2E before graduating production to a larger provider.

E2E's mid-tier position also plays well with India's startup-heavy GPU market. The same portal that serves an experiment is also the entry point for an early-stage seed-funded company that doesn't yet have a six-figure monthly GPU budget. That flywheel gives E2E a steady inflow of customers who later graduate upward, even though graduating customers eventually leave the E2E platform behind.

How Nvidia Owns a Layer in Every Rival's Stack

Nvidia stopped being just the chip seller years ago. The same company that builds the H100 also publishes the reference architecture that tells cloud providers how to rack, cool, and wire those GPUs. NVIDIA AI Enterprise packages production-grade containers and pretrained models that providers like Yotta and Neysa layer on top of their clouds.

Above the chips sit interconnect and orchestration layers that Nvidia also owns. NVLink and Quantum-X800 InfiniBand define the high-speed networking inside any serious training cluster. NVIDIA Cloud Functions, or NVCF, is a serverless API for running AI workloads directly on the GPU fleet. None of those layers come from Yotta, Neysa, or E2E.

• Reference architecture: the rack, cooling, and wiring blueprint a provider must follow to qualify as an NCP
• AI Enterprise software: production-grade containers and pretrained models any provider can license
• NVLink and Quantum-X800 InfiniBand: the high-speed interconnect inside and between GPU racks
• NVIDIA Cloud Functions: the serverless API sitting on top of the GPU fleet for inference at scale

The pattern is hard to miss. The H100 sits at the bottom of a stack Nvidia increasingly owns. Every provider's specialty is one layer above that, and Nvidia keeps adding tools in that layer too. So the sovereign-AI story comes down to who owns the building. The turnkey-stack story becomes a race to bundle Nvidia's own software the fastest. The self-serve-door story collapses into a UI on top of the same silicon.

Yotta's edge is its scale, its sovereign-AI label, and its NCP slot. Neysa's edge is its fresh capital and a turnkey Velocis product. E2E's edge is a frictionless portal and the longest listing age.

India's $277 Billion Data Center Pipeline

India's total data center capacity is on track to nearly double from 1.93 gigawatts in 2025 to about 4 gigawatts by 2028, per a Nomura report cited in the same February 2026 interview. Most of the $277 billion in investment announced at the India AI Summit will flow into data centers over the next five to seven years. U.S. hyperscalers like Google with a $15 billion southern India hub and Microsoft with a $17.5 billion expansion will absorb a chunk of that build.

If the GPU supply stays tight and Nvidia keeps layering software and design above the silicon, the three-way fight could narrow into one default incumbent plus a price war at the bottom. Yotta looks closest to that incumbent role today, with Neysa's fresh funding pointed in the same direction. E2E Networks' self-serve door and its listed balance sheet remain the most plausible counterweight, on the days when an Indian developer wants a GPU in three clicks and not three weeks. The market still looks like three sellers on paper, and the supplier above them increasingly sets the rules all three have to follow.

Frequently Asked Questions

What is India's GPU cloud market and who runs it?

The market is the segment of India's data-center industry that rents out AI-ready GPU compute to enterprises and startups. Three independent providers carry most of the load today: Yotta, E2E Networks, and Neysa. All three stock NVIDIA H100 GPUs and source them from the same vendor.

How did three providers end up splitting one market?

Each provider leaned into a different selling point. Yotta went first with the largest fleet and an NVIDIA Cloud Partner designation. Neysa launched as a turnkey AI Acceleration Cloud with bundled software. E2E Networks built the lowest-friction self-serve portal in the country.

Where does Nvidia fit in India's AI infrastructure story?

Nvidia supplies all three clouds and also publishes the reference architecture, the AI Enterprise software stack, NVLink, Quantum-X800 InfiniBand, and NVIDIA Cloud Functions. As Nvidia layers more software and design above the chip, the parts the three clouds once owned keep shifting.

Who is leading the India GPU cloud race right now?

Yotta, by capacity. Its Shakti Cloud operates more than 16,000 H100 GPUs, the largest fleet in the country. Neysa raised $1.2 billion in fresh capital and now plans to deploy more than 20,000 GPUs on the back of that funding.

Why do Indian AI startups often use more than one GPU cloud?

Because the workloads differ, the providers split. Production inference needs the most reliable provider. Retraining a model can demand a small cluster of tightly connected GPUs running for days on end. Experiments just need the cheapest way to spin something up. Indian startups usually run all three in parallel and route jobs based on which provider has the right capacity free in a given week.