India’s AI cloud market is crowded with multiple providers vying for the attention of startups, IITs, and enterprises. The IndiaAI Mission has empanelled over 34,000 GPUs, with another 6,000 on the way.
Around 72% of these GPUs have been allocated to startups building foundational models, providing a boost to the nation’s AI ambitions.
Yotta Data Services, NxtGen, E2E Networks, and others like Jio, CtrlS, Netmagic, Cyfuture, Sify, Vensysco, Locuz, and Ishan Infotech have carved their own slices of this GPU pie. But, Neysa is staking a distinct claim.
The Mumbai-based AI acceleration cloud system provider is focussed on the problem that most AI teams face: the AI trilemma, as its chief product officer Karan Kirpalani terms it.
At Cypher 2025, one of India’s largest AI conferences organised by AIM in Bengaluru, Kirpalani defined this trilemma: building a product with the right unit economics, speed to market, and product-market fit, all while scaling trust, which rarely works in practice.
“You can build a product at the right cost with speed to market but may fail to align with market needs, or any two of the other criteria. It’s the apartment problem. Pick any two, but you can’t have all three,” he said.
Traditional cloud providers — AWS, Google Cloud, Azure — can solve parts of the problem but rarely all three. “AWS will charge you four times what the prevalent market rate is for an H100 GPU. You get speed, yes, but you miss unit economics. You pivot the other way, buy your own GPUs, and now you’re stuck on speed and scale. No one has solved all three,” Kirpalani elaborated.
Enter Velocis
Velocis Cloud aims to tackle the trilemma. Unlike other providers focused on GPU allocation, Neysa delivers an end-to-end AI cloud platform. From Jupyter notebooks and containers to virtual machines and inference endpoints, everything is pre-integrated and accessible with a click on VelocisCloud.
Enterprises get flat-fee pricing, granular observability, and dedicated inference endpoints for models like OpenAI’s GPT-OSS, Meta’s Llama, Qwen, and Mistral. Startups get credit programs to avoid “project-killing” hyperscaler bills.
“Clients appreciate it more than GPUs. Bare metal, virtual machines, containers, Jupyter notebooks, inference endpoints — you can do all of it with a click, and at far better unit economics than hyperscalers,” Kirpalani said during a podcast at Cypher 2025.
Contrast that with Yotta. CEO Sunil Gupta has ordered 8,000 NVIDIA Blackwell GPUs to expand capacity for IndiaAI projects. Yotta already operates 8,000 H100s and 1,000 L40s, supporting Sarvam, Soket, and other large-scale AI models. “Most large-scale AI model development in India today is happening on Yotta’s infrastructure,” Gupta earlier told AIM.
Yotta’s strength is sheer scale, with a platform-as-a-service API layer for enterprise access. At the same time, Yotta also offers similar services, from training on bare metal hardware to deploying custom models and inference on its Shakti AI Cloud platform.
NxtGen takes a long-term, trust-driven approach to AI and cloud. Unlike Neysa, which focuses on end-to-end platform usability and flexibility, NxtGen leverages its legacy as one of India’s first cloud players and government contracts to build enterprise inference and sovereign AI at scale.
“The first difference is that we have a lot of trust with our customers,” CEO AS Rajgopal told AIM earlier, emphasising that NxtGen is not just providing GPUs but creating an enterprise-grade inference market with open-source, agentic AI platforms. Its philosophy blends early adoption, infrastructure investment, and operational sovereignty.
Standing Out
So where does Neysa fit in this crowded domain? It’s not about who has the most GPUs or the biggest contracts. It’s about usability, predictability, and sovereignty. Kirpalani emphasised India’s need to reduce dependency on foreign models and data centres.
“For India, investing across the stack and reducing dependency on foreign models, hardware, and data centres is vital,” he said. Neysa’s strategy is to offer variety — supporting multiple open-weights models — and control, ensuring enterprises can fine-tune, self-host, and manage token performance without surprises.
Hardware scale is a consideration, but Neysa is pragmatic. “Seeing a homegrown NVIDIA in five years? Not realistic. Manufacturing silicon is complex. A more realistic approach is to incentivise global manufacturers and ODMs to produce in India,” Kirpalani noted. The focus is on accessible infrastructure and a strong supply chain rather than building chips from scratch.
While Yotta, E2E, NxtGen, and others are racing to deploy GPUs and secure large contracts, Neysa is carving a niche for operational simplicity and sovereign AI. Its Velocis Cloud is designed to let AI teams focus on product development rather than cloud headaches.
IndiaAI’s GPU push is impressive — 40,000 units and counting — but sheer capacity alone doesn’t solve the trilemma. That’s Neysa’s take.
The post How Neysa Stands Out in the IndiaAI GPU Race appeared first on Analytics India Magazine.
