India’s AI cloud market is crowded with a number of suppliers vying for the eye of startups, IITs, and enterprises. The IndiaAI Mission has empanelled over 34,000 GPUs, with one other 6,000 on the way in which. 

Round 72% of those GPUs have been allotted to startups constructing foundational fashions, offering a lift to the nation’s AI ambitions.

Yotta Information Providers, NxtGen, E2E Networks, and others like Jio, CtrlS, Netmagic, Cyfuture, Sify, Vensysco, Locuz, and Ishan Infotech have carved their very own slices of this GPU pie. However, Neysa is staking a definite declare. 

The Mumbai-based AI acceleration cloud system supplier is focussed on the issue that the majority AI groups face: the AI trilemma, as its chief product officer Karan Kirpalani phrases it. 

At Cypher 2025, one among India’s largest AI conferences organised by AIM in Bengaluru, Kirpalani outlined this trilemma: constructing a product with the best unit economics, pace to market, and product-market match, all whereas scaling belief, which hardly ever works in follow. 

“You’ll be able to construct a product on the proper price with pace to market however could fail to align with market wants, or any two of the opposite standards. It’s the residence drawback. Choose any two, however you may’t have all three,” he mentioned.

Conventional cloud suppliers — AWS, Google Cloud, Azure — can clear up elements of the issue however hardly ever all three. “AWS will cost you 4 instances what the prevalent market price is for an H100 GPU. You get pace, sure, however you miss unit economics. You pivot the opposite approach, purchase your individual GPUs, and now you’re caught on pace and scale. Nobody has solved all three,” Kirpalani elaborated.

Enter Velocis

Velocis Cloud goals to sort out the trilemma. In contrast to different suppliers centered on GPU allocation, Neysa delivers an end-to-end AI cloud platform. From Jupyter notebooks and containers to digital machines and inference endpoints, all the pieces is pre-integrated and accessible with a click on on Velocis Cloud. 

Enterprises get flat-fee pricing, granular observability, and devoted inference endpoints for fashions like OpenAI’s GPT-OSS, Meta’s Llama, Qwen, and Mistral. Startups get credit score applications to keep away from “project-killing” hyperscaler payments. 

“Shoppers admire it greater than GPUs. Naked steel, digital machines, containers, Jupyter notebooks, inference endpoints — you are able to do all of it with a click on, and at much better unit economics than hyperscalers,” Kirpalani mentioned throughout a podcast at Cypher 2025.

Distinction that with Yotta. CEO Sunil Gupta has ordered 8,000 NVIDIA Blackwell GPUs to increase capability for IndiaAI initiatives. Yotta already operates 8,000 H100s and 1,000 L40s, supporting Sarvam, Soket, and different large-scale AI fashions. “Most large-scale AI mannequin growth in India at this time is going on on Yotta’s infrastructure,” Gupta earlier advised AIM. 

Yotta’s energy is sheer scale, with a platform-as-a-service API layer for enterprise entry. On the similar time, Yotta additionally affords comparable providers, from coaching on naked steel {hardware} to deploying customized fashions and inference on its Shakti AI Cloud platform.

NxtGen takes a long-term, trust-driven method to AI and cloud. In contrast to Neysa, which focuses on end-to-end platform usability and adaptability, NxtGen leverages its legacy as one among India’s first cloud gamers and authorities contracts to construct enterprise inference and sovereign AI at scale. 

“The primary distinction is that now we have quite a lot of belief with our prospects,” CEO AS Rajgopal advised AIM earlier, emphasising that NxtGen isn’t just offering GPUs however creating an enterprise-grade inference market with open-source, agentic AI platforms. Its philosophy blends early adoption, infrastructure funding, and operational sovereignty.

Standing Out

So the place does Neysa match on this crowded area? It’s not about who has essentially the most GPUs or the largest contracts. It’s about usability, predictability, and sovereignty. Kirpalani emphasised India’s want to cut back dependency on international fashions and knowledge centres. 

“For India, investing throughout the stack and decreasing dependency on international fashions, {hardware}, and knowledge centres is important,” he mentioned. Neysa’s technique is to supply selection — supporting a number of open-weights fashions — and management, guaranteeing enterprises can fine-tune, self-host, and handle token efficiency with out surprises.

{Hardware} scale is a consideration, however Neysa is pragmatic. “Seeing a homegrown NVIDIA in 5 years? Not reasonable. Manufacturing silicon is advanced. A extra reasonable method is to incentivise world producers and ODMs to supply in India,” Kirpalani famous. The main target is on accessible infrastructure and a powerful provide chain slightly than constructing chips from scratch.

Whereas Yotta, E2E, NxtGen, and others are racing to deploy GPUs and safe massive contracts, Neysa is carving a distinct segment for operational simplicity and sovereign AI. Its Velocis Cloud is designed to let AI groups concentrate on product growth slightly than cloud complications. 

IndiaAI’s GPU push is spectacular — 40,000 items and counting — however sheer capability alone doesn’t clear up the trilemma. That’s Neysa’s take.

The submit How Neysa Stands Out within the IndiaAI GPU Race appeared first on Analytics India Journal.