India’s AI story in 2026 is no longer just about chatbots, copilots or enterprise AI applications. The real transformation is happening deep in the stack, in AI infrastructure.
As AI models grow larger, training costs rise and geopolitical realities reshape technology supply chains, India is pushing aggressively toward sovereign AI infrastructure. This includes domestic GPU cloud platforms, AI-optimized data centers, foundational models tightly coupled with compute, and even indigenous AI chips.
This blog highlights the top 15 AI infrastructure startups in India as of 2026, focusing strictly on companies building the foundational layers of the stack rather than application-only AI products.
Note: This list is in no particular order and is not an endorsement or ranking. India’s AI infrastructure space is moving very fast, and funding, valuations, GPU capacity and company status change frequently, so verify the latest details before making any decision.
What Counts as AI Infrastructure?
For this list, AI infrastructure covers companies building the foundational layers of the AI stack rather than application-only products. That includes GPU cloud platforms for training and inference, sovereign AI compute and data centers, AI-optimized cloud services, high-performance computing (HPC) clusters, foundational models bundled with their own compute layer, AI accelerators, GPUs and semiconductor enablers, and AI observability, deployment and workload-optimization tools. Pure SaaS AI apps, vertical AI solutions and consumer-facing AI tools without infrastructure ownership are excluded.
Quick Comparison of India’s Top AI Infrastructure Startups (2026)
| Company | HQ | Category | Primary Focus | Stage / Funding |
|---|---|---|---|---|
| Krutrim (Ola Group) | Bengaluru | Models + sovereign infra | Full-stack sovereign AI | Unicorn (~$1B) |
| Sarvam AI | Bengaluru | Models + infra | Indian-language LLMs | Unicorn · $1.5B |
| Neysa | Mumbai | AI-native GPU cloud | Training & inference | Unicorn · $1.4B |
| E2E Networks | Delhi NCR | GPU cloud + HPC | Public AI compute | NSE-listed · ~3,900 GPUs |
| Yotta (Shakti Cloud) | Navi Mumbai | Sovereign AI cloud | Hyperscale GPU cloud | 16,000+ H100 GPUs |
| Jarvis Labs | Bengaluru | On-demand GPU cloud | Affordable GPU access | Acquired by E2E |
| AceCloud | Noida | GPU cloud for GenAI | LLM training & inference | India-first regions |
| 169Pi | Bengaluru | Efficient AI systems | Infra-efficient models | Early-stage |
| Agrani Labs | Bengaluru | Indigenous AI chips | Full-stack GPU + software | $8M seed (Peak XV) |
| Netrasemi | Kerala | AI accelerators / SoC IP | Edge-AI silicon | ~₹107 Cr Series A |
| Hypergro.ai | Bengaluru | AI infra tooling | Deploy & scale AI | Early-stage |
| ChaosGenius | Bengaluru | AI observability | Workload optimization | Early-stage |
| Vaani AI Research | Bengaluru | AI research infra | Speech & language | Early-stage |
| Simplismart | Bengaluru | AI deployment / MLOps | Inference optimization | ~$7M Series A (Accel) |
| Outpost.run | Bengaluru | AI deployment | Edge / cloud compute | Early-stage |
1. Krutrim (Ola Group)
Headquarters: Bengaluru | Focus: Foundational models + sovereign AI infrastructure | Stage: Unicorn (~$1B valuation)
Krutrim is India’s most visible attempt at a full-stack sovereign AI platform. Widely known for its multilingual Indian-language LLMs, its longer-term ambition is to own compute, data, models and deployment infrastructure within India. Backed by Ola Group capital, it became India’s first AI unicorn in early 2024 after a roughly $50 million round led by Matrix Partners India (now Z47).
Founder Bhavish Aggarwal has committed a phased investment that scales toward ₹10,000 crore across Ola Krutrim. The company now runs Krutrim Cloud (its GPU cloud) and Krutrim AI Lab (a frontier research unit), and has released models including Krutrim-2, the Chitrarth vision-language model and the Dhwani speech model. On the hardware side it has announced indigenous chips branded Bodhi (AI), Sarv (general compute) and Ojas (edge), in partnership with Arm and Untether AI, alongside plans to expand data-center capacity toward 1 GW by 2028. Its strategic relevance exceeds its current commercial footprint, though execution across such a wide stack remains the key question.
2. Sarvam AI
Headquarters: Bengaluru | Focus: Indian-language foundational models + efficient AI infrastructure | Stage: Unicorn; ~$234M Series B at $1.5B valuation
Sarvam AI is widely considered a core pillar of India’s sovereign AI layer. Founded in 2023 by Vivek Raghavan and Pratyush Kumar, it emphasizes efficient inference, cost-optimized deployment and India-first AI workloads rather than brute-force model scaling. That approach aligns well with India’s economic realities, where infrastructure efficiency matters as much as raw compute scale.
In 2026 Sarvam became a unicorn, raising a Series B of about $234 million at a $1.5 billion valuation, with backers that now include Lightspeed, Peak XV and Khosla Ventures alongside Bessemer, Nvidia and HCLTech. It was also selected under the IndiaAI Mission to build one of the country’s first sovereign foundational models, and has open-sourced large Indic-language models in the 30-billion and 100-billion-plus parameter range, supporting more than 20 Indian languages.
3. Neysa
Headquarters: Mumbai | Focus: AI-native GPU cloud platform | Stage: Unicorn; ~$1.4B valuation
Neysa has quickly emerged as one of India’s most AI-first cloud infrastructure companies, built specifically for training, fine-tuning and deploying AI models on its Velocis GPU platform. Founded in 2023 by Sharad Sanghi and Anindya Das, it offers GPU clouds tailored for AI workloads, with competitive pricing against hyperscalers and India-focused compliance and latency advantages, which has driven strong adoption among GenAI startups.
In February 2026 Neysa turned unicorn after a Blackstone-led round of roughly $600 million in equity (with additional debt reported), valuing it near $1.4 billion and ranking among India’s largest AI funding rounds to date. The capital funds a planned deployment of more than 20,000 GPUs across the country, and Neysa is empanelled under the IndiaAI Mission to supply sovereign compute.
4. E2E Networks
Headquarters: Delhi NCR | Focus: GPU cloud, HPC, public AI compute | Stage: NSE-listed; ~3,900 GPUs
E2E Networks is one of India’s earliest cloud infrastructure players and now its only listed pure-play GPU cloud, trading on the NSE main board. It operates one of the country’s largest GPU fleets, roughly 3,700 to 3,900 NVIDIA units spanning A100, H100 and H200, across data centers in Delhi and Chennai, and was the first provider to bring NVIDIA H200 GPUs to the Indian market.
The company is MeitY-empanelled under the IndiaAI Mission and won a contract of about ₹177 crore to supply H100 and H200 capacity to foundational-model startup Gnani.ai. In 2025 it also acquired GPU-cloud startup JarvisLabs.ai, consolidating its position as a backbone of India’s sovereign AI compute at national scale.
5. Yotta Infrastructure (Shakti Cloud)
Headquarters: Mumbai / Navi Mumbai | Focus: Sovereign AI cloud + hyperscale data centers | Stage: 16,000+ H100 GPUs
Yotta’s Shakti Cloud operates one of India’s largest NVIDIA-powered sovereign AI platforms, serving enterprises, startups and government bodies from its NM1 hyperscale campus. Built in partnership with NVIDIA, Yotta is India’s first NVIDIA Cloud Partner and one of only a handful globally, running over 16,000 H100 GPUs with plans to scale toward 32,768 and to add next-generation B200 chips.
Under the IndiaAI Mission, Yotta committed more than 9,200 GPUs (8,192 NVIDIA H100 plus 1,024 L40S), reported to be over half of the Mission’s advanced compute capacity, and has partnered with Microsoft Azure AI, Sarvam AI and Hanooman AI. It sits closer to enterprise infrastructure than startup culture, but plays a critical role in India’s AI infrastructure sovereignty.
6. Jarvis Labs
Headquarters: Bengaluru | Focus: Affordable on-demand GPU cloud | Stage: Acquired by E2E Networks
Jarvis Labs became a favourite among AI researchers, indie developers and early-stage startups by offering affordable, on-demand access to NVIDIA GPUs such as the A100 and H100, with some of the lowest published hourly rates in India. Its focus has been on democratizing AI compute access for small teams that cannot commit to large reservations.
In 2025 Jarvis Labs was acquired by E2E Networks, with founder Vishnu Subramanian joining the listed company, so it now operates within E2E’s broader GPU-cloud and IndiaAI Mission footprint rather than as an independent startup. For budget-conscious developers it remains one of the most accessible entry points to high-end GPUs in India.
7. AceCloud
Headquarters: Noida | Focus: High-end GPU cloud for GenAI | Stage: India-first GPU regions
AceCloud delivers enterprise-grade GPU infrastructure optimized for LLM training, large-scale inference and GenAI workloads, with on-demand access to NVIDIA H200, H100, A100 and L40S GPUs. It runs India-first regions in Mumbai and Hyderabad, offering low-latency, data-resident deployments with transparent pricing and no egress charges.
Positioned as a domestic alternative to hyperscalers, AceCloud is increasingly used by mid-sized enterprises and funded startups, and supports GenAI model hosting including India-sovereign model deployments with enterprise-grade support.
8. 169Pi
Headquarters: Bengaluru | Focus: AI infrastructure + efficient reasoning models | Stage: Early-stage
169Pi has gained attention for its infrastructure-efficient AI systems, combining efficient foundational models with infrastructure-aware design and research-driven deployment. It has been associated with research and government-linked environments, including work reported around ISRO projects, positioning it as a niche but strategically interesting player focused on doing more with less compute.
9. Agrani Labs
Headquarters: Bengaluru | Focus: Indigenous AI chips + full-stack compute | Stage: $8M seed (Peak XV Partners)
Agrani Labs is among India’s most ambitious deep-tech AI hardware startups, working toward locally developed GPUs and AI accelerators. Founded by semiconductor veterans from Intel and AMD, with chip pioneer Vinod Dham as a founding advisor, it emerged from stealth in early 2026 with an $8 million seed round led by Peak XV Partners.
The company is building a full-stack AI compute platform that spans a high-performance data-center GPU and its own software stack of compilers, libraries and AI frameworks, aimed at reducing India’s long-term dependence on imported AI hardware. It has reportedly begun raising a larger round to develop CUDA-compatible inference chips. Still early-stage, Agrani is strategically critical to India’s hardware sovereignty ambitions.
10. Netrasemi
Headquarters: Kerala (Thiruvananthapuram) | Focus: AI accelerators + semiconductor IP | Stage: ~₹107 Cr Series A (Zoho)
Netrasemi develops AI-enabling silicon and edge-AI system-on-chips, supported by India’s semiconductor policy push. Founded in 2020 by Jyothis Indirabhai, Sreejith Varma and Deepa Geetha, the Kerala-based company builds domain-specific, energy-efficient chips for on-device AI in applications such as surveillance, robotics and smart infrastructure.
In 2025 it raised about ₹107 crore (roughly $12 million) in a Series A led by Zoho Corporation and Unicorn India Ventures, taking total funding to around ₹125 crore. Its first SoCs, built on TSMC’s 12nm node and including an AI video-analytics chip and an IoT microcontroller, are moving from prototype toward production. Its work is foundational, long-gestation and essential for India’s AI hardware sovereignty.
11. Hypergro.ai
Headquarters: Bengaluru | Focus: AI infrastructure tooling | Stage: Early-stage
Hypergro.ai builds AI-driven tooling for deploying, managing and scaling AI systems, and has appeared in 2026 ecosystem reports as a fast-rising enabler. Its emphasis is on the operational layer that helps teams move AI from experiment to production more efficiently.
12. ChaosGenius
Headquarters: Bengaluru | Focus: AI observability + optimization | Stage: Early-stage
ChaosGenius helps enterprises monitor AI and data workloads, optimize compute usage and improve the ROI of their AI infrastructure investments. As AI and GPU costs rise, observability and cost-optimization platforms like ChaosGenius become increasingly important to teams trying to keep spend under control.
13. Vaani AI Research
Headquarters: Bengaluru | Focus: Core AI research infrastructure | Stage: Early-stage
Vaani AI focuses on speech and language research infrastructure, contributing foundational layers to India’s AI research ecosystem. Its emphasis on Indic speech and language aligns with the broader national push to build AI that works across India’s many languages.
14. Simplismart
Headquarters: Bengaluru | Focus: Simplified AI infrastructure + inference optimization | Stage: ~$7M Series A (Accel)
Simplismart aims to reduce the complexity of deploying AI systems, especially for startups and SMEs. Founded in 2022 by Amritanshu Jain and Devansh Ghatak, it offers an inference-first platform and a high-speed inference engine that improves GPU utilization and lowers inference costs across large language, vision, speech and image models, deployable on AWS, Azure, GCP or on-premises.
The company raised a $7 million Series A led by Accel, with angels including former Meta COO Sheryl Sandberg and Notion co-founder Akshay Kothari, followed by a further round led by Dallas Venture Capital. Nvidia has reportedly been in talks to lead a subsequent round, and its customers include Tata 1mg, Mindtickle and InVideo.
15. Outpost.run
Headquarters: Bengaluru | Focus: AI deployment + edge/cloud compute | Stage: Early-stage
Outpost.run supports AI deployment across hybrid, cloud and edge environments, addressing the growing need for distributed AI infrastructure. As more workloads move closer to where data is generated, tools that can place and manage models across cloud and edge become increasingly valuable.
Which Type of AI Infrastructure Startup Fits Your Needs?
The companies above are not all built for the same buyer. Understanding where each sits in the stack makes the list easier to navigate.
GPU Cloud Platforms
If you need raw compute to train or serve models, the GPU clouds are the starting point. E2E Networks, Yotta’s Shakti Cloud, Neysa, AceCloud and Jarvis Labs (now part of E2E Networks) all rent NVIDIA GPUs, differing mainly on scale, pricing, sovereignty and support.
Foundational Models Bundled With Compute
Krutrim and Sarvam are relevant when the interest is in India-specific foundational models that come with their own infrastructure strategy, particularly for Indian-language and sovereign use cases.
Indigenous AI Chips and Silicon
Agrani Labs and Netrasemi matter for the long game of building AI hardware in India, from data-center GPUs to edge-AI SoCs, rather than for immediate off-the-shelf deployment.
Deployment, Observability and Tooling
Simplismart, ChaosGenius, Outpost.run, Hypergro.ai, Vaani AI and 169Pi sit in the software layer that makes AI cheaper, faster and easier to run, covering inference optimization, cost observability, distributed deployment and efficient model design.
Key Trends Shaping AI Infrastructure in India (2026)
GPU cloud dominance. The clearest theme is the race to build domestic GPU capacity. Listed players like E2E Networks and sovereign clouds like Yotta’s Shakti Cloud, alongside AI-native entrants such as Neysa and AceCloud, are deploying tens of thousands of NVIDIA GPUs to meet surging demand for training and inference inside India.
The sovereign AI push. Government policy is a major driver. The IndiaAI Mission, backed by roughly ₹10,000 crore, is building shared national compute (tens of thousands of subsidized GPUs at rates as low as ₹67 to ₹92 per GPU-hour) and funding foundational-model startups including Sarvam, Gnani.ai and BharatGen. Budget 2026 incentives have reinforced the direction.
Model and infrastructure convergence. The line between model labs and infrastructure is blurring. Krutrim and Sarvam both pair their own foundational models with owned or tightly coupled compute, betting that controlling the full stack is the path to efficiency and sovereignty.
Hardware resurgence. After years of dependence on imported silicon, indigenous chip startups are gaining policy and investor support. Agrani Labs (data-center GPUs) and Netrasemi (edge-AI SoCs) are early but strategically significant attempts to build AI hardware in India.
Rapid evolution. The space moves monthly, with new GPU clouds, funding rounds, unicorns and chip announcements. Several companies on this list changed status within a single year, so treat any snapshot as a moving target.
How to Choose an AI Infrastructure Partner in India
The right partner depends on the problem you are solving. For pure compute, compare GPU type and availability (H100, H200, A100), real hourly and reserved pricing, data-center location and latency, and whether the provider is empanelled under the IndiaAI Mission, which can unlock subsidized rates for eligible startups and researchers.
For sovereignty-sensitive workloads in government, healthcare or finance, data residency and a domestic, compliant stack may outweigh headline price. For model-level needs, consider whether a foundational-model provider’s languages, licensing and deployment options fit your use case. And because this market is young and consolidating, as the Jarvis Labs acquisition shows, weigh each company’s funding, customer base and support commitments, not just its current feature list, before committing to a long-term dependency.
Frequently Asked Questions
Which is the best AI infrastructure startup in India?
There is no single best; it depends on the need. For GPU compute at scale, E2E Networks and Yotta’s Shakti Cloud lead, with Neysa and AceCloud as strong AI-native options. For foundational models with their own infrastructure, Krutrim and Sarvam stand out. For AI chips, Agrani Labs and Netrasemi are the ones to watch.
What is sovereign AI, and why does it matter for India?
Sovereign AI refers to building and running AI (the compute, data, models and infrastructure) within a country’s own borders and control. For India it reduces dependence on foreign cloud and silicon, supports data residency and national security, and keeps economic value and talent at home.
What is the IndiaAI Mission?
It is a central-government programme, backed by roughly ₹10,000 crore, to build India’s AI ecosystem. A core pillar is shared national GPU compute: tens of thousands of subsidized GPUs offered at low per-hour rates through empanelled providers such as Yotta, E2E Networks, Jio and Tata, along with funding for foundational-model startups.
Which Indian startups are building GPU clouds?
The main GPU-cloud players include E2E Networks, Yotta (Shakti Cloud), Neysa, AceCloud and Jarvis Labs (now part of E2E Networks), each offering NVIDIA GPUs for training and inference.
Are there Indian startups making AI chips?
Yes. Agrani Labs is developing data-center AI GPUs and a full software stack, Netrasemi builds edge-AI system-on-chips, and Krutrim has announced indigenous chip designs (Bodhi, Sarv and Ojas). All are early-stage and long-gestation.
Which Indian AI infrastructure startups are unicorns?
As of 2026, Krutrim, Sarvam AI and Neysa have each crossed the $1 billion valuation mark, reflecting how much capital is flowing into India’s AI infrastructure layer.
How is AI infrastructure different from AI applications?
AI infrastructure is the foundational layer: GPUs, cloud platforms, data centers, chips and deployment tooling. AI applications are the products built on top, such as chatbots and copilots. This list focuses on the infrastructure layer rather than application-only companies.
Final Thoughts
India’s AI story in 2026 has moved deep into the stack. The country is no longer only building chatbots and copilots on top of foreign infrastructure; it is building the backbone itself: domestic GPU clouds, sovereign data centers, foundational models tied to compute, and the first indigenous AI chips.
The 15 companies above span that full range, from hyperscale GPU clouds and unicorn model labs to early-stage silicon startups. Backed by the IndiaAI Mission and growing private capital, this layer is maturing quickly, and the names and numbers will keep changing. Treat this as a snapshot of a fast-moving field rather than a final ranking.
Have we missed an AI infrastructure startup that should be on this list? Write to us at info@onlybizbasics.com.