When AI Startups Run Out of Compute, Mid-Sized Firms Step In

Despite the $20 billion AI commitments by 2025, a lack of access to digital infrastructure and computing resources has hindered the scaling of AI startups in India. This “compute gap” leads to a structural imbalance that defines who truly benefits from India’s AI push. 

For every well-funded AI startup scaling globally, there are hundreds of early-stage innovators struggling with access to infrastructure, affordability of computing resources, and institutional visibility.

While India’s cloud and data centre infrastructure is expanding at record speed, access to that infrastructure remains uneven. “While significant investments have been made in data centres and GPU clusters in India, compute can still be a bottleneck for early-stage AI startups,” said Brijesh Patel, founder and CTO of SNDK Corp.

For many emerging founders, the problem isn’t a lack of vision or capability; it’s viability. Brijesh explained that the high costs associated with GPU clusters and the complexity of infrastructure management keep startups from accessing high-performance computing resources. Simply put, India’s AI ecosystem is rich in ambition but divided by affordability.

While policy-led programmes such as the IndiaAI Mission have made commendable progress in providing shared compute clusters and credits, the benefits are not uniformly distributed. “The application processes can sometimes be cumbersome, and awareness of these opportunities is not as widespread as it should be,” Brijesh added. 

For many founders outside major hubs or institutional accelerators, accessing compute still feels like an exclusive privilege rather than a public utility.

What Happens to Those Without Institutional Backing?

The startups not backed by institutional investors or corporate incubators face a steep climb. Without the cushion of venture capital, computing costs quickly become prohibitive. Training large language models (LLMs), vision systems, or multimodal agents can require millions of GPU hours. 

The cost may be as low as ₹67 per hour, depending on the scale and architecture used. This is facilitated through the IndiaAI Mission, which aims to provide compute access to startups, students, and others in need.

Some founders turn to shared cloud credits from AWS, Google Cloud, or Azure, but these are short-lived and designed for testing rather than sustained product development. “While the bottleneck persists,” Brijesh said, “the growing availability of cloud services and public computing platforms is gradually making it more feasible for startups to leverage AI without being limited by their compute capacity”.

This shift towards shared infrastructure marks an encouraging step, but the reality is that India’s AI capital is still concentrated mainly among startups affiliated with premier institutions, accelerators, private venture capitalists or government-backed labs. For the rest, scaling from prototype to product remains constrained by economics.

The Rise of the Mid-Sized Integrators

This is where mid-sized indigenous IT firms, such as SCS Tech India, have emerged as enablers. Positioned between large integrators and fledgling startups, these firms bring a rare mix of scale, agility, and contextual understanding, something India’s AI ecosystem urgently needs.

“We view the AI landscape as a shared-growth ecosystem rather than a zero-sum game,” said Sujit Patel, CEO and MD of SCS Tech India. For Sujit, the company’s mission is clear: empower startups by giving them access to the kind of compute and enterprise-grade infrastructure they could never afford alone.

“We’ve partnered with cloud hyperscalers and domestic data platforms to extend our high-performance computing and AI workbench resources to them, helping to accelerate model training and deployment at a fraction of the typical cost,” he explained.

SCS Tech collaborates directly with startups to bring their niche innovations to enterprise environments. “We see ourselves not as competitors but as collaborators, co-creating with startups and research institutions by integrating their niche AI capabilities, like domain-specific models or predictive algorithms, into live enterprise projects,” Sujit said. 

This approach allows startups to validate their technologies in real-world scenarios while SCS Tech’s clients benefit from faster, more tailored solutions, creating what Sujit called “a supplementary and complementary growth loop.”

The Execution Gap

The gap between innovation and industrial adoption has long plagued India’s tech ecosystem. Startups excel at novelty and ideation, but large-scale deployment requires compliance frameworks, data governance, and long-term reliability, areas where many smaller ventures falter. This is where mid-sized firms can translate innovation into execution.

According to SCS Tech, their R&D efforts are now aligned with national priorities. “We are directing our R&D towards sovereign AI capabilities such as contextual LLMs, scalable data engineering, and ethical AI frameworks,” said Sujit. 

“Mid-sized firms…play a catalytic role in translating policy ambitions into tangible outcomes, bridging the gap between lab and market so India’s AI ecosystem can evolve into a self-sustaining, globally competitive, and inclusive growth engine,” he added. 

Their model exemplifies how mid-sized Indian firms can transform from service providers into system integrators of innovation, building on government infrastructure while co-developing indigenous IP with startups.

Policy Tailwinds, Practical Headwinds

Government programmes such as the IndiaAI Mission have focused on democratising AI access and sovereign compute. The creation of three national AI compute clusters and dedicated grants for startups signals a shift from vision to execution. But, as the MeitY 2025 roadmap suggests, India still needs 20 times more compute capacity to meet its domestic AI demand.

The Digital India Bill and Data Protection Act further strengthen the institutional backbone, but startups continue to face fragmented compliance pathways. For many, partnerships with mid-sized firms serve as a bridge, providing enterprise-grade governance, security, and deployment support without compromising agility.In this hybrid ecosystem, computing could evolve beyond its traditional role as a mere resource, moving towards a shared responsibility within the ecosystem.

The post When AI Startups Run Out of Compute, Mid-Sized Firms Step In  appeared first on Analytics India Magazine.

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