India’s AI Founders Stare at the Credit Cliff
By Smruthi Nadig ·
India has become central to the AI ambitions of global technology giants. The country is now a key component of the AI strategies of companies such as Microsoft, Amazon, Google, and Intel, not only as a vast end market but also as a strategic hub for AI infrastructure, talent, and enterprise deployment.
Yet, beneath the narrative of scale and opportunity lies a structural reality: many Indian AI startups are being built on foundations they do not control.
From GPU credits and cloud incentives to Western foundational model APIs and enterprise co-selling pipelines, hyperscalers now support a substantial portion of India’s AI ecosystem. The support accelerates growth. It also creates embedded dependency, one that becomes visible only when the credits run out.
The Credit Economy and the Cliff After
For early-stage AI startups, cloud credits often determine whether experimentation happens at all.
In a previous conversation with AIM, Jayasudan Munsamy, Founder of DeepVisionTech.AI, describes how his company’s infrastructure costs were absorbed in its early years. “Since 2019, all our cloud costs… have been taken care of by companies like Oracle and NVIDIA, [then] Microsoft for a couple of years, and [most recently] Google [as of December 2025],” he says.
NVIDIA’s startup programme gave the company the runway to train proprietary computer-vision and translation systems before revenue was generated.
This trajectory reflects what a LinkedIn report on startups running out of cloud credits describes as a “deferred cost illusion”. When computing is free, consumption increases without economic feedback. Teams optimise for performance over efficiency, and as architectures become more complex, GPU-heavy pipelines become the norm.
Reports on the ‘credit cliff’ further frame this moment as a structural inflexion point. During the subsidised phase, startups are encouraged to experiment aggressively. But when credits expire, infrastructure bills that were once abstract become operating constraints. Startups often discover their burn rate is substantially higher than expected because systems were designed for abundance rather than sustainability.
Praveer Kochhar of KOGO AI considers this predictable. “Working without cloud credits is another one of those choices that make our business model and cost of goods sold real, and our ROI impact calculations accurate,” he says.
Ashutosh Prakash Singh, CEO of Revrag, sees a recurring pattern. “Most startups burn through compute credits not because they’re building too much, but because they’re building inefficiently early on.”
Several common issues arise when working with large language models (LLMs), he says, including treating them as black boxes, which limits understanding of their mechanics.
“Early teams often optimise for speed of experimentation, which is fine, but without discipline it quickly turns into uncontrolled spend,” he stresses.
Credit-funded experimentation often delays cost discipline.
Renting Intelligence
Infrastructure is only one layer of the AI stack. Increasingly, even the intelligence layer is often rented. A recent report notes that a significant portion of Indian AI startups rely heavily on Western APIs and closed models for both training and inference. This offers immediate access to state-of-the-art capability, but also concentrates core intelligence outside domestic control.
Seema Rao, Managing Director of Strategic Partnerships at Google India, maintains that Google’s support extends beyond credits. “Cloud and compute credits are an important starting point, but they represent just one element of our comprehensive support,” she says.
For technically strong, pre-revenue startups, Google’s focus shifts to helping them move “from experimentation to production-ready, scalable systems.” This includes mentorship on architecture, deployment, and long-term efficiency—not just free GPU hours.
Crucially, she emphasises that startups should not remain dependent on credit. “Our intent is to help founders build durable products and businesses that can sustain themselves beyond initial incentives, rather than remain dependent on credits alone.”
Cloud-credit exhaustion also exposes pricing unpredictability. When startups build deeply into a specific model provider or cloud architecture, switching costs, even if technically possible, become operationally expensive.
If foundational model pricing shifts or inference costs rise, startups have little negotiating leverage.
Those that survive the credit cliff move away from simple API wrapping towards deeper domain expertise.
The Subtler Dependence
Infrastructure and models are visible dependencies. Distribution is quieter. “As startups mature, the critical challenge shifts from access to technology to access to customers and repeatable adoption,” Rao explains.
Hyperscaler market access programmes help startups shorten enterprise sales cycles. For early-stage AI companies, this credibility is invaluable.
Rao also points to Google’s vertical integration as a cost stabiliser. “Our own TPUs are driving cost-efficiencies that we pass on to customers. This protects startups from the volatility of supply chains they can’t control.”
She addresses vendor lock-in concerns head-on. “We help founders avoid ‘model myopia,’ the risk of building a business dependent on a single model provider that might raise prices or disappear.”
By offering a ‘Model-as-a-Service’ framework where startups can switch between Google’s models and open ecosystem alternatives, theoretically avoiding closed environments
“We ensure these startups aren't stuck in a fixed ‘meal plan’ where they are served the exact same model regardless of cost or fit,” she emphasises.
Yet, even with portability in theory, investors remain attentive to architectural entrenchment. Switching models in production often requires retuning systems, retraining embeddings, and recalibrating performance benchmarks.
Singh argues that credits should be prioritised for learning that directly advances the business, rather than open-ended experimentation.
According to him, investing in evaluation and observability is crucial so that each experiment yields valuable insights, enabling continuous learning and informed decision-making for future enhancements.
“Pure exploration has diminishing returns very quickly. Startups that win tend to focus on getting their first few customers working reliably, even if the system isn’t perfect yet,” Singh highlights.
Similarly, the LinkedIn report on cloud credit exhaustion also warns that distribution partnerships can mask go-to-market fragility.
Where Scale Meets Risk
From a venture perspective, reliance on hyperscalers is a capital structure question.
In an previous conversation with Stellaris Venture Partners, partner Naman Lahoty explains that venture outcomes depend on building companies capable of delivering outsized returns. “Our economics work when you are really able to create a large outcome. Out of those 30 companies, probably four or five have to be really successful to really make up for all the money that we lost,” he says.
VCs, therefore, are underwriting scale, not incremental success.
As hyperscalers expand deeper into startup ecosystems, offering credits, accelerator programmes, co-selling, and sometimes direct investments, an obvious question arises: Does Big Tech compete with venture capital?
Lahoty says no. Corporations, he explains, typically invest for strategic alignment. “They do see that, how does it fit into their strategy at some point down the line?”
If a corporate investor is involved, alignment expectations follow. “It might create [the possibility] that they may not be able to realize unlock full value of whatever efforts they're putting in, because it might be capped,” Lahoty says. “It also restricts their freedom to a certain extent, because you have to align with the strategic direction of that company.”
Venture capital, by contrast, is structurally different. “We are a pure financial investor in that sense, and our interest is in maximising the outcome value of the company and the overall size of the outcome which they can deliver,” he adds. That objective of maximising enterprise value typically aligns more directly with founders’ ambitions.
The distinction matters in the AI era.
Vardhan Dharnidharka, Principal at Stellaris, reinforces that differentiation must sit beyond access to commoditised infrastructure. “The open AI APIs are available to everybody,” he says. If access is universal, value must be created through execution, domain depth, and iteration speed.
“While the underlying tools are commoditised… how you build it out into the final product… is not quite yet commoditised,” he adds.
In an environment where platforms provide infrastructure, distribution, and occasionally strategic investment, the real competition is not between VCs and Big Tech; it is between startups that build leverage and those that build dependence.
Roa highlights that the Google Market Access Program is designed to help startups move “from pilots to enterprise adoption by strengthening go-to-market readiness and enabling meaningful engagement with the right buyers.”
She frames Google’s role as reducing friction, not replacing founders’ ownership of relationships. “Our intent is to reduce friction in scaling, not to replace a startup's go-to-market strategy, and to help strong products find sustainable paths to growth over time”
Platform Consolidation and Competitive Compression
For startups building orchestration or AI agent layers on top of hyperscaler infrastructure, this raises a strategic risk: the platform you build upon may one day compete with you.
Lahoty, therefore, prioritises problem statements “which cannot be easily disrupted or commoditised.”
After the credit cliff, only durable layers endure. Singh asserts that merely offering compute credits is not enough for startups to succeed. The ecosystem must empower these companies by guiding them to design efficient architectures, establish strong evaluation and cost-monitoring practices, and remain model-agnostic to avoid reliance on a single vendor.
Sharing best practices on when to use large, smaller, or hybrid models is also essential, he says. The foundation of sustainable AI businesses lies in helping startups effectively leverage their compute resources, rather than just increasing their availability.