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India Generative AI Startup Landscape 2025: Mapping the Momentum

August 7, 2025 32 0 nasscom insights AI

India Generative AI Startup Landscape 2025: Mapping the Momentum

India’s GenAI startup ecosystem stands at a transformative inflection point in 2025, rapidly scaling in size, deepening in capability, and becoming more strategically relevant to enterprises, policymakers, and the broader technology landscape. Over the last year, the ecosystem has seen an extraordinary 3.7X surge in startup formation and is now among the most active globally in terms of GenAI application development. However, this rise is also being shaped by an environment that presents both enabling tailwinds and structural challenges. This report, titled “India Generative AI Startup Landscape 2025: Mapping the Momentum”, presents an in-depth analysis of this fast-evolving landscape. It offers a structured view across global and Indian market trends, startup formation patterns, funding shifts, enterprise adoption behavior, and policy interventions. The report also highlights where India stands, what it uniquely brings to the global GenAI table, and what must be done to unlock its full potential. The recommendations are in the form of a forward-looking playbook for founders, investors, enterprises, and policymakers to act decisively in shaping the next decade of AI innovation from India.

Key Highlights

 

The number of global GenAI startups has grown 9X in just two years, crossing 4,500 by H1 CY2025, reflecting an accelerated shift toward verticalized solutions and agentic platforms.

  • Cumulative funding in GenAI startups has increased 2.7X, reaching $54 Bn in H1 CY2025, with ~88% of capital flowing to late-stage players, indicating investor confidence in scaling and enterprise adoption.
  • The US continues to dominate with the highest number of GenAI startups and lion’s share of funding, owing to big-tech partnerships, compute-rich infrastructure, and deep-pocket VCs.
  • While late-stage funding thrives globally, a decline in new GenAI startup formation in mature markets like the US signals growing barriers for early-stage entrants.

 

 

A 3.7X surge in startups places India as the world’s second-largest GenAI startup hub

  • India’s GenAI startup landscape has witnessed 3.7X growth in cumulative startups, reaching 890+ by H1 CY2025. GenAI application startups alone have grown 4X to cross 740, contributing ~83% of the total.
  • Pivoting is a defining trend, with 63% of Indian GenAI startups pivoting their model or focus in the past year, largely toward vertical SaaS and application-focused models.
  • Cumulative funding in Indian GenAI startups grew by 30% Y-o-Y, touching $990 Mn by H1 CY2025, though this still lags significantly behind global peers.
  • India is emerging as a hub for vertical AI specialists, with the largest rounds going to startups solving domain-specific problems in regulated industries.

 

 

GenAI use cases are moving from exploration to deployment, with BUs driving purchasing decisions

  • 64% of founders are focused on model efficiency, yet 58% lack a sustainable compute strategy, revealing a major gap in operational readiness and cost planning.
  • Startups are increasingly sophisticated in their data approach: 79% use proprietary customer data, and 45% augment with synthetic data for better fine-tuning and accuracy.
  • The technology stack has evolved significantly, with most Indian startups now using Autoregressive models, indicating strong alignment with global trends.
  • Despite the rise in enterprise interest, 30% of startups report no active partnerships, and regulatory hurdles and
  • IP protection concerns remain the top two concerns with such collaborations for the second year in a row.

 

 

Talent, compute, and capital scarcity create a foundational resource crisis while GTM struggles continue to hinder scaling

  • Risk-averse capital markets and absence of patient funding have trapped many startups in low-complexity application zones, limiting deep tech R&D.
  • India can build a differentiated GenAI trajectory through agents, vernacular LLMs, and DPI-linked AI
  • Strategic interventions must focus on compute access, patient capital, and scalable adoption enablers

 

 

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