Cloud data company Snowflake is significantly expanding its presence in India, identifying the nation as one of its fastest-growing global markets. Sridhar Ramaswamy, CEO of Snowflake, highlighted India's pivotal role in the enterprise shift towards unified data and AI platforms, moving away from legacy siloed systems.
Snowflake's Expanding Footprint in India
Over the past two years, the Nasdaq-listed firm has doubled its India headcount to approximately 700 employees. India has emerged as a crucial strategic market, not only for its rapidly increasing adoption among local enterprises but also for the growing influence of India's Global Capability Centres (GCCs) and development hubs in advancing AI initiatives for multinational corporations.
Snowflake collaborates with prominent Indian customers like Indigo, Swiggy, and Bajaj Allianz General Insurance. India also hosts about half of Snowflake’s Asia-Pacific and Japan partner ecosystem, including major players such as Microsoft, TCS, and Deloitte. The company's development centers in Pune and Bengaluru are integral to its global operations, contributing to engineering, finance, and IT functions, and are increasingly becoming hubs for AI-led innovation.
The Rise of Agentic AI
A significant driver for this momentum, according to Ramaswamy, is the emergence of agentic AI. He stated, “Agentic AI is opening up new areas of growth as companies turn to AI to simplify processes and improve data management.” Snowflake has observed a sharp increase in customer uptake for its core AI offerings: Snowflake CoCo, an agentic coding assistant, and Snowflake CoWork, an intelligence platform. Both have seen rapid enterprise deployment, aimed at reducing costs and accelerating technology projects.
Ramaswamy emphasized that AI is profoundly altering the economics of software development and data management. Tasks like data migration, which previously demanded extensive human resources and long timelines, can now be completed by smaller teams in a fraction of the time.
Internal AI Adoption and Strategic Partnerships
Internally, Snowflake is leveraging AI tools to automate routine tasks, freeing employees to focus on developing new applications. Much of this internal innovation is spearheaded by the company's Pune center. For instance, Snowflake’s GTM AI Assistant is utilized by over 6,000 employees for tracking external engagement and personalizing communication strategies, achieving a 99.9% accuracy rate in enhanced lead filtering.
The company is also deepening its partnerships with major technology providers to meet surging enterprise demand. Recent announcements include a $6 billion multi-year infrastructure commitment with Amazon Web Services and a strategic partnership with Anthropic. This underscores the scale of AI and data workloads migrating onto its platform. Currently, 13,600 of Snowflake’s 13,900 customers utilize its agentic AI capabilities weekly, demonstrating widespread adoption. Vijayant Rai, managing director of Snowflake India, noted, “We are seeing AI acceleration at scale across India.”
Investing in Future Skills and Business Models
Beyond enterprise adoption, Snowflake is investing globally in AI skills development, committing $20 million to upskill one million people in data and AI by 2029. In India, it is collaborating with initiatives like FutureSkills Prime (alongside Nasscom and MeitY) and the ICT Academy to train professionals and educators.
Ramaswamy believes the widespread adoption of AI agents will compel enterprises to rethink traditional business models, especially in consulting and software services, where pricing is shifting from effort-based to outcome-based models. For employees, the implications are equally significant, with professionals expected to evolve from narrow specialists into broader generalists, using AI to amplify productivity across multiple functions.
“India is a massive opportunity waiting to happen,” Ramaswamy concluded, expressing strong confidence in India's long-term potential, driven by both traditional enterprises and digital natives adopting AI and migrating data to production use cases on Snowflake.