In the past two years, the Indian enterprise landscape has undergone one of the most dramatic shifts since the early cloud migration wave a decade ago. Legacy-heavy organisations are consolidating their fragmented data estates. Digital-first companies are scaling global products from India. And AI — until recently a fringe pilot — is now seen as an operational imperative across sectors.
Few companies have benefited from this convergence as decisively as Snowflake. What began as a modern data platform conversation has now evolved into a far more ambitious narrative: India emerging as a core engine for Snowflake’s GTM expansion, partner-driven innovation, AI use case development, and global product ecosystem growth.
“We see India entering a phase of unprecedented acceleration,” says Vijayant Rai, Managing Director, Snowflake India. “Enterprises are moving from siloed, legacy data estates to unified data, AI and governance frameworks. Once that foundation is set, AI becomes an immediate unlock.”
Doubling GTM Presence and Tripling Market Depth
Snowflake’s India organisation now stands at nearly 700 employees — a striking expansion considering its size just two years ago. The company has doubled its GTM headcount, strengthened industry-specific expertise, and deepened technical roles across major metros.
This is not an expansion driven by pipeline alone. It is the result of structural shifts Snowflake sees in Indian enterprise behaviour. “India is no longer just a consumption market,” Rai emphasises. “It’s a market where global-grade products are being built, where partners are creating IP on our platform, and where entire industries are experimenting with AI in ways that are ahead of many developed economies.”
The company has also expanded coverage in manufacturing, energy, healthcare, retail and digital commerce, where the convergence of IT and operational data is creating massive AI-driven opportunities.
The Rise of India’s Largest Snowflake Partner Ecosystem
A defining pillar of Snowflake’s India strategy is its ecosystem — now the largest in APJ. Consulting partners, GSIs, local integrators, domain specialists, analytics boutiques, and data providers are building IP on Snowflake at a speed the company did not anticipate even two years ago.
Rai positions this as an India-first model of ecosystem acceleration.
“When partners build on Snowflake, they do not just deliver services. They deliver products. They create accelerators. They create domain solutions. That’s how industries become data ecosystems — not isolated digital islands.”
Companies like FirstHive have built their entire next-generation CDP on Snowflake. Several Indian startups are creating global SaaS products using Snowflake as a native data and AI engine. The pattern is consistent: India is becoming not just a deployment hub, but a product creation hub.
BFSI: India’s Most Transformed Snowflake Sector
Financial services remains Snowflake’s strongest Indian cohort. Asset management companies, in particular, have embraced Snowflake Marketplace for real-time access to global datasets from Bloomberg, FactSet, Refinitiv and dozens of niche data providers.
The bigger breakthrough, however, is happening with India-origin datasets.
“We are now onboarding Indian datasets directly onto Snowflake,” Rai reveals. “For AMCs, insurers, and capital market firms, this changes the game. They don’t need ETLs, manual file transfers, or heavy pipelines. Data becomes live.”
This shift has triggered a new decision-making paradigm: 24×7 NAV availability, instant KYC validation, near-real-time reconciliation, continuous fraud surveillance, and contextual advisory models for wealth and retail customers.
“Speed of data access becomes a compliance capability,” Rai notes. “Not a technical one.”
Manufacturing: Where AI, OT and IoT Converge
India’s manufacturing sector is quietly becoming one of Snowflake’s most promising growth levers. The reason is simple: manufacturers are finally breaking open their OT (Operational Technology) data silos.
“Manufacturers and their suppliers are sharing data seamlessly,” Rai explains. “Inventory visibility, production telemetry, quality data, vendor performance — everything can now be shared with zero friction.”
By unifying IT, OT, IoT and telemetry into a governed data layer, Snowflake is enabling predictive maintenance, real-time asset performance monitoring, SKU-level production optimisation, and context-aware AI agents for shop-floor decision support.
In some early deployments, AI agents are analysing IoT signals from hundreds of machines simultaneously, predicting failures hours before they happen.
“This is the beginning of AI-native manufacturing,” Rai says. “Not AI on top of data — but AI inside the data supply chain.”
Snowflake’s AI Advantage: The Data Never Leaves
With AI tools multiplying at record pace, CIOs are overwhelmed with options. Snowflake’s differentiation is strategic simplicity: AI comes to the data, not the other way around.
“Enterprises don’t want to move sensitive datasets to a dozen different tools,” Rai asserts. “Every time you move data, governance risk grows. With Snowflake, we invert that logic — the LLMs come to your data. Nothing leaves the platform.”
The model strengthens governance, reduces legal risk, and compresses AI deployment cycles from months to days.
Snowflake Intelligence — the company’s conversational enterprise engine — is already in live deployments across India. It allows employees to ask natural-language questions and instantly obtain insights grounded in enterprise data.
“This moves organisations from dashboards to decisions,” Rai says. “The semantic layer eliminates ambiguity, reduces hallucinations, and ties AI outputs back to trusted enterprise data.” Sales teams are using it to generate contextual customer insights. HR teams are querying workforce patterns. Cybersecurity teams are analysing logs at unprecedented scale. Customer service teams are retrieving unified customer histories through simple English prompts. The breadth of use cases indicates a common theme: if the data sits in Snowflake, the intelligence becomes instantly accessible.
Another pivotal development is SAP’s Business Data Cloud on Snowflake. It allows enterprises to unify SAP and non-SAP data without ETL and with zero-copy integration.
“For CIOs, simplified SAP access has been a multi-year aspiration,” Rai says. “We are finally making that a reality. You can operationalise SAP data for AI without creating more data copies or more pipelines.” This directly addresses one of India Inc.’s most persistent challenges — reconciling diverse datasets spread across SAP, legacy systems and homegrown applications.
OpenFlow: Solving India’s Biggest Data Engineering Problem
Perhaps the most consequential innovation for India is OpenFlow — Snowflake’s unified ingestion framework capable of pulling structured, unstructured, batch, streaming and IoT data from more than 300 sources.
“One of the biggest bottlenecks Indian enterprises face is fragmentation at the data ingestion layer,” Rai says. “OpenFlow unifies all of that. It makes pipelines simpler, safer and infinitely more scalable.”
The impact will be most dramatic in industries drowning in sensor data — logistics, EV fleets, energy utilities, factories and infrastructure.
Snowflake is also seeing explosive interest in applying generative AI to unstructured data: legal documents, emails, maintenance logs, PDFs, SCADA outputs. Rai shares a story of a large insurer that ingested hundreds of thousands of legal-case PDFs into Snowflake. AI uncovered fraud patterns by finding recurring witness names across multiple cities — a correlation humans would never detect manually. “That’s the power of unstructured intelligence,” Rai notes. “Unstructured data is India’s hidden advantage.”
Telemetry: India’s Next Multi-Billion-Dollar Data Frontier
India’s commercial mobility ecosystem is generating millions of data points every minute — fuel consumption, tyre health, engine load, braking patterns, route variations. Fleet operators are beginning to use Snowflake to aggregate, enrich, and run AI on this telemetry.
“India is sitting on a mobility data goldmine,” Rai says. “Once this data is consolidated and governed, AI-led optimisation becomes inevitable.”
Snowflake expects similar adoption across industrial IoT, energy grids, and smart infrastructure.
ISVs: The Next Global SaaS Wave Will Come from India
Snowflake sees a generational opportunity in India’s ISV ecosystem. Dozens of Indian SaaS providers are building products on Snowflake designed for global scale. Razorpay’s DataSync — a real-time, governed data-sharing platform — is often cited as the flagship example. Razorpay, for instance, has created a product called DataSync on Snowflake, which allows all their 100,000 e-commerce partners to look at transaction and UPI data almost in real time, for fraud management, transaction reconciliation, all of that, versus the traditional method of ETLs and the slightly cumbersome exchange of data, which usually happens.
“When an ISV builds on Snowflake, they are not just customers,” Rai says. “They become part of the Data Cloud itself. They create new categories, not just new features.”
Rai gives the example of a company called Bijli, which works with utility companies . The company has built a platform on Snowflake where they get data from utilities, run AI/ML algorithms on top of it, and provide that information back to the utility companies.
India Moves Into the Core of Snowflake’s Global Strategy
As enterprises modernise their data estates, centralise governance, and adopt AI across operations, Snowflake believes India is entering a multi-year supercycle.
“Most Indian enterprises still have their core data on-premise,” Rai observes. “As that shifts to cloud and consolidates on Snowflake, AI adoption will explode. India is going to move faster than any mature market.”
For Snowflake, India is no longer a regional opportunity. It is a strategic centre of gravity shaping the company’s roadmap — from AI innovation and partner-driven solutions to global product development and industry ecosystem building.