AI reshapes Indian SaaS, leaner models key to survival

BENGALURU: The Indian Software-as-a-Service (SaaS) boom, once fuelled by easy capital and relentless expansion, faces a reality check. As global IT budgets tighten and AI disrupts traditional development cycles, startups are compelled to eliminate cost overheads, reconsider pricing strategies, and adopt leaner product models to survive the competition.
While founders raced to adapt and investors pushed for profitability, the current shakeout is set to redraw the contours of India's SaaS landscape, separating the truly innovative from those riding the wave of past growth. Manav Garg, Co-founder and managing partner at Together Fund, described the shift as fundamental. “Agentic-first and API-first solutions were coming. An agent could automate about 30% to 70% of human work as we speak today, and it would further increase. You had to think of that paradigm and start building solutions around it,” he said. Garg also noted that Indian companies already reduced up to 30% of development costs through automation in engineering, either by avoiding additional hiring or cutting costs where necessary.
This view aligned with Zoho co-founder Sridhar Vembu's recent post on X, where he argued that the current SaaS slowdown was not driven by AI, but by decades of inefficiencies in enterprise IT spending that were now being exposed as global IT budgets shrank. “A large number of IT jobs in India came to depend on those original inefficiencies and the multiplied inefficiencies,” Vembu wrote. However, while Vembu’s thesis struck a chord with many in the ecosystem, some industry leaders believe that AI is already playing an active role in increasing productivity and reducing cost.
Abhinav Johri, partner at EY India’s technology consulting division, offered a broader view of the structural challenges within global tech spending. “Global tech spends are still structured to cater to ‘run’ rather than ‘grow and innovate.’ The presence of legacy tech, long-term managed service contracts, and the inability to pivot to a full-fledged product-led IT function are key reasons for this skewed spending. While success has been seen in areas like data, cloud, and engineering, large-scale cloud migrations often fail to deliver expected value due to the lack of a cohesive strategy that addresses the heterogeneous application landscape,” Johri explained.
Johri added that provider companies are adapting differently based on their positioning. While enterprise app providers like Salesforce and ServiceNow focus on enhancing existing features, core technology players like Snowflake and Databricks curate use-case-based capabilities. New-age SaaS companies, meanwhile, are capitalising on the gaps left by legacy players, offering more flexible and consumable solutions.
Jaspreet Singh, founder and CEO of Druva, stated that SaaS companies increasingly adopted consumption-based models. “We had not seen cost-cutting measures yet, but there was a general trend toward a consumption model for better agility and alignment to business goals. Druva’s consumption model resonated with customers and helped us gain market share while most competitors still sold hardware,” Singh said.
On the engineering side, Druva leveraged AI tools like Cursor and Claude for coding, though Singh said that “while writing code got easier, the integration, security, and governance aspects needed to be carefully balanced. We expected this would get much easier and efficient in the next 6-12 months.” Shobhit Jain, managing director and head of enterprise technology and services at Avendus Capital, believed the rise of AI drove consolidation in the SaaS space. “Niche AI-driven services replaced point SaaS solutions in large enterprises, helping them streamline tech stacks and improve efficiency. While foundational SaaS providers continued to grow, smaller point solutions that didn’t integrate AI struggled to stay relevant,” Jain said.
Arun Chandrasekaran,distinguished vice-president and analyst for AI at Gartner, pointed out that the disruption wasn’t limited to product innovation. “SaaS startups needed to enable embedded AI within their applications but do so in a way that preserved customer data privacy. The changing global political landscape also ushered in more customer requirements around data sovereignty that SaaS providers needed to address proactively,” he said.
Garg believes that AI fundamentally changed the nature of SaaS development. “Previously, you’d need 20-30 people to build a SaaS company. Now, with AI, three or four people could build a fully functional SaaS product,” he added.
With the cost of capital rising and enterprise clients demanding more flexible pricing and AI integration, SaaS startups were under pressure to deliver both efficiency and innovation. As Chandrasekaran put it, “We were certainly witnessing an era of disruption in SaaS – how deep and quick this disruption would be yet unknown. What we did know was this would create new winners and losers among SaaS providers.” However, the VC and growth funding in software and SaaS, including GenAI, rose 1.2 times to $1.7 billion from 2023 to 2024 as high-quality scaled assets entered the market, showed the India Venture Capital Report 2025 by Bain & IVCA.