End-user spending on public cloud services in India is forecast to grow 28.1% to total $17.5 billion in 2026, up from $13.7 billion in 2025, according to Gartner, Inc, a business and technology insights company.

“Strong enterprise demand for AI-ready cloud infrastructure is redefining cloud investment priorities in India, driving the next phase of public cloud spending growth,” said Ashish Banerjee, Sr Principal Analyst at Gartner.

This was further fuelled by rising demand for application modernisation, digital sovereignty, digital service delivery and more scalable, consumption-based IT models, as organisations move toward more advanced and strategic cloud use cases.

“Cloud adoption is becoming more tightly aligned with business goals, such as improving productivity, accelerating innovation and go-to-market speed, enhancing customer experience and strengthening business resilience,” said Mr. Banerjee.

The rising need for AI-ready infrastructure, including GPUs, high-performance compute, high-speed networking, scalable storage and always-on inference capacity, was amplifying IaaS adoption and driving higher spending in this segment, Mr. Banerjee further said.

Infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) are expected to be the fastest-growing segments in India’s cloud market, with IaaS projected to grow 40% in 2026, followed by PaaS at 25.4%, as per Gartner analysis.

PaaS is the largest spending category for Indian organizations in 2026, and is forecast to reach $6.4 billion, as enterprises rebuild their technology foundations to support AI-driven initiatives.

Arunasree Cheparthi, Sr Principal Analyst at Gartner said, “Organizations are accelerating adoption of AI-driven technologies to unify data, connect systems, speed up development and enable real-time digital interactions, driving PaaS growth beyond cloud migration toward platform-led execution.’’

Also, SaaS is expected to exhibit more moderate growth in 2026. “This reflects its established adoption base, as enterprises optimize licenses, rationalize usage and shift incremental spending toward infrastructure and platform capabilities required to scale workloads and operationalise AI at scale, Ms.Cheparthi commented.