India cloud spend set to hit $17.5 billion as AI drives infrastructure and platform investments

Enterprises redirect spending toward AI-ready compute and platforms, creating new opportunities for cloud partners.

India's public cloud market is entering a new phase, with spending increasingly driven by the infrastructure and platforms required to support production-scale AI workloads. According to Gartner, end-user spending on public cloud services in India is projected to reach $17.5 billion in 2026, up 28.1 percent from $13.7 billion in 2025.

“Strong enterprise demand for AI-ready cloud infrastructure is redefining cloud investment priorities in India,” said Ashish Banerjee, senior principal analyst at Gartner. “This is further fueled 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.”

The growth is not uniform across cloud segments, and where enterprises are allocating spending offers insight into how cloud strategies are evolving.

Gartner expects Infrastructure-as-a-Service (IaaS) to lead the market with 40 percent growth in 2026 as organisations invest in AI-ready compute, networking and storage. Platform-as-a-service (PaaS) is forecast to become the largest cloud spending category at $6.4 billion, while SaaS growth slows to 18.9 percent as enterprises increasingly direct new spending toward infrastructure and platform capabilities.

The numbers suggest that a growing share of cloud spending is moving toward the infrastructure and platform layers required to support AI workloads at scale.

Cloud is no longer serving primarily as a destination for migrated workloads. Increasingly, it is becoming the execution layer for AI initiatives.

AI workloads pull demand toward infrastructure and platforms

The strongest growth driver is the need for AI-ready infrastructure.

Enterprises are investing in GPU-backed compute, high-performance networking, scalable storage and inference capacity to support both AI development and deployment. Gartner expects this demand to drive significantly higher IaaS consumption across the market.

At the same time, PaaS is emerging as the largest spending segment as organisations modernise applications, unify data, connect systems and accelerate development efforts around AI use cases.

The trend indicates that enterprises are investing not only in cloud applications but also in the foundational technology layers needed to operationalise AI at scale.

As organisations move beyond experimentation and pilot projects, many are rebuilding technology environments to support continuous, data-driven and AI-enabled operations.

Cloud adoption shifts from expansion to execution

The spending pattern further reflects a shift in enterprise cloud maturity. Many organisations have already completed phases of cloud adoption. The challenge now is operating complex hybrid, multicloud and AI-enabled environments while maintaining governance, performance and cost control.

This is creating new operational pressures.

Hybrid architectures are difficult to manage as workloads, applications and data spread across multiple environments. AI initiatives span different cloud platforms, with model development, inference and data often residing across separate systems.

Governance is becoming a concern as enterprises navigate compliance requirements, data residency obligations and growing accountability around AI deployments.

Gartner predicts that by 2030, over 60 percent of enterprises will perform intensive AI model activity in one cloud while leveraging data in another, up from less than 10 percent today.

At the same time, organisations are placing greater emphasis on FinOps practices as infrastructure-heavy AI workloads introduce new cost-management challenges.

The conversation is shifting from cloud adoption to cloud operation.

SaaS growth moderates as priorities evolve

SaaS remains a significant part of enterprise cloud spending, but Gartner expects growth to be more moderate than in the infrastructure and platform segments.

According to Gartner, enterprises are optimising licences, rationalising usage and directing incremental spending toward infrastructure and platform capabilities needed to support AI initiatives.

This signals that as organisations scale AI deployments, they are placing greater emphasis on data, infrastructure and integration capabilities alongside applications.

Applications remain critical, but enterprises increasingly require the underlying platforms and infrastructure that support real-time processing, model orchestration and cross-system workflows.

Channel opportunity moves up the stack

For the channel ecosystem, the opportunity extends beyond cloud consumption growth itself.

The expansion of AI-driven infrastructure is creating demand for migration, modernisation, integration, governance and ongoing cloud operations services. As environments become more complex, enterprises are increasingly relying on partners to execute and manage these initiatives.

System integrators, managed service providers and cloud specialists are finding opportunities in areas such as hybrid cloud management, workload optimisation, AI infrastructure operations, governance frameworks and cost management.

The partner's role is also evolving.

Instead of deploying cloud environments and moving on, partners are expected to manage performance, control costs, ensure compliance and continuously optimise AI-driven workloads. Engagements are becoming longer-term and closely tied to business outcomes.

As infrastructure and platform spending expands, a larger share of value is likely to shift toward services delivered on top of those environments.