By Piyush Gupta, VP Sales and Business Development for India, APAC and the Middle East, Vultr
In 2026, Indian enterprises will adopt heterogeneous GPU portfolios combining general-purpose and specialized processors to balance performance, efficiency, and cost. Success will hinge on flexible AI frameworks that enable rapid deployment, testing, and iteration of AI models, unlocking innovation and measurable business outcomes. The GPU cloud market is consolidating, with over 80% of high-performance GPUs deployed by a few neocloud providers who leverage strong capital and scale to meet soaring AI demand. India has deployed over 80,000 GPUs through the IndiaAI Mission. However, providers lacking sufficient capital and infrastructure face competitive challenges. Focused infrastructure upgrades, grid power investment, data center cooling, and accelerated indigenous GPU production, with trials planned by end-2025, are critical to sustaining growth and reducing import dependence.
The “For What?” Year of the Sovereign Cloud
Until now, sovereign cloud in India has been regarded as a vital yet evolving imperative—recognized for its importance but lacking fully defined frameworks, clear regulations, and prioritized execution. Despite strong government backing through initiatives like the IndiaAI Mission and Digital India programs, progress has been restrained by regulatory ambiguities and infrastructure challenges. In 2026, India is expected to accelerate sovereign cloud adoption by integrating it into national digital strategies, linking deployments to the growth of local startups, academic innovation, and AI research ecosystems. This year will mark a shift where India’s sovereign cloud moves from an aspirational concept to a purpose-driven infrastructure cornerstone, enabling data sovereignty, security, and digital self-reliance tailored to India’s unique economic and technological landscape.
The Rise of the Multiple Hyperscaler
Enterprises will shift beyond traditional hyperscalers to “multiple hyperscalers”—new cloud providers blending public cloud scale with specialized AI infrastructure. This enables high-performance workloads, cost efficiency, and openness. Multicloud strategies will distribute workloads for optimal performance, compliance, and budget, favoring platforms with seamless interoperability and no lock-in to drive Indian business agility.
The enterprise AI rebuild shows real impact
Enterprises will finally move from AI strategy to execution. Approaches such as platform engineering will drive faster integration, while decision-making shifts from data analysts to developers who prefer open-source over black-box tools. Meanwhile, open ecosystems, alternative hyperscalers, and silicon diversity are reducing the barriers to retooling and scaling, minimizing risk and vendor lock-in. For the first time, significant use cases and success stories will emerge, demonstrating real-world value and providing examples that other organizations can follow.
Agentic AI at the edge puts industries first
Edge AI will be highly industry-specific, supporting use cases that demand domain expertise, such as drones inspecting nuclear power plants with on-device models for real-time detection. Broad deployment of general-purpose AI agents at the edge remains a longer-term goal, with adoption occurring incrementally, use case by use case, and industry by industry.