As India accelerates toward becoming a global AI powerhouse, cloud infrastructure is undergoing a fundamental transformation, from a backend utility to the very foundation of innovation. In this conversation, Piyush Gupta, Vice President for India, APAC & the Middle East at Vultr, outlines how AI-native workloads, GPU-powered compute, and edge infrastructure are reshaping enterprise cloud strategies. He also shares his perspective on India’s growing role in the global AI infrastructure ecosystem and what enterprises must prioritise today to stay competitive in the next wave of digital transformation.
How do you see cloud infrastructure evolving from a support function to becoming the core foundation of India’s AI economy by 2026? What structural shifts are driving this transition?
Cloud infrastructure is no longer a backend utility, it is becoming the foundation on which AI economies are built. In India, this shift is being driven by the move from centralised IT systems to AI-native, distributed computing environments, where infrastructure must support high-performance, low-latency workloads at scale.
Structural changes, such as increased investments in data centers, rising demand for GPU-powered compute, and the need for local data residency, are accelerating this evolution. Enterprises are now designing around workloads rather than servers, which is fundamentally changing how cloud is consumed. As AI adoption deepens, infrastructure that can seamlessly support experimentation, training, and production at scale becomes mission-critical rather than optional.
These shifts will mean that a lot of foreign demand will be served from India, and the scale will increase. From Megawatts, it will now be multi, Gigawatts of AI Compute across India.
Why will cloud strategy in 2026 be shaped by AI workload readiness rather than storage or virtualisation alone?
Cloud strategy is shifting from traditional metrics like storage and virtualisation to AI workload readiness, because AI introduces fundamentally different infrastructure requirements. These workloads demand high-performance compute, GPU acceleration, real-time data processing, and scalable orchestration environments like Kubernetes.
Organisations are now thinking in terms of how quickly they can move from model experimentation to production, and whether their infrastructure can support dynamic scaling, data integration, and automation. This is why composable and cloud-native architectures are gaining traction, as they allow enterprises to align infrastructure directly with workload needs rather than static resource provisioning.
What’s the role of independent cloud infrastructure platforms in expanding choice, performance, and cost predictability?
Independent cloud platforms play a critical role in rebalancing the cloud ecosystem. Today, many enterprises face challenges such as vendor lock-in, opaque pricing, and limited flexibility with hyperscalers. Independent providers address this by offering transparent pricing, modular infrastructure, and greater interoperability.
They enable businesses to avoid dependency on a single ecosystem, while delivering high-performance infrastructure with predictable costs. This is particularly important in AI, where compute costs can quickly escalate. By simplifying pricing models and eliminating hidden charges, independent clouds help restore confidence and enable organisations to scale innovation without financial uncertainty.
With GPU-accelerated workloads becoming central to AI innovation, how can cloud providers ensure democratised access across startups, SMBs, and developers in India?
Democratising AI starts with making high-performance GPU infrastructure accessible and affordable. Cloud providers can achieve this by offering on-demand GPU resources without long wait times, flexible scaling models, and cost-efficient pricing structures.
Equally important is investing in developer ecosystems, documentation, and easy onboarding, so startups and SMBs can build and deploy AI solutions without deep infrastructure expertise. By combining accessibility with affordability and ease of use, providers can ensure that innovation is not limited to large enterprises but extends across India’s developer and startup ecosystem.
As enterprises adopt hybrid and multi-cloud strategies, how important will edge infrastructure and data sovereignty be?
Edge infrastructure and data sovereignty will be central pillars of India’s AI-driven cloud landscape. As applications become more real-time and latency-sensitive, processing data closer to its source through edge computing becomes essential.
The industries and use cases using edge extensively will be Media & Entertainment, E-Commerce, News, Cloud Gaming, AdTech, EdTech, Fintech, Autonomous Vehicles, Healthcare, Oil & Gas, Manufacturing, Virtualized radio networks and 5G (vRAN), Content Delivery, Smart Homes, Video Analytics – Facial Recognition, vRAN, AR/VR, Video Optimization/Editing, Smart Campus, Smart Stadium, and Drones.
At the same time, data sovereignty requirements are driving enterprises to ensure that sensitive data remains within national borders, especially in regulated sectors. The combination of edge computing and sovereign cloud capabilities enables organisations to balance performance, compliance, and scalability, making hybrid and multi-cloud strategies more effective and future-ready.
AI workloads are both compute-intensive and cost-sensitive. How can organisations balance performance, scalability, and cost optimisation?
Striking this balance requires a shift toward composable and dynamically scalable infrastructure. Organisations need environments where they can allocate resources in real time based on workload demands, ensuring they only pay for what they use while maintaining performance.
Leveraging GPU-optimised infrastructure, automation, and workload orchestration tools helps maximise utilisation and reduce waste. Cost predictability is equally critical, as transparent pricing models allow businesses to plan effectively without unexpected overruns. Ultimately, the goal is to create an infrastructure layer that is both high-performing and economically sustainable for AI at scale.
India is emerging as a major digital economy. What role can it play in the global AI infrastructure ecosystem?
India is uniquely positioned to become a global hub for AI innovation, driven by its strong developer base, growing digital economy, and increasing investments in cloud and data infrastructure.
As AI adoption accelerates, India can contribute significantly by building scalable AI solutions, fostering startup innovation, and driving demand for localised infrastructure. Cloud providers play a key role by expanding regional data centers, enabling affordable access to AI infrastructure, and supporting local compliance needs, thereby strengthening India’s position in the global AI ecosystem.
What key capabilities should enterprises prioritise in their cloud infrastructure today to stay competitive over the next 3 to 5 years?
Enterprises should focus on building AI-ready, flexible, and future-proof cloud environments that can continuously adapt to evolving workload demands. This includes adopting composable infrastructure for dynamic resource allocation, leveraging GPU-enabled compute to power AI and ML workloads, and embracing cloud-native architectures such as Kubernetes and containers for scalability and agility.
Strong data management and integration capabilities through data fabrics are equally critical to ensure seamless flow and usability of enterprise data. In addition, edge computing and low-latency infrastructure will play a key role in enabling real-time intelligence, while robust security, compliance, and data sovereignty frameworks will ensure trust and regulatory alignment.
Equally important is choosing platforms that offer ease of integration, developer-friendly tools, and predictable pricing, so that infrastructure acts as an enabler of continuous innovation rather than a constraint.