‘Cost efficiency with a transparent and predictable pricing model is our differentiator’: Piyush Gupta, VP, India, APAC and Middle East, Vultr

Vultr is accelerating AI infrastructure adoption by expanding access to GPU cloud resources, making high-performance compute more widely available. In an exclusive interaction, Piyush Gupta, VP, India, APAC and Middle East, Vultr tells Bhavya Bagga, Business Reporter, CXO Media about their push towards multi-cloud strategies and open ecosystems, as enterprises recognize the long-term risks of dependency on a single hyperscaler.

What are the solutions and services currently in the Vultr portfolio?

At Vultr, our portfolio is built around delivering simple, high-performance, and globally accessible cloud infrastructure. We offer a comprehensive suite of solutions, including cloud compute instances, bare metal servers, block and object storage, Kubernetes (managed containers), and GPU-powered cloud infrastructure tailored to manage AI and ML workloads. 

A key focus area for our business is around GPU-as-a-service that enables enterprises and startups to access high-performance AI compute without heavy upfront investments. Additionally, our platform supports developer-friendly tools, APIs, and global data center presence, ensuring scalability and flexibility across use cases.

How do you ensure budgetary costs are within limits and a reasonable TCO for organizations looking at cloud migration?

We address cost concerns through transparent pricing models, no hidden fees, and near elimination of egress charges, which are often a major contributor to unpredictable cloud bills. In fact, we work toward significantly reducing or nearly eliminating egress-related costs to help make cloud spending more predictable and manageable. Unlike hyperscalers, Vultr focuses on predictable and simplified pricing structures that allow organizations to better plan and control their cloud spend.

Additionally, we advocate for multi-cloud and open architectures, helping businesses avoid vendor lock-in, optimize workloads across environments, and reduce long-term total cost of ownership (TCO). Our unique approach ensures customers pay only for what they use, with the flexibility to scale up or down based on demand.

What are your unique differentiators over other cloud solution providers?

We champion an independence-first, open cloud approach built on open standards and strong interoperability, enabling customers to avoid vendor lock-in and design flexible, future-ready architectures that can evolve with their needs.

Cost efficiency is also a key differentiator for us. Our pricing model is intentionally transparent and predictable, helping organizations reduce cloud cost complexities and avoid unexpected billing spikes. With simplified billing, no hidden infrastructure charges, and bandwidth models that include up to 10TB of free monthly egress and 11TB of high-performance, direct storage in dedicated VMs and in bare metal versions means customers can scale up to 15TB egress and 28TB of direct attached high-performance storage to optimize their overall cloud spend while maintaining performance at scale. 

At the same time, we are focused on democratizing AI infrastructure by making GPU cloud resources more affordable and accessible, addressing global supply constraints and pricing inefficiencies so that startups and enterprises alike can innovate and scale without traditional barriers.

Our approach is also increasingly recognized by leading industry analysts, with Vultr being featured in Gartner’s Magic Quadrant for strategic cloud platform services, reinforcing our position as a trusted alternative to hyperscale providers with a strong focus on performance, simplicity, and cost efficiency.

Unlike traditional hyperscalers, Vultr prioritizes developer simplicity, global accessibility, and performance parity without ecosystem lock-ins.

How have your conversations with CIOs and other tech leaders changed over time?

Over time, conversations with CIOs have evolved from basic cloud adoption discussions to far more strategic decisions around core infrastructure, cost governance, and AI readiness. Today, technology leaders are increasingly prioritizing the ability to avoid vendor lock-in, maintain architectural flexibility, and optimize cloud costs in a sustainable and predictable way.

This shift is also reflected in industry research. According to Gartner’s 2026 CIO Agenda insights, CIOs are increasingly focused on driving operational efficiency while balancing AI investments and modernization initiatives. Cost optimization, cybersecurity, AI enablement, and delivering measurable business outcomes have emerged as some of the top enterprise priorities for technology leaders globally.

A major shift we are seeing is the growing emphasis on building AI-ready infrastructure that can support advanced workloads at scale without significantly increasing operational costs. At the same time, digital sovereignty and data flexibility have become critical considerations, as organizations seek greater control, compliance, and resilience in how and where their data is managed.

There is also a stronger push toward multi-cloud strategies and open ecosystems, as enterprises recognize the long-term risks of dependency on a single hyperscaler. AI adoption has further accelerated these discussions, with CIOs looking for scalable, high-performance, and cost-efficient GPU access to support generative AI and large-scale data workloads, while ensuring infrastructure spending remains optimized and predictable.

What are the key pillars of your GTM structure and what are the focused initiatives planned?

Our go-to-market strategy is anchored on a direct GTM through structured and scalable reach out program as well as partner-led growth model, where we actively collaborate with system integrators, managed service providers (MSPs), and regional partners to scale reach and delivery. We have made deep investment in the developer ecosystem through a developer-first approach that prioritizes startups, developers, and digital-native businesses. 

Our focus is on providing easy access to scalable cloud and AI infrastructure that is simple to deploy, cost-efficient, and flexible enough to support rapid experimentation, application development, and AI innovation at scale.

A key focus area is accelerating AI infrastructure adoption by expanding access to GPU cloud resources, making high-performance compute more widely available. 

Geographically, we are strengthening our presence across India, APAC, and the Middle East as part of our long-term commitment to high-growth digital markets. APAC is a strategic region for Vultr, driven by accelerating cloud adoption, AI innovation, and rising demand for cost-efficient infrastructure.

India is especially important to us because of its fast-growing startup ecosystem, large developer community, and strong momentum in AI and digital transformation. As businesses scale AI and cloud workloads, there is increasing demand for flexible, high-performance, and transparent cloud infrastructure, and we are focused on enabling that growth with accessible and cost-efficient solutions.

These efforts are supported by initiatives such as deepening partner engagement, expanding local data   ecosystems, and enabling AI-first workloads for both enterprises and startups. Our growing local data center footprint helps reduce latency and improve performance by bringing compute closer to end users, enabling customers to run modern cloud and AI workloads more efficiently across regions. 

How is Vultr’s partner ecosystem designed in India?

In India, our partner ecosystem is designed to be inclusive, scalable, and value-driven, bringing together a diverse set of collaborators to accelerate cloud and AI adoption. We work closely with system integrators and managed service providers to deliver localized, high-impact solutions, while channel partners help us expand market reach across regions.

In parallel, we actively engage with AI startups and ISVs to co-build innovative, differentiated offerings on top of our infrastructure. The ecosystem is strengthened through continuous enablement, structured training programs, and co-selling opportunities, ensuring that partners are well-equipped to deliver tailored cloud and AI solutions that address India’s evolving enterprise and regional needs.

What major shifts are you observing in cloud infrastructure modernization across India and APAC?

We are witnessing a clear shift in enterprise cloud adoption toward cloud-native and containerized architectures, alongside rapidly growing demand for GPU-driven infrastructure to support AI workloads. 

Across India and APAC, AI infrastructure adoption is accelerating as enterprises move from experimentation to production-scale deployments. Industry reports from IDC and Gartner highlight that AI and GenAI have become top CIO investment priorities in 2026, with a significant share of IT budgets shifting toward AI infrastructure, data platforms, and high-performance compute. In India, this momentum is further driven by a strong startup ecosystem, rapid digital transformation, and increasing enterprise focus on automation and AI-led innovation.

Organizations are increasingly adopting multi-cloud and hybrid strategies to build more agile and AI-ready infrastructure stacks. At the same time, data sovereignty and regional infrastructure priorities are rising, with demand for localized cloud regions to ensure performance, control, and low-latency AI execution.

This shift aligns strongly with Vultr’s value proposition of cost-efficient, high-performance, and developer-friendly infrastructure. As AI adoption scales, we see strong growth opportunities in providing accessible GPU infrastructure that reduces complexity and cost barriers, enabling both startups and enterprises to scale AI workloads efficiently across India and APAC.

What role do cloud providers like Vultr play in democratizing AI adoption?

Cloud providers including Vultr play a critical role in accelerating AI adoption by removing key barriers related to cost, accessibility, and infrastructure complexity. 

This is achieved by offering affordable GPU infrastructure, enabling organizations to access high-performance compute without heavy upfront investment, while also supporting open-source AI models that encourage innovation and collaboration across the ecosystem. 

By eliminating restrictive pricing practices such as egress fees and promoting open cloud ecosystems built on interoperability, we help businesses avoid vendor lock-in and maintain architectural flexibility. 

Together, these efforts ensure that startups and mid-sized enterprises can innovate and scale freely, without being constrained by capital limitations or proprietary dependencies, ultimately fostering a more inclusive and democratized AI landscape.

What are the most significant opportunities at the intersection of cloud and AI over the next 3–5 years?

Over the next 3–5 years, the biggest opportunity at the intersection of cloud and AI will come from AI shifting decisively from pilot projects to production-scale deployment. As highlighted in Deloitte’s State of AI in the Enterprise 2026, enterprises are moving beyond experimentation and increasingly embedding AI into core business processes, with a stronger focus on ROI, scalability, and operational reliability. This transition is fundamentally reshaping infrastructure requirements, as AI moves from isolated use cases to always-on, business-critical systems.

As AI matures, cloud demand is shifting toward infrastructure that is scalable, high-performing, and easy to operationalize at scale. At the same time, regional AI ecosystems in markets like India and APAC are expanding, driving demand for localized, low-latency infrastructure to support real-time AI applications.

This creates a strong opportunity for independent cloud providers like Vultr to enable faster AI adoption through simple, on-demand, and cost-efficient infrastructure. As AI becomes production-first, the ability to deliver flexible and accessible cloud environments will be key to helping enterprises and startups scale AI without complexity or lock-in.

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