As Indian enterprises are transitioning from experimental AI pilots to production-scale, they are forced to revamp their digital infrastructure. With rising GPU costs and mandatory sustainability reporting, cloud bills that looked reasonable during experimentation are expanding sharply at scale. The result is a fundamental rethink of cloud economics across boardrooms in India.
The new cloud math
For years, Indian enterprises treated cloud spending as a growth investment focused on speed and agility. That conversation is changing. CXOs now scrutinise efficiency, predictability and governance, especially as AI workloads push infrastructure costs significantly higher.
FinOps adoption is one of the clearest signals of this shift. Organisations want better visibility into cloud consumption, stronger workload optimisation and tighter control over spend. The goal has moved from scaling quickly to scaling sustainably and intelligently.
Multi-cloud and hybrid strategies follow the same logic. Rather than relying on a single public cloud, businesses are distributing workloads by performance, compliance and cost requirements. Public cloud suits elastic workloads, while private cloud and bare metal infrastructure make more sense for predictable, compute-intensive or regulated environments where long-term economics are more favourable.
Sustainability is now a third variable in the equation. India’s Business Responsibility and Sustainability Reporting (BRSR) framework requires the top 1,000 listed companies to disclose ESG performance, including Scope 2 emissions. The pressure is now tightening. Reasonable assurance of BRSR Core extends to the top 500 listed companies from FY 2025-26 and reaches the top 1,000 by FY 2026-27.
SEBI has eased some adjacent rules in its March 2025 circular, making value chain ESG disclosure voluntary for the time being, though the direction of travel toward verified, audit-ready emissions data is set. As assurance requirements climb, emissions linked to cloud infrastructure will increasingly reach CXO-level discussions, and enterprises are weighing providers on energy efficiency and emissions transparency alongside pricing and performance.
Performance without the virtualisation tax
Bare metal architecture is gaining relevance for AI because it removes the performance penalties of virtualised environments. In GPU-intensive workloads, the virtualisation tax can range between 5 and 25 percent compared to bare metal. That overhead may be manageable for traditional applications, but it becomes costly in large-scale AI training environments where thousands of GPU operations run simultaneously across distributed systems.
Direct access to dedicated CPUs, GPUs, memory, and storage delivers lower latency, more consistent throughput and better workload isolation. It also reduces the noisy neighbour effect common in shared environments, where resource contention from other tenants creates unpredictable latency that directly affects training and real-time inference.
The mature approach is workload-specific. Many organisations run bare metal for high-performance training while using Kubernetes and containerised environments for inference and orchestration flexibility. Purpose-built bare metal using technologies such as water cooling can also improve thermal efficiency and cut energy consumption at GPU cluster scale, supporting broader ESG and BRSR goals.
Sovereign by design
Sovereign cloud has moved from a niche government requirement to a mainstream enterprise decision in India, driven by the Digital Personal Data Protection (DPDP) Rules and tightening regulatory focus on data residency and third-party risk. Sovereignty cannot be retrofitted later; it must be architected into the cloud strategy from the start.
Sovereignty and cost efficiency can coexist. Tiered models keep sensitive customer and transactional data onshore under sovereign controls, while non-sensitive workloads run on global infrastructure for scalability and cost optimisation. The more important question for providers concerns legal jurisdiction, beyond simply where data is stored. That distinction matters for audit readiness, regulatory confidence and long-term trust.
The risk of vendor lock-in
Vendor lock-in has become a long-term financial and operational risk, as licensing structures within proprietary ecosystems continue to evolve unpredictably. Open standards such as OpenStack and Kubernetes reduce that exposure. OpenStack offers control and customisation without dependency on proprietary infrastructure, and it lets organisations build expertise around portable technologies, reducing dependency on vendor-specific APIs. Kubernetes adds a workload portability layer across private cloud, public cloud and on-premises environments.
Enterprises should still be realistic. Kubernetes now runs in production at 82 percent of organisations, yet many teams face challenges around operational complexity, skills gaps, security and scalability. Open standards reduce strategic lock-in, but they require investment in platform engineering and operational maturity.
The realities of scaling AI
The gap between AI pilots and being production-ready is largely an infrastructure challenge. Distributed GPU training is often constrained more by network throughput than by compute capacity, and training environments require high-bandwidth sequential reads that conventional enterprise storage was never designed to support. Cost architecture surprises teams late: hyperscale egress charges of US $0.08 to US $0.12 per GB can significantly inflate AI project costs once workloads move into production.
Security planning deserves the same foresight. Research shows 43 percent of organisations do not know what cryptographic assets they own, making inventory the biggest barrier to quantum readiness. Building cryptographic agility into cloud strategies today avoids costly redesigns later.
The enterprises that will have a successful cloud strategy will treat infrastructure choices as deliberate, workload-specific decisions. Compliance, resilience and cost efficiency can reinforce each other when the architecture is designed for all three from the beginning.
Authored by Shiv Kumar DVS, Cloud Solutions Architect APAC, OVHcloud