India is running one of the most ambitious digital races in the world. Between the rapid rollout of the ₹2,000-crore IndiaAI Mission, the rise of AI-specialised Special Economic Zones (SEZs) and the expansion of national skilling platforms, organisations of every size are modernising at unprecedented speed. But behind this momentum lies a growing blind spot: The cloud costs powering this transformation are becoming harder to understand, predict, and control.
Cloud has transformed from an IT cost centre into the scalable compute and infrastructure foundation essential for modern AI workloads. Training and serving AI models requires elastic compute, high-performance GPUs, and large, distributed datasets—capabilities that cloud platforms are uniquely designed to deliver.
The move toward GPU-heavy workloads, larger datasets and real-time AI applications means that India’s cloud consumption is rising sharply. Projections for India show continued double-digit growth in public cloud services, driven primarily by AI adoption, modernisation efforts and expanding digital public infrastructure. The challenge is that, while cloud environments scale quickly, visibility doesn’t always scale with them. The result is a widening gap between what organisations think they’re spending and what they actually are.
Every business wants to move faster. AI promises efficiency, better customer experiences and a competitive edge in a crowded market. India’s leading banks, fintechs, e-commerce platforms and health-tech companies are already deploying AI across fraud detection, personalisation and operational automation. But as AI adoption expands, cloud environments (such as containers, GPUs, storage, networking) become more complex. And complexity always comes with a cost.
Across the industry, teams are struggling to right-size infrastructure, forecast resource needs and track the growing number of services supporting AI workloads. And this is a global problem. Recent research shows that 83% of container costs across organisations worldwide are tied to idle resources (capacity that’s paid for but unused). Another common drain is availability zone traffic, with most organisations incurring charges simply because resources are distributed across zones without engineers realising the financial impact.
These issues often go unnoticed because cloud environments expand and shift in ways that are difficult to track without unified visibility. The intention is innovation, but without the right oversight, companies end up paying for far more than they consume.
The truth is that tech debt doesn’t always look like legacy code anymore. These days, it hides in the cloud. Every time a team spins up resources just to be safe, leaves a pipeline running longer than it needs to, or forgets about a storage bucket created during a sprint, it adds to a growing pile of invisible debt. None of it feels urgent in the moment, but it adds up fast: compute that sits idle, pipelines that grow messy over time, storage that nobody cleans up and workloads placed wherever they happen to land. And eventually, the bill shows up in ways no one enjoys.
Industry analyses over the last two years show that companies can lose 20 to 30% of their cloud budgets to unoptimised or underutilised resources. In India’s high-growth environment, this has real consequences. Every rupee diverted to cloud waste is a rupee not spent on strengthening AI models, improving customer experience or building new products.
The irony is that cloud overspend often grows fastest in companies that are the most aggressive adopters of AI. The more innovative the business, the more likely it is to accumulate hidden inefficiencies if visibility isn’t part of the modernisation plan.
For years, finance watched the cloud bill and engineering watched the dashboards—and everyone assumed that was efficient. Today, it’s anything but. With AI workloads scaling unpredictably, that old division creates more blind spots than clarity. I see this first-hand in conversations with customers: If cost and performance aren’t discussed in the same room, teams end up solving the wrong problems, twice as slowly.
The organisations that manage their cloud effectively are the ones where engineers can see performance, utilisation and cost in the same workflow. When they understand the cost impact of a high-throughput service, a persistent GPU node or a cross-zone data transfer, they make different choices—not slower, but smarter.
This is where observability becomes essential. True observability doesn’t stop at monitoring. It helps engineers understand the why behind behaviour: Why workloads spike, why a container sits idle, why a function scales unexpectedly, why a microservice quietly accumulates charges, and so on. When teams gain this clarity, optimisation becomes a continual, proactive practice instead of a scramble.
AI changes the economics of cloud overnight. The moment an organisation starts training or serving models at scale, the assumptions that worked for traditional workloads stop applying. And this is prevalent across industries: Teams that were meticulous with compute planning suddenly find themselves dealing with unpredictable spikes, GPU nodes that run longer than intended and storage growth that outpaces budgets.
Modern observability solutions—especially those powered by AI—can detect anomalies across utilisation, cost and performance in real time. They help teams avoid over-provisioning, reduce unnecessary GPU spend and surface patterns early. More than cutting costs for the sake of cutting, it’s about ensuring the cloud is being used in a way that supports innovation instead of silently consuming budgets.
India’s shift toward AI-driven systems has raised the bar for what modern infrastructure needs to deliver. But with every new workload and service, the operational noise grows louder. The real differentiator now is visibility—seeing the cost, performance and behaviour of your environment without guesswork. Teams that have this clarity cut through inefficiency quickly and build systems that scale without stumbling.
This article is authored by Namit D’Cruz, RVP Enterprise, India & SAARC, Datadog.
