New Delhi, Sept. 18 -- For Style Lounge, a Noida-based beauty-tech startup, the first cloud bill arrived like a rude awakening. The team expected their cloud costs to be steady and predictable but later realised that every extra minute, detour, and piece of data carried a price tag."It felt like we had ordered a short ride and been charged for a tour around the whole city," the founder, Deepak Gupta, recalled. The shock was not only about the size of the bill, but also included the hidden traps behind it. Idle test servers kept running in the background, quietly racking up charges. Large, unoptimised images were inflating data transfer costs. Powerful servers, meant for scale, sat underused in the company's early days. It was their first taste of cloud bill shock, a growing reality for startups and enterprises alike. A term common in the CXO circles, cloud bill shock is the unexpected and substantial in cloud costs. It is often caused either by underestimating the resource consumption or due to hidden fees and unforeseen usage. Cloud bill shock is expected to be more pronounced as the industry moves to more resource-hungry AI and generative AI implementations. How AI is intensifying the blow A new Capgemini report highlights how companies are moving from capital-heavy IT spending to flexible, consumption-based models, with cloud, SaaS, and generative AI expected to claim 41% of IT budgets in the coming year, up from 29% today. Over 70% of the surveyed executives say cloud scalability and performance are critical for growth and competitiveness. However, concurrently, executives are struggling to manage the financial side of this shift. More than 80% report sharp cost increases across cloud, SaaS, and Gen AI, which is driven by inflation, rising infrastructure demands, and new AI workloads. Three-quarters of companies overshot their public cloud budgets last year by an average of 10%. Nearly 70% exceeded their Gen AI budgets, while more than half overspent on SaaS. Take, for example, Bengaluru-based AI recruitment firm Incruiter. Founded in 2018, it was one of the 45 startups under Microsoft for Startups, as part of which, the company received $100,000 in cloud credits. "For almost a year and a half, that covered all our needs. However, in March, those credits ran out. By April, our first real cloud bill landed, it had hiked from zero to nearly Rs.10-12 lakh in a single month," said CEO and co-founder, Anil Agarwal. This was because the company was running multiple AI models and was using high compute power to ensure the smooth running of its products. "Without credits to cushion us, the true cost became visible. We had missed setting up budgeting and alerting features to flag overspending, so the spike went unchecked." Since then, the company has been auditing the system and reconfiguring workloads, bringing costs down to nearly one-sixth. As per experts' estimates, over 60% of enterprises overshoot their cloud budgets, in some cases by 20-30%. The rise of data-intensive workloads such as Generative AI is further amplifying this unpredictability. " Training and inference at scale demand massive compute and storage resources, while the continuous movement of data across regions multiplies egress costs. For many enterprises, the unpredictability of GenAI costs has outpaced existing governance models," said Abhinav Johri, Technology Consulting Partner, EY India. Concurring with this, Rubal Sahni, AVP - India and Emerging Markets, Confluent said that generative AI has shifted cost model from predictable workloads to 'prompt-driven chaos'. "Large Language Models (LLMs), vector databases, continuous context enrichment, require low-latency, high-bandwidth architectures.One poorly structured prompt or low-optimized retrieval pipeline can launch a cascade of GPU cycles and API hits." Maturity gap in cloud cost management With maturing AI landscape, enterprises are at an inflection point. Analysts point to three main barriers: finance teams control budgets, engineering teams control architecture, but no one bridges the gap; companies being overwhelmed by multiple billing dashboards; and retrospective billing. These factors result in a maturity gap in cloud cost management. Many teams lack deep financial engineering skills or fail to enforce guardrails across business units. Analysts opine that while there is hope that cloud providers will simplify pricing, complexity is unlikely to disappear completely. Notably, according to the Capgemini study quoted above, many organisations have adopted cloud cost management tools, but only a few have evaluated its effectiveness. While FinOps teams are becoming more common, most remain narrowly focused on day-to-day operations rather than driving strategy. Only 2% of the surveyed organisations with finOps teams look at cloud, SaaS, and generative AI spending in an integrated way, and even fewer have real influence on business decisions.
Published by HT Digital Content Services with permission from TechCircle.