AI is at a pivotal juncture, poised to transform various sectors, from automated content production to comprehensive decision-making frameworks. This shift presents unprecedented opportunities for technology companies. AI is not only reshaping workflows and expediting product cycles but is also generating entirely new categories of business value.
In this context, India aims to leverage its robust technology ecosystem to capitalize on the AI wave. The country’s digital economy is projected to reach USD 1 trillion by 2030, with the government’s India AI Mission, supported by ₹10,300 crore and a national GPU infrastructure comprising 38,000 units, underscoring AI’s critical role in achieving this vision. However, a significant barrier to realizing AI’s potential for driving economic growth in India is the effective management of cloud and AI costs.
From Servers to Tokens: The New Cost Equation
The transition from on-premises servers to cloud computing facilitated simpler governance and budgeting in the early cloud era, allowing finance teams to approve consistent hourly or monthly server expenditures. However, today’s AI workloads have drastically altered this paradigm. Costs are now calculated per token, inference, and dataset storage, leading to dynamic resource consumption based on model interactions, image generation, or retraining loops. For instance, an engineer conducting multiple model prompts or a marketing team generating numerous images can inadvertently escalate costs significantly.
According to McKinsey, over 80% of enterprises face difficulties in accurately predicting GenAI costs, with some underestimating inference costs by as much as 30%. The elastic scaling once seen as an advantage of cloud solutions has turned unpredictable without real-time FinOps (Financial Operations).
Smart FinOps: Redefining AI Expenditures
Traditional FinOps practices focused mainly on visibility and tagging. In contrast, FinOps in the GenAI era must evolve into a strategic discipline, ensuring every token spent corresponds to measurable value. Companies adopting AI-enabled FinOps frameworks can reduce cloud wastage by up to 40%. A notable case is Klarna, which integrated GenAI into its campaign design, yielding annual savings of USD 10 million, including USD 6 million from automated image generation. This shift illustrates how FinOps can align costs with value rather than simply act as a cost-cutting measure.
Key Strategies for Enterprises
A practical approach for enterprises includes:
- Token Capping: Establishing expenditure limits by department (e.g., marketing, engineering) to mitigate uncontrolled prompting.
- ROI Measurement: For developers utilizing code-assist tools, tracking metrics such as defect reduction and quicker feature releases can be instrumental.
- KPI Alignment: Evaluating every token expenditure as an investment, with tangible business value reflected in enhanced developer velocity.
Fixed vs. Variable AI Costs: A Governance Challenge
Predictable pricing models exist for tools like GitHub Copilot or Amazon CodeWhisperer. However, custom large language models (LLMs), open-source fine-tuning, and inference-heavy workloads are subject to variable costs that can surge with usage, resulting in unforeseen expenses. Mechanisms like daily dashboards, anomaly alerts, and predictive cost models can help forecast cost spikes. Advanced FinOps strategies ensure that AI scaling is deliberate rather than accidental.
Surveys from numerous Indian cloud consulting organizations indicate a majority of Indian enterprises struggle to maintain FinOps maturity across multi-cloud configurations, especially as GenAI efforts become increasingly distributed among various business units.
India’s Mission: Shifting from AI at Scale to AI at Value
Lenovo anticipates that by 2025, 43% of AI spending will be directed toward GenAI applications, with enterprise AI investments in India reaching USD 9.2 billion by 2028. Nevertheless, many organizations lack structured tagging, governance, or value-based assessment, which poses a risk that AI costs may escalate faster than the value it delivers.
For India to convert its AI ambitions into tangible economic benefits, FinOps practices must transition from an ancillary function to a priority at the board level.
The Path Forward
Generative AI is set to shape the next decade of digital innovation. However, achieving responsible scalability necessitates embedding financial intelligence into AI strategies from the outset. Organizations that effectively balance innovation with financial discipline are likely to lead the upcoming era of global competitiveness.
According to Bhavesh Goswami, Founder & CEO of CloudThat, FinOps should be viewed not merely as a cost-control mechanism, but as a key driver that can help ensure India’s AI transformation results in a significant value-based revolution. The successful enterprises will be those that cultivate not only advanced models but also sustainable and scalable AI ecosystems.
Published on Nov 19, 2025
