When Uber’s finance team noticed something was wrong, the damage was already done. Employees across the company had exhausted an AI spending budget meant to last a full year in just four months. The company responded by capping individual AI usage—a blunt instrument that signals a deeper problem many Indian enterprises will soon face.
The culprit wasn’t a single runaway project. It was death by a thousand API calls. Engineers experimenting with large language models, product teams prototyping chatbots, analysts running queries through AI assistants—each transaction small, but the cumulative bill enormous.
Why AI Costs Are Different From Traditional IT Spend
Most enterprise software costs are predictable. You pay per seat, per server, or per license. AI spending works differently. You pay per token—essentially per word processed—which means costs scale with usage in ways that are hard to forecast.
A developer testing a feature might run thousands of queries in an afternoon without realizing each one costs money. Multiply that across hundreds of employees with API access, and budgets evaporate. OpenAI, Anthropic, and Google all charge based on consumption, which makes cost visibility critical.
Indian enterprises moving fast on AI adoption face the same trap. The pressure to experiment is high. The guardrails are often nonexistent.
The Control Problem: Too Tight Kills Innovation, Too Loose Kills Budgets
Uber’s response—hard caps on spending—solves the immediate problem but creates a new one. Developers who hit their limit mid-project either stop working or find workarounds, neither of which helps the business.
The real challenge is designing controls that throttle waste without throttling innovation. This requires treating AI spend like cloud infrastructure spend, which enterprises learned to manage over the past decade through tagging, quotas, and chargebacks.
Tagging means every API call gets labeled with a project code, team name, or cost center. Without it, you cannot answer basic questions: Which team is spending the most? Which projects are worth the cost? Which experiments should be shut down?
Building an AI Cost Governance Framework
CIOs who want to avoid Uber’s situation need four components in place before AI usage scales further.
Centralized procurement: Instead of letting every team sign up for OpenAI or Claude accounts independently, route all AI vendor relationships through a single contract. This gives you volume discounts and a single dashboard for spending.
Tiered quotas: Not every employee needs unlimited access to GPT-4. Set default limits based on role—generous for data science teams, modest for general staff—with a clear process to request more when needed.
Internal marketplaces: Some companies are experimenting with AI credit systems. Teams receive a monthly allocation and can trade or request additional credits. This creates natural friction that makes people think before they spend.
Real-time alerts: By the time you get a monthly invoice, the money is gone. Implement monitoring that flags unusual spikes within hours, not weeks. Tools from companies like Portkey, Helicone, and LangSmith offer this kind of observability for AI API usage.
The Indian Context: Move Fast, But Not Blind
Indian enterprises are under pressure to ship AI features quickly—customers expect it, boards demand it, competitors are doing it. That urgency makes cost governance feel like bureaucracy.
But the companies that scale AI successfully will be those that treat cost visibility as a feature, not a constraint. Infosys, TCS, and Wipro are already building AI cost management into their enterprise offerings, recognizing that clients will need help here.
Startups are equally vulnerable. A seed-stage company burning through runway on API calls it cannot track is making the same mistake Uber made, just with less margin for error.
What This Means for You
If you cannot answer the question “How much are we spending on AI, by team, by project, this month?” then you have the same problem Uber had. The fix is not to ban experimentation—it is to make experimentation visible.
Start with tagging. Mandate it for every API integration before the next quarter. Then add quotas and alerts. The goal is not to slow down AI adoption. It is to make sure you can afford to keep going.