The hidden cost of AI: Why finOps is becoming critical for AI-driven organisations
By Naman Aggarwal, CGMO at CloudKeeper
For most organisations, the conversation around AI starts with possibilities. Faster development. Smarter customer experiences. Better productivity. New products. New revenue streams.
The conversation around cost usually comes later.
That’s because AI doesn’t fit into the same cost model that organizations have spent years optimizing.
Cloud teams know how to manage virtual machines, storage, databases, and containers. AI brings an entirely different layer of complexity, where costs are influenced by foundation models, GPU infrastructure, inference requests, token consumption, and a growing mix of managed AI services.
AI projects worldwide are increasingly moving from pilots to production and these costs have now become part of everyday business.
AI Has Changed the Cost Equation
Traditional cloud applications are relatively predictable. Once workloads stabilize, teams have a good idea of what they’ll spend each month.
AI isn’t nearly as predictable.
A new feature might double inference requests...
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