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 overnight. A larger model may improve response quality but also increase costs significantly. More users mean more prompts, more tokens, and more compute.
Even small changes can have a noticeable impact on the cloud bill.
The complexity is, however, multifold. AI spend is spread across multiple services, making it difficult to understand where the money is actually going. It’s not uncommon for engineering teams to ask why cloud costs have increased, while finance teams struggle to identify which AI initiatives are responsible for the additional spend.
FinOps for AI Looks Beyond Infrastructure
When people think about AI costs, GPUs usually dominate the conversation. They’re certainly one of the biggest contributors, but they’re far from the only ones.
Every AI application has its own cost footprint. Behind a single chatbot or AI assistant are inference requests, token usage, managed AI platforms, vector databases, storage, networking, containerized workloads, and supporting cloud services. Looking at GPU utilization alone doesn’t explain the full picture.
That’s why FinOps for AI needs broader visibility.
Take model selection, for example. The largest model isn’t always the right choice. For many workloads, a smaller or more efficient model delivers nearly the same outcome at a much lower cost. Comparing models based on both performance and cost helps organisations make smarter decisions instead of defaulting to the most capable – and often the most expensive – option.
Inference is another area that deserves close attention. One request may cost only a fraction of a cent, but millions of requests every month tell a very different story. Understanding which applications, teams, or business units are driving inference costs makes optimization far more targeted.
The same goes for token consumption. Without visibility into how tokens are being used, organisations have little context behind rising AI costs. Tracking token usage across applications and users makes it easier to allocate costs, identify inefficient workloads, and understand where optimization efforts will have the biggest impact.
And then there’s the infrastructure itself. GPU utilization, Kubernetes clusters, managed AI services, cloud resources, and storage, all contribute to the overall cost of delivering AI. Bringing these insights together creates a much clearer picture about the optimization strategy, than monitoring each service in isolation.
FinOps Is Becoming a Business Function
Finance teams want predictable budgets. Business leaders want to understand the return on AI investments. Engineering teams need the flexibility to experiment without worrying about runaway costs.
FinOps brings those conversations together.
Instead of waiting for monthly invoices, organisations can monitor AI spending in real time, understand where costs are coming from, allocate spend to products or teams, and identify optimization opportunities before they become expensive problems.
More importantly, it creates a common language between engineering, finance, and business teams.
Everyone has visibility into the same data, making it easier to balance innovation with financial accountability.
AI adoption is only going to accelerate. New models will emerge, workloads will grow, and organisations will continue investing in AI across every part of the business.
The companies that succeed will be the ones who find a balance between innovation and cost efficiency – the ones that understand the financial impact of every AI decision they make.
That’s where FinOps is headed.
Takeaway
AI has introduced a new cost landscape. One that’s far more dynamic than traditional cloud infrastructure. Managing that complexity requires more than infrastructure optimization.
It calls for visibility into models, inference, token usage, GPUs, and the entire AI stack. We don’t even call AI a differentiator any more. It is a core business necessity, and FinOps is evolving alongside it.
Rather than just reducing cloud bills, FinOps needs to now also focus on helping organisations build, scale, and govern AI with confidence – without losing control of costs.