From advanced analytics to intelligent automation, AI opens the door to game-changing enterprise capabilities. But if your CIO or CFO pulled you aside and asked for a clear breakdown of AI spend – who’s using what, what it’s costing, and whether it’s worth it – would you have the insights they need?
That depends on a few things:
- Can you benchmark AI spend across teams, or are you in the dark when it comes to cost ownership?
- Are you tying AI costs to ROI, or watching your budget grow without context?
- Can you spot inefficiencies, or is waste hiding in plain sight?
- Are you actively forecasting API usage and GPU consumption?
- Can you build proactive cost-based alerts, or react after the bill arrives?
- Can you track and allocate AI costs in dynamic environments like Kubernetes, or are your resources too fluid to follow?
- Do you know how AI costs stack up against planned budgets, and can you track AI-related costs?
Relying on outdated cost management tools and strategies for something as dynamic and complex as AI is like trying to navigate a Formula 1 track in a golf cart. That explains why 72% of IT and financial leaders find their AI-driven cloud spending unmanageable.
If you want a play-by-play of how AI spend behaves and the most common culprits of cost overruns, we’ve got you covered in this blog. Here, we continue the conversation by looking at “AI in the wild” scenarios, the most common ways organizations track AI spend, and how to get out of reactive mode.
Managing AI Spend the Hard Way: 3 Cost Scenarios
Do any of these scenarios sound familiar?
HR starts dabbling with an AI resume screening feature of their application tracking system (ATS).
In expense reports or vendor dashboards, it’s buried as a vague line item or blended into the monthly subscription total. Finance can only see the cost, not who’s using it or if it’s delivering ROI. To answer even the most basic questions, someone has to log into the ATS, export usage logs, match each account to a department, and estimate cost per hire. It’s hours of manual work that can easily slip through the cracks.
Support launches a new AI chatbot, running it on Kubernetes with GPUs.
These costs almost always appear under confusing names that sound like something from Star Wars. They could be lumped together under one big “cluster” charge (not just for the chatbot but possibly other workloads and tools), and split across multiple services that live in different sections of the cloud bill. Without a purpose-built view, someone has to pull raw billing exports, cross-reference them with Kubernetes logs and labels (if they even exist), and then manually calculate utilization.
Marketing signs up for an AI writing tool to help with content creation.
A few weeks later, someone in sales does the same. In the budget tracker, these show up as harmless software charges on different cards. Unless someone knows to connect the dots, you won’t see that you’re paying twice for the same tool – or that you already have an enterprise license somewhere else. To find out, finance has to manually normalize vendor names, comb through expense reports, and check with department heads.
We see in these scenarios a few common challenges:
- Limited visibility. You can see the charges, but not the story behind them. AI costs often hide under generic labels, get scattered across services, or blend into other workloads.
- Always in reactive mode. Even if you know the culprit is AI, you’re scrambling to explain or fix the issue without understanding the root cause. You’re unable to get ahead of it.
- Difficult-to-decipher details. Cloud bills are already complicated. AI makes them even harder with cryptic line items, hidden services, and bundled costs that obscure what’s driving spend.
- Skill and resource gaps. You might see a sudden uptick tied to something like a Tera server, but tying it back to a specific workload takes technical expertise and time most teams just don’t have. By the time you’ve pieced it together, the budget has already taken a hit.
So, What Are Your Options?
Option 1: Hire a Dedicated Expert
Hiring full-time AI specialists can work if you have the budget and the time. In the U.S., the average AI professional makes roughly $140k per year. You could be shelling out more, considering that there’s a shortage of qualified candidates and such high demand. There’s also the challenge of having enough in-house AI expertise to properly vet candidates. If hiring managers aren’t AI-savvy themselves, it’s hard to be confident you’re bringing on the right person. It’s a lot to navigate – meanwhile, cloud costs continue to rise.
Option 2: Bring in a Consultant
Consultants are great for a short-term fix. They’ll untangle the mess, optimize here and there, and send a report. But what happens next quarter?
Putting one on retainer will likely cost more than hiring a dedicated specialist, and you still won’t have someone embedded in your company’s culture. True AI cost management isn’t just a process. It needs to be built into day-to-day operations. That means cross-functional collaboration, shared financial accountability, and continuous improvement.
Option 3: Use a Purpose-built Platform
By this, we mean a solution that’s purpose-built for AI cost optimization as a FinOps-certified platform. FinOps is widely considered the gold standard for cloud cost governance and financial oversight, and the FinOps Framework was updated earlier this year to recognize AI’s financial impact.
A FinOps-certified platform means it’s purpose-built, tested, and proven to meet the Framework’s rigorous Domains and Capabilities, including AI. It’s designed to:
- Translate AI services into understandable, labeled categories (e.g., GenAI, NLP, ML).
- Tie spend to cost centers, projects, or business units.
- Help optimize in real-time, not after the fact.
- Build shared visibility across IT, finance, and leadership.
But even here, not all certified platforms are created equal. Every platform checks the “AI” box, but not all go beyond the basics in terms of AI-specific insight, GPU-level granularity, or ease of use for non-technical stakeholders.
To understand the FinOps Framework and some of its more complex components, our latest guide simplifies it all term-by-term.
It’s About More than a Fix. You Need a Strategy.
AI cost management must be treated as a sustainable, long-term business practice. That’s only possible when you have a clear, day-to-day view of the full picture across your organization. A FinOps-certified platform is designed specifically to make that possible.
The organizations that win won’t be the ones with the biggest budgets. They’ll be the ones with the clearest visibility, smartest insights, and strongest cross-functional alignment. If your current tools can’t give you that, choose wisely because the wrong move will cost you.
See how Tangoe One Cloud stands apart from other FinOps-certified platforms by scheduling a demo.