Enterprises are signing large AI contracts without knowing their actual spend. Traditional software charges a fixed price per user. AI tools like Claude, Cursor, and Gemini charge by the token. This usage compounds silently every day. Finance teams often receive month-end invoices they cannot explain.
CloudEagle.ai provides real-time visibility into this consumption-based spending. The platform gives finance, procurement, and IT teams the data they need to track usage and negotiate renewals. We sat down with Nidhi Jain, CEO of CloudEagle.ai, to discuss how companies can take control of their AI budgets.
Q: How does the billing model for AI tools differ from traditional software, and why does this cause problems for finance teams?
Nidhi Jain: Traditional software is straightforward. You pay a fixed price per user, per year. Finance teams know exactly what they’re signing up for when they put pen to paper.
AI tools work completely differently. You’re paying by the token, by the API call, by the credit consumed. Every query your team runs, every prompt your developers send, every workflow your systems trigger – all of it compounds into a bill you see at the end of the month.
What makes this particularly hard for finance teams is that the spend is invisible while it’s happening. And when the invoice finally arrives, it comes as a single number. No breakdown by department, no visibility into which team consumed the most, no way to trace usage back to a cost center. Budget forecasting becomes guesswork because there’s no historical split to build from.
We saw this pattern with a SaaS ops team we spoke with recently. They were running five different AI tools simultaneously, like Claude, ChatGPT, Cursor, Gemini, and GitHub Copilot. Each bills differently, each has a different consumption model. Their ask was very specific: “I want to be able to say Brian spent two thousand dollars of spend this week and one thousand dollars last week.” Per-user, per-tool, per-week. That granularity doesn’t exist unless you’ve centralized the data. And most companies haven’t.
That’s the fundamental problem: consumption-based billing requires real-time governance, and most enterprises aren’t built for it. Organizations reporting AI as an active FinOps concern jumped from 31% in 2024 to 63% in 2025. The awareness is there. The infrastructure to act on it largely isn’t. Finance teams were designed for fixed-cost software. A model in which every interaction costs something, and where 43% of organizations are already reporting significant AI cost overruns impacting profitability, requires a completely different approach to tracking, forecasting, and governance.
Q: Without proper governance, AI costs grow quickly. What are the long-term risks for a business that scales its AI usage without tracking it?
Nidhi Jain: The cost risk is the most visible one, but it’s actually not the most dangerous.
When you scale AI usage without tracking it, you lose accountability across three dimensions: financial, security, and operational. Who’s spending what, who has access to which tools, and whether the investment is actually driving value. Most organizations can’t answer any of those questions today.
On the financial side, contracts auto-renew at higher tiers because no one has the usage data to push back. Three different teams independently evaluate and buy the same capability. We had a customer who discovered two of their teams were independently subscribing to Claude, paying for the same thing twice, because there was no system to catch it. Overprovisioned licenses sit unused with no automated reclamation in place. The waste compounds quietly, and by the time someone notices, the budget has already absorbed the hit.
But beyond spending, the governance gap creates real security exposure. Which AI tools are employees actually using? Are they using sanctioned platforms, or are they feeding sensitive company data into tools IT never approved? I was on a call recently with the head of information security at a financial institution. They’d rolled out Copilot to a pilot group and almost immediately saw people creating personal AI agents inside its workflow agents that could access and move information. His exact words were: “I want to claw that back. I just don’t trust it that much. I want human eyes on it.” That’s where most security-conscious organizations are right now.
The third risk is ROI. We have customers who rolled out Copilot to 20% of their workforce and genuinely did not know whether to expand it because they had no visibility into adoption or actual feature usage. Without usage tracking, you cannot answer the basic question boards are now asking: Is this investment actually delivering value?
Governance is not a compliance checkbox. It is the mechanism that allows AI adoption to scale without creating compounding financial, security, and operational debt.
Q: IT, procurement, and finance teams require different information from an invoice. How does CloudEagle.ai format this data so each department gets what it needs?
Nidhi Jain: This is something we spent a lot of time thinking about, because the problem isn’t just that the data doesn’t exist. It’s that even when companies have some data, it’s formatted for one audience and unusable by everyone else.
IT needs operational data. Which teams are consuming the most tokens? Which applications are the heaviest AI users? Is actual usage aligned with what was provisioned? They need to act on that data, not just read it. And CIOs need it to answer a harder question the board is now asking: is this AI investment actually delivering value? That conversation is no longer optional, and you cannot have it without usage data that maps consumption back to business outcomes.
Procurement needs contract intelligence. Are we tracking against the committed spend in our contract? When is the renewal? What does our usage trajectory look like relative to the tier we’re on? They need that data 90 days before renewal, not the day before.
Finance needs budget accountability, and this is where consumption-based billing creates a genuinely new problem. With traditional software, you know your annual spend the day you sign the contract. With AI tools, you don’t. Usage fluctuates. Teams scale up. Integrations multiply. Forecasting becomes unreliable because the cost model doesn’t behave like anything finance teams have managed before. A 2025 survey found that a majority of organizations misestimate AI costs by more than 10%, with nearly a quarter underestimating by 50% or more. Budget overruns don’t get caught until the invoice arrives, and by then it’s too late to course-correct.
CloudEagle.ai ingests the raw consumption data and maps it across all three views in a single platform. The same underlying data surfaces differently depending on whether you’re an IT admin, a procurement lead, or a finance director. The goal is simple: no one should have to translate data that was built for someone else.
Q: The platform maps token consumption against contract terms. How does this real-time tracking prevent unexpected billing issues at the end of the month?
Nidhi Jain: Month-end surprises happen because there’s no feedback loop during the month. You’re flying blind until the invoice lands.
What real-time tracking does is create a continuous feedback loop. CloudEagle.ai maps your actual token consumption against the terms of your AI contract: what tier you’re on, what your committed spend is, what overage triggers look like, and surfaces that as a live view. If you’re burning through your committed volume faster than expected, you see that in week two, not week five.
The other piece is anomaly detection. If a team’s consumption pattern suddenly spikes, that surfaces as an alert. It could be legitimate – a new project launched, a new workflow was automated. Or it could be an integration that’s running redundant API calls nobody intended. Either way, you find out in real time instead of discovering it on the invoice.
We also tie this to renewal planning. If your usage trajectory shows you’re going to exceed your current contract tier before renewal, procurement needs to know that now so they can either renegotiate or adjust provisioning before you hit overage pricing. That’s the kind of decision that saves real money, but only if you have the data early enough to act on it.
You can’t manage what you can’t see. Real-time visibility converts an unpredictable cost into a manageable one.
Q: Platform standardisation is a major priority for many CIOs. How does granular usage data change the way a company evaluates its software tools?
Nidhi Jain: Every CIO I talk to is trying to consolidate. Reduce the number of platforms they manage, cut integration debt, and get to a smaller and higher-performing stack. The challenge has always been that these decisions get made without good data.
Historically, the evaluation conversation looked like this: someone advocates for a tool, someone else advocates against it, and the decision gets made on anecdote and loudness. You’d renew a tool not because it was driving value, but because one vocal champion pushed for it.
I was on a call recently with the IT head of a financial institution. He was managing Copilot license decisions by manually exporting data from Microsoft Purview every two weeks, stitching it together in Excel with VLOOKUPs and pivot tables, and then figuring out who to contact about inactive licenses. His exact words: “I don’t have time for that. That’s old school.” And this wasn’t someone who was behind – this was a well-run IT team. That’s just the reality of where most organizations are.
Granular usage data changes the question entirely. Instead of “Does the team like this tool?”, you can ask: What percentage of provisioned licenses are active? What features are people actually using? Is usage concentrated in two power users or distributed across the whole team? Those are questions you can answer with data, and they lead to very different decisions.
When procurement and IT walk into a renewal with that specificity, they’re not guessing anymore. They’re negotiating with evidence. That’s a fundamentally different posture.
Q: As spending on models like Claude and Gemini increases, how will the relationship between software procurement and AI adoption change?
Nidhi Jain: The relationship has to change because the risk profile is completely different from traditional SaaS.
When you buy a seat-based SaaS tool, procurement signs the contract and largely steps back. The cost is fixed. The renewal is predictable. The financial exposure is bounded.
AI tools don’t work that way. Spend scales with consumption. Contracts have overage clauses. New models get released, and teams want to upgrade. API integrations get built that burn tokens at rates no one initially projected. We spoke with a company that had 92 different AI engines in use across their organization. Ninety-two. Most of them not centrally managed. That’s not unusual anymore. AI adoption is moving at the pace individual employees make decisions, not the pace IT procurement does.
What I think will happen, and what we’re already seeing with forward-thinking customers, is that AI procurement will start to look more like cloud procurement. FinOps as a discipline exists because cloud spend is dynamic and requires continuous management. AI spend is the same. There will be dedicated ownership, continuous monitoring, and active optimization as table stakes.
The companies that treat AI tools like traditional software, sign the contract and assume the spend is fixed, are going to be surprised. The ones that build governance infrastructure now will have a real advantage when it comes to renewal leverage, cost predictability, and the ability to scale AI adoption responsibly.
AI adoption has already outpaced the governance structures most enterprises have. The question is how fast they close that gap.
The interview shows that companies must track their AI consumption. Without clear data, technology investments quickly become unmanageable costs. Tracking tokens, credits, and API calls ensures accurate financial reporting and responsible growth.
The ability to control AI spending will determine which businesses successfully scale these tools. CloudEagle.ai offers a practical platform to manage complex billing structures. With the right data, companies can finally align their software costs with actual business value.
To learn more, visit https://www.cloudeagle.ai/
