Just when you think you’ve figured out your cloud costs, AI comes barreling along. IT and finance teams are now facing a new, fast-growing layer of cost complexity:
- AI budgets are growing faster than IT: IT budgets are increasing this year by about 2%, while AI spending is jumping nearly 6%.
- GenAI is surging: Global GenAI spend is estimated to top $644B in 2025 – a 76% increase compared to 2024.
- AI is driving cloud waste: 82% of enterprises agree that AI is fueling increased complexity and cloud spending.
Roughly 63% of companies now actively manage AI spending – up from 31% last year – and the FinOps Foundation has taken notice. Earlier this year, AI cost tracking was added to the FinOps Framework, signaling just how critical this category has become for cloud governance.
There’s a lot going on right now, and it’s all shaping the way companies approach AI and cloud costs. If your organization is using AI, planning to, or even just experimenting, you need a strategy to help keep your cloud spend in check. Here, we unpack the greatest cost challenges behind AI’s rapid growth and how to ensure innovation efforts stay efficient, optimized, and financially accountable.
The Catch-22 of AI: Innovation vs. Cost Complexity
Beneath every claim that AI transforms business lies the underbelly of innovation: rising costs, runaway usage, and limited visibility. Let’s break down the inner workings of how AI spend behaves – and how costs can get out of control.
GPU-intensive infrastructure
AI runs on powerful GPU machines that can cost up to $50 an hour. Spending can spiral if you’re running AI around the clock (think training large language models from scratch or fine-tuning them to fit your specific business needs).
Heavy use of AI tools that charge per request
AI tools like ChatGPT offer free or low-cost plans for individuals, but API-based access (using these models through code, what most enterprises do) means every request comes at a cost based on the amount of text sent and received.
For example, a customer support app that pings GPT-4 via API to draft a reply the moment an email hits an agent’s queue. A customer sends one paragraph and AI replies with a few more – this single interaction only costs a few cents, but hundreds of thousands of interactions a day adds up fast.
And it’s not just support teams. Finance might be using AI to reconcile payments, HR to screen resumes, and sales to summarize CRM notes. These tools are often siloed, unmanaged, and don’t show up clearly in cloud dashboards or expense reports. You’ll see usage rising but not the who, where, or why – and definitely not the “what now?”
Shadow AI services
This is the stuff no one tells finance about, like a team swiping a card for an AI writing tool or duplicate subscriptions to the same AI service. It doesn’t raise red flags – that is, until you start looking at line items.
How Do You Keep AI Costs from Spiraling?
AI isn’t just another cloud cost. It’s an entirely new category of cloud spend that demands greater visibility and control.
- Detect and tag AI and ML workloads: Teams need to ensure that AI and ML workloads are clearly identified and tagged from the start, no matter where they’re running.
- Differentiate between training, inference, and experimentation: Teams must be able to distinguish accordingly to accurately assess AI usage patterns and prioritize spend.
- Track usage of API-based models: Teams should monitor how frequently API-based models are being called, who’s using them, and for which use cases so costs can be allocated properly.
- Break down GPU costs: GPU costs should be tracked separately from regular cloud spend so teams can see exactly what’s driving usage and pay only for what they need.
- Attribute AI spend: AI usage and costs should be assigned to specific teams, departments, or initiatives to enable accountability and align spending with strategy.
- Forecast usage and flag anomalies: Teams should monitor AI usage trends, establish benchmarks, and build forecasting practices that flag unexpected cost surges in real-time.
- Bring shadow AI to light: Teams need visibility into what’s being used, who’s using it, and whether it aligns with broader governance and procurement policies.
The Answer: A FinOps Certified Platform (But Not Just Any Will Do)
The FinOps Foundation adding AI to its framework is a big deal. It validates that AI spend has become a major concern and requires the same structure, accountability, and visibility as any other cloud expense. FinOps offers that structure, but many organizations are still early in their journey or haven’t fully tapped into its capabilities. And with AI only recently added to the mix, most haven’t even begun to consider how that fits into their cloud management strategies.
There are resources out there, but moving beyond reactive fixes to a mature, sustainable FinOps practice demands steep investment and learning. As a result, more organizations are turning to FinOps Certified platforms and service providers to help navigate this new terrain. These solutions are purpose-built to support the FinOps Framework’s Domains and Capabilities – a seal of approval from the FinOps Foundation that the platform can put FinOps principles into measurable practice.
More platforms are pursuing certification to meet these evolving standards, including newer scopes like AI, SaaS, and licensing. But while certification is a strong indicator of readiness, capabilities still vary. Not all certified platforms offer the same level of visibility into AI spend, which is quickly becoming one of the most volatile and fast-growing cloud cost drivers. Evaluating how deeply a platform supports your organization’s full FinOps scope, including AI, is key to choosing the right partner.
That’s where Tangoe One Cloud Stands Out
Tangoe One Cloud is FinOps certified and purpose-built for AI cost optimization and financial oversight.
- Our AI Cost Visibility Dashboard easily visualizes and isolates AI and ML spend across cloud providers, projects, and cost centers.
- Trend analysis views help teams spot AI usage spikes early and make more informed decisions so spend doesn’t spiral out of control.
- Tangoe continuously monitors AI workloads to detect underutilized, oversized, and idle resources. You’ll get actionable insights and cost savings recommendations, with the option to automatically act on them with the push of a button.
Organizations need to start treating AI like any other high-impact cloud cost by managing it intentionally, with deep visibility and targeted controls. Tangoe One Cloud empowers IT and finance leaders to tackle AI head on, driving innovation while saving up to 40% on their cloud costs.
See how much you could be saving with a demo of Tangoe One Cloud.