AI has stepped out of the lab and into production—and with it, our cost models are breaking. As GenAI and large language models (LLMs) become foundational to modern products, traditional FinOps approaches—built for compute, storage, and bandwidth—are quickly becoming outdated.
Today, a spike of $10,000 in GPU inference costs isn’t uncommon, and more importantly, it can happen overnight—without corresponding business value. We’re not just scaling performance anymore—we’re scaling uncertainty, if we don’t get ahead of it.
In classic FinOps, we’re focused on CPU hours, reserved instances, and predictable compute workloads. But AI can introduce a new layer of unpredictability:
• Training Costs: One-time, high-burst GPU workloads
• Inference Costs: Ongoing, real-time compute demand at scale
• Orchestration Overheads: Pipelines involving LangChain, vector databases, embedding stores, and caching layers
What used to be side-notes in the bill are now center stage. A single LLM endpoint in production can inflate cloud spend by 30%—with very little tagging or cost control in place.
GPU vs CPU Economics
It’s tempting to deploy GPUs everywhere. But that’s not strategy—it’s sprawl. FinOps leaders now need to ask better questions:
• Cost per token or inference: How does performance compare across CPU, A100, and H100 workloads?
• Utilization insight: Are our expensive GPUs consistently working above 70%, or are they idle-burning at 15%?
• Demand forecasting: Can we predict GPU burst demand based on historical model access or business seasonality?
One example I saw: A generative search feature deployed via OpenAI’s API was costing $20,000/month. By redesigning prompts and switching to a quantized on-prem model, costs dropped to $4,500—with no real loss in user experience.
If your dashboard still highlights EC2, S3, and RDS as top concerns—you’re missing the AI picture.
Here’s what I recommend tracking:
• Cost per model inference or per token
• Usage vs. value delivered per model version
• GPU queue wait times vs. allocation
• Cost spikes between model versions (v1.0 vs. v2.0)
• Drift cost: Is your accuracy decreasing while spend increases?
Governance in AI-Heavy Environments
Tagging VMs isn’t enough. FinOps in AI needs to plug into the model lifecycle itself.
• Identity tagging: Tag by model name, version, and deployment endpoint—not just resource group
• Role-based budgets: ML engineers and data scientists should see cost as part of their deploy pipeline
• Lifecycle anchoring: Track costs across training, fine-tuning, deployment, and eventual sunset
I’ve seen cases where a model version change doubled GPU costs—with no business gain. This is avoidable—with the right FinOps hooks.
Welcome to AI Value Engineering
Cost per unit is a dated metric. In AI-first organizations, the real question is: what outcome are we buying?
Ask yourself:
• Are we measuring ROI per model?
• Are GPU dollars translating into better conversions or customer retention?
• Are we funding exploration or exploitation—and is the mix intentional?
FinOps must evolve into AI ValueOps: where finance, engineering, and product come together to assess value per dollar spent—not just dollars spent.
From FinOps to AI-Ops?
The lines are blurring. FinOps is no longer just about optimizing spend—it’s about making AI financially sustainable.
We might soon stop calling it FinOps altogether. Maybe it becomes AI-Ops, or ValueOps—but it will belong to organizations who treat every GPU hour like an investment, not a sunk cost.
“Don’t just track AI costs. Justify them. Or eliminate them. That’s what modern FinOps leadership looks like.”
Thanks for reading,
Richa.