AI is reshaping the cost landscape. As a positive person, I’m going to call this change exciting!
FinOps teams are integrating AI into cloud platforms and incurring the spend that comes with it. As a FinOps strategist who has helped several companies optimize cloud spend across industries, it became evident that clarity around AI spend unlocks swift, smart decisions. That’s AI … optimized.
When you understand where your dollars are going — by project, function, or feature — you can align teams, optimize resources, and guide innovation with confidence.
That’s exactly what this guide is here for.
I’ll walk you through how to tag and track AI costs with a persona-first lens — including inputs and a twist on leveraging outputs. I’ll outline your next steps based on the FinOps Maturity Model (crawl, walk, run).
Excited? Me too! Let’s dive in.
Tagging AI Spend With Purpose
Standard AI invoices often include:
- GPU type
- Model name
- Total usage
- Input/output volume
These give you a snapshot — but not a story … yet. (No worries, we’ll reveal the story together, with tagging.)
To connect AI costs to business value, you need a richer view. We can leverage tags to gain answers to business outcome-based questions that leadership is asking, such as:
“What project is this powering? What stage is it in? What results is it delivering?”
The goal is visibility that leads to action. That’s where a strong tagging framework comes in.
AI Optimized: A Practical Tagging Strategy
I’m going to outline a FinOps-friendly tagging strategy that creates clarity around AI workload spend.
And here’s a handy diagram for those of us who are visual learners:
1. Tag by Project
I’ve got two words for making progress: start simple.
Assign a tag for each project your AI model supports. This can help you understand cost allocation by initiative and sets the stage for deeper insights in the future.
Example: projectABC-chatbot-v1-inference
2. Tag by Workload Type (Training, Inference, Fine-tuning)
Each model behaves differently depending on where it is in its lifecycle:
- Training: Higher cost, foundational phase
- Inference: Actively responding to inputs
- Fine-tuning: Adjusting and refining for better accuracy
Tagging by stage provides context. You’ll see which models are learning, which are operating, and how their cost aligns with their purpose.
3. Tag by Feature or Function
What is your AI model doing for your users?
Tag it based on the functionality it delivers — like:
- Chatbot
- Recommendations feature
Pro tip: Tagging by feature/function helps tie technical spend directly to user value.
Once these tags are deployed, you can plan future feature additions to various projects.
For example, we’re in a planning meeting for a new project. The team is in discussions about adding a chatbot feature to a new project.
With this recommended tagging in place, teams can reference the cost of a current project with a chatbot feature and assess the cost of resources needed to launch this feature into a new project. This kind of transparency leads to accountability- which can positively impact profitability.
4. Tag Successful Outputs
This one’s a favorite of mine.
Tag each response based on whether it was successful — like correct answers, user-approved outputs, or high-confidence predictions.
Pro tip: You could track API requests and responses for this one.
You’ll see which models are delivering value, which need adjustment, and how to focus your investment where it creates the most impact.
Customize Tags To Fit Your Goals
Whether you’re tagging by customer, feature, business unit, or time window, more precision leads to deeper visibility. Now, you can choose the tag combinations that tell the best story for your team. See? I told you we would reveal that story!
Now each team is different. Our starting line may be different as well, based on our capabilities, headspace, and funds. Want a step based on where you are today- I’ve got you! Here is a layout of steps you can take to gain AI visibility into spend based on where you stand in the FinOps Maturity stages of crawl, walk, and run.
Implement With Confidence: Crawl, Walk, Run
This framework makes it easy to start tagging at every FinOps Maturity stage.
1. Crawl
Begin by identifying key tags that align with your team’s priorities. Document them clearly and apply them to select resources.
Pro tip: Use lowercase and dashes — clean, consistent, and automation-ready.
2. Walk
Automate tag application. Empower your engineers with scripts or workflows that apply tags by default. As tag coverage increases, your CloudZero dashboard becomes a rich source of insight.
3. Run
At this stage, tagging is a habit — and your insights are powerful.
- Visualize cost per function
- Monitor quality and outcomes
- Align AI investment with business value
- Initiate smart optimization conversations
Tagging then becomes a cultural norm. You’re managing AI costs and driving data-driven AI value creation.
Unlocking Unit Cost Metrics For AI
Once you’re tagging consistently, you can go beyond total spend and start calculating meaningful unit metrics:
- Cost per feature
- Cost per output
- Cost per successful result
This empowers your team to answer questions like:
“Which AI feature delivers the best value per dollar?”
“How do our costs shift when we increase model quality?”
What do we call that? AI … optimized!
Final Thought: Every Step Toward Visibility Is Progress
Clarity around AI spend can start with taking one step.
Tag one resource, align one project, share one insight.
Every small improvement adds up to a powerful FinOps AI strategy.
Let’s Make Your AI Costs Count
Tune into the FinOps in Full Bloom podcast for more stories:
And send your AI tagging wins and tips to [email protected] with your name, position, and company, and we’ll compile the best into a new article!
The Cloud Cost Playbook
The step-by-step guide to cost maturity
