As generative AI adoption surges across industries, a quiet but expensive challenge is forming: understanding and attributing the true cost of GenAI workloads.
No, it's not just about how many tokens you burned through OpenAI or how long a model hogged a GPU.
If you're serious about FinOps in an AI-first org, cost attribution isn't optional—it’s survival. And the way each cloud provider handles this is wildly different. So if you're navigating AI cost management across AWS, Azure, and GCP, here’s the blog you didn’t know you needed.
Amazon Bedrock came to play. AWS introduced something called Application Inference Profiles—a way to attribute GenAI costs per application, with proper billing-level tagging built-in.
Here’s what it means: instead of tracking spend per endpoint or per API call and then backtracking through your logs to figure out which team used what—you create a profile per app, give it a tag (e.g. team=marketing-ai, project=chatbot), and Bedrock does the rest.
Every single call through that profile gets attributed to that tag in the CUR. It shows up just like your EC2 or S3 tags, and you can group it in Cost Explorer, Athena, or (yes) Finout.
Think of it like having native business mappings for AI inference, not after the fact. It’s as if EC2 let you pass a team name with every CPU cycle.
If you’re using Bedrock and not using inference profiles—you’re just leaving traceability on the table.
Azure does not have an equivalent to Application Inference Profiles. You can’t tag OpenAI API calls directly. So what does Azure want you to do?
In theory? Fine. In practice? Good luck when four teams are calling the same endpoint.
To Azure’s credit, they allow tag inheritance from subscriptions and resource groups. So you can get some attribution at scale if your architecture is clean and your governance is on point.
But if AWS gives you cost attribution with a bow on top, Azure asks you to duct-tape it yourself.
Google is the middle child here. No Application Inference Profiles, no call-level tagging—but they have labels and BigQuery export for billing data.
Want to track spend for a specific model deployment or endpoint? Label the resource with app=fraud-detection, and query your billing export to sum up its cost.
And if you really want attribution per workload? You create a separate GCP Project per team or application. That’s your unit of isolation and analysis.
But—and this is key—you can’t track usage by request. If multiple apps share the same deployment, the billing record can’t tell you which app was responsible.
So in GCP, it’s either rigid project segmentation, or manual instrumentation inside your app to do postmortem allocation.
Let’s be clear: even with AWS's Inference Profiles and all the tagging magic, there’s still a lot missing from the CUR:
The CUR shows costs per service per tag. It doesn’t tell you why they were incurred or how efficiently.
This is where Finout comes in.
We don’t replace tagging—we supercharge it.
Finout plugs into your CURs, your Azure exports, your GCP billing pipelines—and combines them with:
We connect the billing metadata with the operational behavior—so you can say, “Yes, this spike in usage came from our GenAI Marketing Bot—and here's its cost vs. ROI.”
If you’re running GenAI in production and don’t know what it’s costing per team, per use case, or per business line—this isn’t just a cost issue.
It’s a blind spot.
Let’s fix it.