In my last post, I talked about the tagging trap - the gap between how cloud cost allocation is supposed to work, and how it breaks down in practice. If you’ve ever spent more time cleaning up dashboards than shipping code, you know what I mean.
Now, I want to talk about what comes next.
Not a new dashboard. Not a stricter policy. Something fundamentally different.
It’s called Magic Allocation, and it’s built on the idea that tagging and allocation don’t have to be painful - they can actually be intelligent.
Let’s Be Honest: The System Is Broken
We’ve spent the last decade pretending that if we just enforced tagging policies hard enough, we’d get clean allocation.
We wrote wiki pages. We built Terraform modules. We sent Slack reminders with red exclamation marks. And for a while, it kind of worked.
But here’s the problem: the real world doesn’t stand still. Teams change. Projects pivot. Infrastructure evolves. And tagging logic that made sense three quarters ago now feels like an inside joke nobody remembers.
The harder you push for perfect tagging, the more brittle your system becomes.
Why AI Is a Better Fit for the Problem
Here’s where AI - specifically large language models - change the game.
They don’t rely on fixed rules. They adapt.
They don’t panic when things aren’t perfectly labeled. They ask questions.
They don’t need a thousand lines of tagging policy. They learn what matters based on how you respond.
Magic Allocation uses conversational AI to do what no dashboard ever could: it talks to your engineers like a teammate.
Imagine this:
- A new cluster shows up in your cost report, untagged.
- The system notices it and sends a message: “Hey, is this part of the onboarding service or something else?”
- The engineer replies: “Yeah, staging environment for onboarding.”
- Boom. The system updates the tag, applies the logic to similar resources, and keeps learning.
No policy required. No guilt trip. Just a quick chat, and it moves on.
What Makes Magic Allocation... Well, Magic?
First, it’s not about fixing one tag. It’s about building a system of intelligence that keeps getting smarter.
Magic Allocation:
- Normalizes tags across AWS, Azure, GCP, and your vendors
- Fills in gaps where metadata is missing
- Allocates shared resources based on real usage patterns
- Explains how it made its decisions - in human language
And the best part is that it doesn’t break when your org changes! Because it was never relying on rigid rules to begin with.
But What About Control?
Look, I get the skepticism. “AI” has been slapped on everything from cost alerts to autocomplete. So let’s be clear: Magic Allocation doesn’t make decisions in a vacuum.
It puts engineers and FinOps in the loop.
If it’s not sure how to classify something, it asks.
If it does something, it tells you why.
If you disagree, you can correct it - and it learns.
This isn’t about giving up control. It’s about finally getting control over a system that’s been too brittle, too manual, and too slow for too long.
This Is More Than a Tool - It’s a Shift
The real magic isn’t in the interface. It’s in what changes when engineers and finance teams start speaking the same language.
When tagging becomes a conversation, not a compliance exercise.
When cost data reflects real context, not outdated guesses.
When your AI teammate can explain exactly why shared infrastructure was split across teams - and finance actually understands it.
That’s the future we’re building.
And if you’re reading this thinking, “This is exactly what we’ve needed for years” - good news. You can help shape what comes next.
In my next post, I’ll talk about how Magic Allocation fits into a broader strategy to align cloud cost with business value - starting with COGS.
Until then, if you're curious or skeptical (or both), let's talk.
We’re building this thing in the open. And we’d love your input.
