The Promise of FinOps Reasoning: Why MCP Might Be More Than Just Hype
When Microsoft introduced Copilot, the world took notice—not just for the productivity bump, but for a deeper, more transformative claim: reasoning over business data. For those of us entrenched in the complex financial responsibilities of cloud operations, that promise lit up the dashboard.
At first glance, Model Context Protocol (MCP) might seem like just another acronym in the alphabet soup of emerging tech. In reality, it’s simply a protocol. And to be fair, what MCP enables could technically be done before—just with more effort, more tooling, and often, more frustration. But here's where it gets interesting: what used to require clunky integrations and brittle automation might now be as simple as asking the right question. And for those of us juggling FinOps at scale, that’s not just interesting—it’s game-changing.
As Head of Cloud Operations, part of my day-to-day involves wearing a "FinOps" hat. I work across engineering teams to help them understand the business impact of their architectures, justify spend, and ultimately make sure our cloud consumption story makes sense to the C-suite. Early on, tools like dashboards and budget alerts picked off the low-hanging fruit. But after that? You enter a murky realm of cost anomalies driven by misconfigurations, suboptimal architectures, or hidden data flows—where intuition meets investigation.
And that’s where reasoning comes in.
Microsoft’s early vision of Copilot reasoning over Excel sheets caught my attention immediately. Imagine: exporting raw data from AWS, Azure, or GCP; loading it into Excel; and then using natural language to probe your hypotheses. “Why did data transfer costs spike on Project X last month?” “What’s the trend of underutilized compute across environments?” That sort of dynamic querying would transform how we approach cloud cost management.
But in practice? It didn’t quite deliver. Automation for CSV exports were clunky. And Copilot, while impressive, struggled with the the ability to provide the value I seeked and that FinOps demands. So that vision quietly moved to the back burner—until now.
MCP has reopened that door.
I’m now exploring a lightweight FinOps Reasoning Service: an MCP server that pulls real-time data from AWS Cost Explorer, filters it by cost category, and enriches it with supporting metrics from CloudWatch. For example, I might look at traffic patterns to understand if data transfer charges are aligned with actual application usage. It’s not just about cost tracking—it’s about cost intelligence.
Yes, platforms like Apptio exist—and I’ve used them—but in my experience, the cost and complexity often outweigh the value. What I’m after is something more agile, open, and hypothesis-driven. MCP could be the connective tissue that finally allows an LLM to reason directly on cost telemetry, without needing a team of engineers to wrangle the data first.
This space is evolving fast. The open-source community is bubbling with ideas. And while MCP isn’t the final answer, it might just be the missing piece that brings cost reasoning closer to reality for cloud leaders like us.
So if you’ve ever stared at a cloud bill and thought, “This doesn’t add up, but I can’t prove why yet”—you’re not alone. And it might finally be time to rethink how we interact with the data that drives our cloud decisions.
Edit from 08/04/2025
A few days after writing this post, I came across an open-source project in this direction. aws-cost-explorer-mcp-server .