FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization

TL;DR

This paper presents a FinOps AI agent that automates cloud cost optimization by integrating heterogeneous data sources, analyzing them, and generating actionable recommendations, demonstrating comparable performance to human practitioners.

Contribution

We developed a goal-driven AI agent for FinOps that automates data retrieval, analysis, and decision-making in cloud cost management, a novel application in this domain.

Findings

The AI agent effectively understands and executes FinOps tasks.

The agent's performance matches that of human practitioners.

Metrics show successful automation of cost optimization processes.

Abstract

FinOps (Finance + Operations) represents an operational framework and cultural practice which maximizes cloud business value through collaborative financial accountability across engineering, finance, and business teams. FinOps practitioners face a fundamental challenge: billing data arrives in heterogeneous formats, taxonomies, and metrics from multiple cloud providers and internal systems which eventually lead to synthesizing actionable insights, and making time-sensitive decisions. To address this challenge, we propose leveraging autonomous, goal-driven AI agents for FinOps automation. In this paper, we built a FinOps agent for a typical use-case for IT infrastructure and cost optimization. We built a system simulating a realistic end-to-end industry process starting with retrieving data from various sources to consolidating and analyzing the data to generate recommendations for optimization. We defined a set of metrics to evaluate our agent using several open-source and close-source language models and it shows that the agent was able to understand, plan, and execute tasks as well as an actual FinOps practitioner.