FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911240273526784 |
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| author | Vo, Ngoc Phuoc An Kesarwani, Manish Mahindru, Ruchi Narayanaswami, Chandrasekhar |
| author_facet | Vo, Ngoc Phuoc An Kesarwani, Manish Mahindru, Ruchi Narayanaswami, Chandrasekhar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization Vo, Ngoc Phuoc An Kesarwani, Manish Mahindru, Ruchi Narayanaswami, Chandrasekhar Artificial Intelligence 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. |
| title | FinOps Agent -- A Use-Case for IT Infrastructure and Cost Optimization |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25914 |