<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>finops on Dhaval Shah</title><link>https://www.dhaval-shah.com/categories/finops/</link><description>Recent content in finops on Dhaval Shah</description><generator>Hugo -- gohugo.io</generator><lastBuildDate>Thu, 13 Aug 2026 01:00:50 +0000</lastBuildDate><atom:link href="https://www.dhaval-shah.com/categories/finops/index.xml" rel="self" type="application/rss+xml"/><item><title>Three Azure Cost Leaks - And the Analysis Process That Found Them</title><link>https://www.dhaval-shah.com/finops-azure-ai-review/</link><pubDate>Thu, 13 Aug 2026 01:00:50 +0000</pubDate><guid>https://www.dhaval-shah.com/finops-azure-ai-review/</guid><description>Background This is the fifth post in a series on AI-augmented software engineering across the disciplines that matter most for production grade enterprise systems. The earlier posts covered ground that's probably more familiar with software engineering fraternity:
Three Fintech Architecture Post-Mortems The GC Summary Report Wasn't Wrong Same JSON Storage Problem, Different Database A Black Friday Incident That Took 9 Days to Resolve This post covers different ground - Cloud Cost Optimization.</description></item></channel></rss>