How Predictive Analytics Stops Budget Overruns Before They Happen?

How Predictive Analytics Stops Budget Overruns Before They Happen?

Author: Demetrios August 20, 2026 Duration: 29:23

Every engineer at Wayfair can now see, in real time, exactly what their code costs, and that's on purpose. Brent Eubanks, FinOps Architect at Wayfair, walks us through what happens when you stop treating AI spend as a finance problem and start treating it as an engineering one.The story that sticks with you: a team was burning $400k a month on an LLM-driven workflow, until they flipped the whole thing on its head - hard-coded logic doing the heavy lifting, AI called in only when it's actually needed. New spend: $80k. It's the kind of move that only makes sense once you realize more agentic autonomy isn't always the win it's sold as.From there we get into the machinery Wayfair has built to make cost ownership feel less like a punishment and more like a superpower: guardrails baked straight into coding copilots, predictive alerts that catch a team blowing its budget before the month even ends, a "spend captain" system that pushes budget decisions down to the people closest to the work, and a genuinely strange new question every tech leader is starting to ask: if you were staffing a company with a team of AI agents instead of people, what would that org chart even look like?Wayfair: https://www.wayfair.comBrent Eubanks: https://www.linkedin.com/in/brenteubanksDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:[00:00] Intro & teaser [01:14] Meet Brent (FinOps Architect, Wayfair) [02:34] Making AI adoption easy for developers [03:30] Three-area cost governance framework [04:45] Automated FinOps guardrails in pull requests [07:29] Predictive analytics: 400% over budget warnings [08:26] The 400% spender: asset or liability? [10:06] The "spend captain" model [12:13] The goal: 1000x more token use in two years [13:05] The thought experiment [15:08] The $400k workflow rebuilt for $80k [16:42] Hardcoded first, LLM as fallback [19:49] Engineers now own their full cost stack [20:13] The FinOps companion that works overnight [24:10] Guardrails injected before the pull request [26:17] Recurring tasks become automated skills [27:46] Sankey diagram: cost per agent step in real time [28:40] "The bill didn't blow up - that doesn't happen by accident"


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

Agentic Conversations (formally mlops.community)
Podcast Episodes
We Cut LLM Latency by 70% in Production [not-audio_url] [/not-audio_url]

Duration: 1:05:20
Maher Hanafi is an engineering leader who went from zero AI experience to self-hosting LLMs at enterprise scale — managing GPU costs, optimizing inference with TensorRT LLM, and building an AI platform for HR tech. In th…
Getting Humans Out of the Way: How to Work with Teams of Agents [not-audio_url] [/not-audio_url]

Duration: 50:30
Rob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Scie…
Fixing GPU Starvation in Large-Scale Distributed Training [not-audio_url] [/not-audio_url]

Duration: 52:48
Kashish Mittal is a Staff Software Engineer at Uber, working on large-scale distributed systems and core backend infrastructure.Fixing GPU Starvation in Large-Scale Distributed Training // MLOps Podcast #367 with Kashish…
arrowspace: Vector Spaces and Graph Wiring [not-audio_url] [/not-audio_url]

Duration: 56:01
Lorenzo Moriondo is a Technical Lead for AI at tuned.org.uk, working on AI agent protocols, graph-based search, and production-grade LLM systems.arrowspace: Vector Spaces and Graph Wiring // MLOps Podcast #365 with Loren…
Agentic Marketplace [not-audio_url] [/not-audio_url]

Duration: 51:26
Donné Stevenson is a Machine Learning Engineer at Prosus, working on scalable ML infrastructure and productionizing GenAI systems across portfolio companies.Pedro Chaves is a Data Science Manager at OLX Group, working on…
Durable Execution and Modern Distributed Systems [not-audio_url] [/not-audio_url]

Duration: 1:00:36
Johann Schleier-Smith is the Technical Lead for AI at Temporal Technologies, working on reliable infrastructure for production AI systems and long-running agent workflows. Durable Execution and Modern Distributed Systems…