This week's episode of FinOps in Action takes a different shape, with PointFive Co-Founder and Chief Product Officer Gal Ben-David stepping in to host a conversation with Mike Graff, Infrastructure Architecture Director at Dolby Laboratories and a FinOps Ambassador. Mike shares a couple of real cost inefficiencies he's tracked down in the wild, then the conversation opens up into bigger questions about where FinOps is headed from new roles emerging on the engineering side to how AI might change the way teams find and fix waste. It's a grounded, practitioner-led episode for anyone trying to close the gap between what engineering builds and what finance sees on the bill.

Here’s what we talked about:

• Small misconfigurations can hide big costs. A couple of the “craziest” inefficiencies Mike’s found weren’t exotic, they were simple settings almost anyone could miss, and easy to fix once spotted.

• Platform tools don’t always keep pace with the cloud. Vendors can lag behind what cloud providers already support, and Mike argues teams shouldn’t just accept those gaps.

• Cost optimization and security fight the same uphill battle. Both compete for engineering attention against feature deadlines and both benefit from getting into the conversation earlier.

• The FinOps role may be split in two. Mike and Gal dig into whether cost efficiency is becoming its own engineering discipline, separate from the finance side of FinOps.

• You don’t need a title to start. Many practitioners begin as a side project, start small, find one win, and let momentum build.

"It all starts somewhere. It starts with poking into that data transfer dashboard and figuring out what's going on. Just get those wins, chipping away at the low hanging fruit.” - Michael Graff

Connect with Michael:

• LinkedIn: https://www.linkedin.com/in/michaelegraff/

• Website: https://www.dolby.com/

In this episode of FinOps in Action, PointFive Co-Founder and Chief Product Officer Gal Ben-David sits down with Mike Graff, Infrastructure Architecture Director at Dolby Laboratories, to make the case that closing the gap between engineering and finance takes more than a mandate from the top, it takes technical fluency on the ground. Mike breaks down why some of the costliest inefficiencies are hiding in plain sight and easy to fix once someone knows to look, how cost optimization and security fight the same uphill battle for engineering attention, why the FinOps role may be splitting into a distinct engineering discipline as organizations scale, and why you don't need a title or a mandate to start, just curiosity and one early win.

This episode of FinOps in Action pulls together voices from across the industry, including Pathik Sharma of Google Cloud, Emily Cornock, Kevin Mueller, Zach Johnson, Henrique, Shailaja Beeram, Marit Hughes, and Oliver Milke, all wrestling with the same question: how do you actually manage the cost of AI. Two years ago the idea of "AI for FinOps" got brushed off as premature. Now it sits in the top three priorities in the FinOps Foundation's own survey data, and for good reason. The guests dig into why AI spend is accelerating faster than cloud ever did, why most companies still cannot prove ROI on it, and what it actually looks like to build guardrails without slowing teams down. It is a rapid fire look at where FinOps practice is headed as AI stops being a side experiment and starts becoming the biggest line item on the P&L.Here’s what we talked about:

• Treat your AI investment like a science experiment. Control your variables, change one thing at a time, and always tie spend back to a measurable business outcome, not just a feeling that things are getting more productive.

• Token cost is only the tip of the iceberg. Storage, the adaptive storage layer, GPU and TPU compute, and platform hosting all add up, so don’t let your team optimize the visible 20% while ignoring the rest.

• Not all AI actions carry the same risk, so treat them differently. Let AI handle low stakes, non disruptive work like cost allocation labeling on its own, but keep a human in the loop for anything disruptive, like rightsizing a Kubernetes workload during Black Friday.

• Don’t let analysis paralysis become your default setting. Make your best reasonable estimate on AI spend, document your assumptions, and move forward instead of letting fear triple your forecasts.

• Shift FinOps further left than you think you need to. Get engineering and product involved in AI cost conversations early, because the goal is not command and control, it’s giving teams the data to make good decisions themselves.

"I think the myth is that FinOps is something that you finish, right? You don't ever arrive at FinOps." - Amanda Wagner

Connect with Amanda and Sean:

• Amanda’s LinkedIn: https://www.linkedin.com/in/amanda--wagner/

• Sean’s LinkedIn: https://www.linkedin.com/in/sean~kennedy/

• Website: https://www.zf.com/usa_canada/en/home/home.html

In this episode of FinOps in Action, host Taylor Houck brings together FinOps practitioners from across the industry to tackle one of the field's toughest new challenges: managing AI spend. The conversation digs into why token costs are only part of the picture, how teams can tell if their AI investments are actually paying off, and where AI can be trusted to act on its own versus where humans need to stay in control. Along the way, the group shares real-world examples of costs brought under control without sacrificing performance and what FinOps practitioners should be doing right now to keep up with how fast AI is changing.

In this episode of FinOps in Action, host Taylor Houck welcomes two guests for the first time on the show, Sean Kennedy, Senior Cloud FinOps Consultant, and Amanda Wagner, Cloud FinOps Consultant, both from Trace3. Sean focuses on presales strategy and partner enablement across Trace3's FinOps practice, while Amanda works directly with a portfolio of customers on the practitioner side. Together they unpack why FinOps never really finishes, how customer maturity changes the conversation, and what the AI cost landscape looks like right now.

Here’s what we talked about:

• FinOps is a cycle, not a destination. There’s no finish line, just an ongoing loop of informing, optimizing, and operating that adapts as engineering, pricing, and business needs change.

• Document your processes before you need them. Repeatable procedures for tagging, showback, and optimization reviews protect your FinOps program from falling apart when key people leave.

• Meet customers at their maturity level. Don’t push budgeting and forecasting on a team that’s still working on basic visibility; introduce concepts in bite-sized chunks as they’re ready.

• The biggest wins often come from on-prem-to-cloud transitions. Companies migrating legacy infrastructure and mindsets tend to have the most untapped savings potential, partly because the human resistance to change is as big a factor as the technical work.

• Clarify what “AI spend” actually means before optimizing it. Seat-based licenses, hyperscaler AI services, GPU infrastructure, and token-based usage are all totally different cost problems that need different strategies.

"I think the myth is that FinOps is something that you finish, right? You don't ever arrive at FinOps." - Amanda Wagner

Connect with Amanda and Sean:

• Amanda’s LinkedIn: https://www.linkedin.com/in/amanda--wagner/

• Sean’s LinkedIn: https://www.linkedin.com/in/sean~kennedy/

• Website: https://www.zf.com/usa_canada/en/home/home.html

In this episode of FinOps in Action, Taylor Houck sits down with Sean Kennedy and Amanda Wagner of Trace3 to make the case that FinOps isn't a project you finish, it's a discipline you repeat. Sean and Amanda break down why the biggest mistake teams make is treating cost optimization like a checklist, how documented, repeatable processes protect a FinOps program when the person who built it moves on, why companies shifting from on-prem to cloud tend to have the most untapped savings on the table, and why the fast-expanding world of AI spend, from seat-based licenses to token usage to GPU infrastructure, demands its own clarity before teams can even start optimizing it.

In this episode of FinOps in Action, host Taylor Houck sits down with Piotr Kuczmera, Head of FinOps at ZF Group, to unpack how he built a FinOps practice from scratch at a 150,000 person global enterprise, and why the first question every organization should ask is not “how do we cut costs” but “what exactly do we need?”

Piotr shares how a single report request from his manager five years ago turned into a full scale internal FinOps platform, a hub and spoke operating model across 15 to 20 business units, and a team built on the combination of FinOps practitioners and data engineers working side by side.

Here is what they covered:

• Why FinOps is more than cost savings. Piotr explains that equating FinOps with cost optimization alone leads to failed implementations, and that starting with a clear requirements conversation, like a tailor fitting a suit to the right occasion, is what sets a practice up for long term success.

• How chargeback became the turning point. When ZF connected individual names to specific budgets and sent real invoices, something clicked. Engineers and budget holders stopped asking about aggregate project spend and started asking “why am I paying this much and how do I reduce it?”

• The case for building your own platform. ZF evaluated the major third party FinOps tools and walked away. Licensing costs, limited multi-cloud support, and a lack of flexibility in adapting to existing finance and procurement processes all pointed toward a homegrown solution built on native cloud tooling and a BI layer they already owned.

• The hub and spoke model for FinOps at scale. At the center sits a core team of FinOps consultants and data engineers. In each business unit, a nominated budget owner and a FinOps champion bridge the gap between central strategy and local execution.

• AI for FinOps, not just FinOps for AI. Piotr is most energized by how AI can automate repetitive FinOps workflows and take recommendations beyond basic CPU and memory parameters to consider commitment connections, usage patterns, and resource families all at once. He also sees FinOps as a function that should open doors for AI adoption, not slow it down.

• FinOps as connective tissue. Across financial colleagues, engineering teams, and business managers, Piotr sees FinOps as the function responsible for translating the right information to the right persona, not flooding finance with VM data, and not giving engineers a budget variance they cannot act on.

"Stay humble and don't be afraid of challenges." - Piotr Kuczmera

Connect with Piotr:

• LinkedIn: https://www.linkedin.com/in/piotr-kuczmera-59b39712a/

• Website: https://www.zf.com/usa_canada/en/home/home.html

In this episode of FinOps in Action, Taylor Houck sits down with Piotr Kuczmera, Head of FinOps at ZF Group, to make the case that a successful FinOps practice starts with a question, not a tool: what do we actually need? Piotr breaks down how he built ZF's entire FinOps program and platform from scratch over five years, starting with a single cloud spend report, why his team chose to build in-house rather than buy so they could stay flexible across a multi-cloud strategy, how precise chargeback turned cost from an abstract line item into something engineers felt personally accountable for, and why the real opportunity ahead isn't just managing AI spend but using AI to make FinOps itself faster and more capable, so long as governance never becomes a blocker to innovation.

In this episode of FinOps in Action, Taylor sits down with Ruby Agarwal, VP of Engineering at Avaya, whose responsibilities span engineering, DevOps, DevSecOps, CloudOps, and FinOps. Ruby shares how her team reduced cloud spend by 75% in just two months, why visibility matters more than perfect data, and how cost awareness becomes part of engineering culture rather than a one-time optimization project. They also explore how AI is changing operational decision-making and why the future of FinOps will depend on balancing innovation with discipline.

Here’s what they got into:

• Start with visibility, not spreadsheets of despair. Ruby’s team didn’t need perfect data to cut 75% of spend, they needed a one slider view everyone could understand. Skip the 20 page report and focus your team’s attention on the handful of buckets driving most of the cost.

• Cost isn’t the enemy, inefficiency is. Turning everything off would bring spending to zero, but that’s not the goal. The goal is getting the same performance, reliability, and security for less, not gutting the services delivering value.

• Build your COGS model before you write code. On Avaya Infinity, Ruby’s team defined their cost model before a single line was written, and they publish and measure it every month. Bake cost discipline into the architecture from day one instead of cleaning up after launch.

• Separate your AI innovation zone from your production zone. Let engineers experiment freely with the most powerful models while building, but production agents don’t need the flashiest reasoning model, they need the cheapest one that gets the job done reliably.

• The skills that carry your career aren’t technical. Ruby calls systems thinking, structured decision making, communication, and connection building the real differentiators, not soft skills but critical skills. Investing in those early pays off longer than any single technology wave.

“ Your first cloud cost bill will be dependent on what architectural decisions you have made for your projects.” - Ruby Agarwal

Connect with Ruby:

• LinkedIn: https://www.linkedin.com/in/rubyagarwal/

• Website: https://www.avaya.com/en/

In this episode of FinOps in Action, Taylor Houck sits down with Ruby Agarwal, VP of Engineering at Avaya, to make the case that FinOps discipline has to be architectural from day one, not cleanup after the fact. Ruby breaks down how she cut cloud spend by roughly 75% in two months by building visibility first and turning cost awareness into a team habit rather than a mandate, how she splits AI into an "innovation zone" versus a "production zone" so experimentation stays free while production spend stays capped and matched to the simplest model for the job, and why the real risk ahead is intelligence scaling faster than governance, making cost discipline and clear decision-making from data more important than ever.

Taylor Houck sits down with Pathik Sharma, Cloud Cost Optimization Lead at Google Cloud and Cofounder of their Cloud FinOps practice, to talk about how AI is closing the gap between knowing and doing in FinOps. Pathik shares how teams can hand off low risk tasks like tagging and labeling to AI agents while keeping humans in the loop on anything production related, and offers a four bucket framework for evaluating AI ROI: cost efficiency, productivity, differentiation, and revenue. His take for practitioners: embrace the change, learn the tooling, and let AI handle the friction so you can focus on business value.

Here’s what they got into:

• Don’t silo your FinOps practice. Cost optimization doesn’t exist in a vacuum. Factor in performance, security, scalability, and capacity from the start, and build tight relationships with platform, SRE, and app teams to get anything done.

• Use AI agents for the low risk wins first. Start with non-disruptive tasks like tagging and labeling before giving AI autonomy over anything that touches production. Build confidence incrementally.

• Keep humans in the loop on consequential actions. Have your AI agent create a pull request and route it to the application owner for approval rather than pushing changes directly. The app team still owns uptime.

• Don’t pick your AI model on instinct. Build a golden dataset, define what good looks like, and test models against it. One retail company cut their AI costs from $340K to $17K a month by switching models after running the data.

• Start from the problem, not the solution. Identify the real friction points your FinOps and engineering teams face, then figure out where AI reduces that friction. Chasing AI for its own sake is how you burn the budget without value.

"Embrace the change that is happening and learn it. FinOps holds keys to the kingdom" - Pathik Sharma

Connect with Pathik:

• LinkedIn: https://www.linkedin.com/in/pathik-sharma/

• Website: https://pathiksharma.com/

In this episode of FinOps in Action, Taylor Houck sits down with Pathik Sharma, cloud cost optimization lead at Google Cloud and co-founder of Google's Cloud FinOps practice, to make the case that agentic FinOps is the next evolution of the discipline. Pathik breaks down why the real opportunity isn't replacing FinOps teams with AI but using an army of agents to close the gap between knowing and doing, how customers are drawing the line between safe autonomous actions like cost allocation and labeling versus high-risk moves like Kubernetes rightsizing that still need a human in the loop, and why managing the explosion in AI spend means looking far beyond token costs to the full stack of infrastructure, storage, and change management behind it.

In this episode of FinOps in Action, host Taylor Houck sits down with Gerard Sanchez, Head of Enterprise Platforms and Cloud Technology at Resolution Life, to unpack what it actually looks like to build a cloud engineering, DevOps, and FinOps practice from absolute zero at a company that was only six months old when he joined. Gerard brings a hardcore infrastructure and networking background to a discipline that too often gets siloed away from engineering, and the results speak for themselves.

Here’s what they covered:

• Set up automated cost alerts and guardrails from the start. Getting notified about a surprise bill is useful, but building automations that throttle or shut down rogue functions before the cost spiral is where you really want to be.

• Tagging is non negotiable. If you can’t trace spend back to a specific application or team, you’ll never have the visibility to make smart optimization decisions.

• Shift FinOps left into the architecture and design phase. Catching cost inefficiencies before they deploy is far more effective than trying to optimize them after the fact.

• Match your cloud services to the actual workload. Before defaulting to high-performance, high cost infrastructure, pressure test whether the use case actually demands it. A Toyota Corolla solution often performs just as well as a Bugatti for the average user.

• Keep humans in the loop as you adopt AI. Even as agentic systems get more capable, building strong permissions, tagging, and audibility into your AI framework now will save you from costly and chaotic surprises later.

"I think especially for production environments or environments that are super sensitive, there's always gonna be a human in the loop." — Gerard Sanchez

Connect with Gerard:

• LinkedIn: https://www.linkedin.com/in/gerard-r-sanchez/

• Website: https://www.resolutionlife.com/

In this episode of FinOps in Action, Taylor Houck sits down with Gerard Sanchez, head of enterprise platforms and cloud technology at Resolution Life, to make the case that FinOps maturity is only as strong as the foundation you build it on. Gerard breaks down how he stood up an entire cloud engineering and FinOps practice from scratch at a six-month-old company, why shifting cost visibility left into the architecture and design phase is what actually prevents waste instead of just catching it, and how embedding a technically fluent FinOps team inside engineering rather than finance is what makes optimization recommendations stick.

In this episode of FinOps in Action, Taylor sits down with Mike Jaco, who spent years at Mastercard running one of the most sophisticated hybrid cost programs in enterprise technology. A unified TBM and FinOps practice covering a multi-billion dollar technology budget, on-prem and public cloud, under a single common taxonomy.

Mike’s core belief: most large organizations will be hybrid forever, and the industry hasn’t caught up to what that actually requires.

Here’s what they covered:

• Build a common taxonomy before you do anything else. If your technology teams, product teams, and finance teams are using different languages to describe the same thing, no one is having the right conversation. Get everyone onto one shared structure first and everything else gets easier.

• Allocate cost down to the product level, not just the business unit. Stopping at the business unit level leaves too much room for ambiguity and not enough room for accountability. The more granular your allocation, the clearer it becomes who owns what and what action to take.

• Most large organizations will be hybrid forever, so build for it. Regulation, data sovereignty, and steady state workloads mean a full cloud migration is not realistic for most enterprises. Build your measurement framework to cover both worlds under one taxonomy or you are only ever telling half the story.

• Savings that stay in the team drive more action than savings that disappear. If engineers know that any efficiency they find gets reinvested into their own backlog rather than returned to a central budget, the motivation to optimize shifts completely. Self-funding growth is a more powerful incentive than a cost reduction target.

• FinOps is a people problem first and a data problem second. When an engineer is not making a change, it is almost never because they want to waste money. They are overloaded, they have competing priorities, or the task does not benefit them. Solve for the people dynamic and the technical changes follow.

“You can do more with the same budget.” - Mike Jaco

Connect with Mike:

• LinkedIn: https://www.linkedin.com/in/mike-jaco/

In this episode of FinOps in Action, Taylor Houck sits down with Mike Jaco, former leader of the unified TBM and FinOps practice at Mastercard, to make the case that most large enterprises will be hybrid forever and that FinOps only tells part of the story if you're not measuring both worlds the same way. Mike breaks down how Mastercard built a common taxonomy across a multi-billion dollar technology budget, why allocating cost down to the product level (rather than stopping at the business unit) is what actually drives accountability and action, and how a carbon allocation model built in parallel to cost ended up changing engineering conversations in ways that pure cost data never could.

In this episode of FinOps in Action, host Taylor Houck sits down with Kumar Singirikonda, Director of DevOps Engineering at Toyota Financial Services, FinOps Foundation board member, and author of the DevOps Automation Cookbook. Kumar makes the case that FinOps as we know it is only the first chapter, and the next chapter belongs to self-healing systems, AI-driven remediation, and a class of costs that traditional cost reports have never been able to surface.

Here is what they covered:

• The hidden costs that never appear on your dashboard. Track your direct cloud expenses all you want, but your biggest operational costs are invisible. A single 30 minute outage won’t change your infrastructure bill, yet it pulls in engineering teams, delays releases, and creates support volume that compounds for days.

• From cost control to value creation. Stop asking how to reduce cloud spend and start asking how to maximize business value from your engineering investments. When you do that, cost optimization becomes an outcome of operational maturity rather than the goal itself.

• What a self-healing platform actually looks like. When a routing issue triggers a 404 spike, your system should detect the anomaly, correlate it with the recent deployment, invoke a remediation workflow, and execute a rollback in under two minutes without a human touching a keyboard. That is what operational maturity actually looks like in practice.

• AI is not a silver bullet. A model alone does not create operational intelligence. Connect AI to your observability platforms, automation workflows, and remediation playbooks, and that is where the real value comes from.

"The biggest operational cost problem today is inefficiency rather than infrastructure pricing. Organizations that reduce operational friction unlock far greater long-term value than organizations focused only on reducing the compute spend." - Kumar Singirikonda

Connect with Kumar:

• LinkedIn: https://www.linkedin.com/in/kumarsingirikonda

• Website: https://www.toyota.com/usa

• Book link: https://www.amazon.com/DevOps-Automation-Cookbook-Harness-automation/dp/9355519060

In this episode of FinOps in Action, Taylor Houck sits down with Kumar Singirikonda, Director of DevOps Engineering at Toyota Financial Services and FinOps Foundation board member, to make the case that FinOps is only the first chapter of a much larger story. Kumar argues that operational inefficiency, not infrastructure spend, is the real cost problem organizations face today, breaks down why hidden costs like incident response, engineering toil, and deployment delays routinely exceed the cloud bill itself, and explains why the future belongs to self healing, AI driven systems that let engineers stop firefighting and start innovating.

In this episode of FinOps in Action, I sit down with senior FinOps manager Chitra Jadhav to explore what it truly takes to build a FinOps practice that lasts not just one that generates a list of recommendations nobody acts on.

Drawing on eight years of building FinOps programs from the ground up across six or more organizations, Chitra explains how governance, culture, and cross functional collaboration are the real foundations of cloud cost optimization and why FinOps is only as powerful as the trust and authority an organization is willing to give it.

From Architecture Review Boards to Production Readiness Reviews, this conversation unpacks how FinOps can embed itself into the engineering lifecycle before waste ever happens, and why the shift to AI spend makes getting those basics right more urgent than ever.

Here’s what we talked about:

• Why organizational recognition and governance are the prerequisites for any FinOps program to succeed

• How to assess FinOps maturity using the FinOps Foundation framework as a starting point

• Why FinOps is a team sport and how to engage finance, engineering, procurement, and application teams

• What ARBs and Production Readiness Reviews are and how FinOps earns a seat at the table

• How to approach optimization when native cloud provider recommendations run dry

• The importance of the FinOps champion model when working across large engineering organizations

"Instead of paying that money and incurring that waste, you are taking some steps proactively by making sure that you are present at these discussions." - Chitra Jadhav

Connect with Chitra:

• LinkedIn: https://www.linkedin.com/in/chitrajadhav/

• Website: https://www.rogers.com/

In this episode of FinOps in Action, Matan Ben-Ishay draws on experience spanning EY cloud consulting to FP&A engineering at Credit Karma to argue that misaligned incentives, not technical complexity, are FinOps practitioners’ biggest obstacle. He breaks down why giving engineers public credit for cost optimization is the key to driving org wide adoption, how three year commitment forecasting with capacity planning and finance creates a true 360 degree view of infrastructure decisions, and why governance best practices must be embedded from day one, because the engineer who skips retention policies today becomes the manager who doesn’t care about them tomorrow.

In this special episode of FinOps in Action, PointFive’s Co-Founder and CPO, Gal Ben David sits down with Matan Ben-Ishay, leading FP&A, Engineering, and Cloud Infrastructure at Credit Karma, to unpack a real-world cloud efficiency discovery that led to tens of millions of dollars in savings without reducing usage or cutting performance.

Matan shares how a simple assumption about “cheaper” CPUs quietly inflated costs, why billing data alone didn’t reveal the problem, and how the true FinOps impact only happens when finance and engineering work side by side.

We also covered how FinOps wins happen at the intersection of finance, engineering, and product and how understanding accounting concepts is a career advantage in FinOps.

Here’s what we talked about:

• How a hidden GCP dataflow inefficiency cost tens of millions until Matan sat with engineers and discovered faster CPUs actually cost less per hour

• Why rigid governance policies backfire and how flexible guidelines that encourage engineers to evaluate their actual workload needs prevent waste

• Why FinOps professionals need to understand accounting concepts (depreciation, amortization, commitments)

• The emerging need for cloud efficiency engineers embedded in teams who can bridge the gap between FinOps strategy and hands-on optimization work

• How running parallel tests of old vs. new approaches proves value to skeptical engineers and turns short-term testing costs into long-term savings

• The structural friction between engineering OKRs (focused on launches and reliability) and cost efficiency, and why engineers should treat cloud spend like any other company resource

“We need those cloud efficiency engineers in order to understand.” - Matan Ben-Ishay

Connect with Matan:

• LinkedIn: https://www.linkedin.com/in/matan-ben-ishay/

• Website: https://www.creditkarma.com/

In this episode of FinOps in Action, Matan Ben-Ishay draws on experience spanning EY cloud consulting to FP&A engineering at Credit Karma to argue that misaligned incentives, not technical complexity, are FinOps practitioners’ biggest obstacle. He breaks down why giving engineers public credit for cost optimization is the key to driving org wide adoption, how three year commitment forecasting with capacity planning and finance creates a true 360 degree view of infrastructure decisions, and why governance best practices must be embedded from day one, because the engineer who skips retention policies today becomes the manager who doesn’t care about them tomorrow.

In this episode of FinOps in Action, I sit down with Oliver Milke from TD, who shares how he built a Data FinOps practice from the ground up inside a large financial institution, starting as a team of one and scaling into a dedicated function.

Coming from a background in economics and finance, Oliver offers a unique perspective on cloud cost optimization. Rather than focusing solely on infrastructure, his team goes one level deeper: tracking and allocating costs at the data layer, down to individual tables, workloads, and user groups.

The conversation explores why traditional FinOps models fall short for shared data platforms, how transparency drives accountability, and what it takes to operationalize cost awareness across thousands of users. Oliver also breaks down the role of education, chargeback, and engineering collaboration in building a sustainable FinOps practice.

Here’s what we talked about:

• What “Data FinOps” is and how it differs from traditional FinOps

• Why shared data platforms require a fundamentally different cost model

• Building cost visibility down to the table and workload level

• Moving from showback to chargeback and how it shifts user behavior

• Platform vs. workload-level optimization strategies

• The role of education and change management in FinOps success

“We recognized that this cannot be a one-off, but it needs to be a permanent ongoing thing.” - Oliver Milke

Connect with Oliver:

• LinkedIn: https://www.linkedin.com/in/oliver-milke/

• Website: https://www.td.com/us/en/personal-banking

In this episode of FinOps in Action, Oliver Milke draws on a background in economics and project management not engineering to make the case that FinOps for a shared data lake is an entirely different discipline than traditional cloud FinOps. He breaks down why a driver based chargeback model changed the way thousands of TD users think about cost, how combining billing data with telemetry is the key to unlocking true workload level insights, and why education isn't a nice to have for a mature FinOps practice it's a core pillar that needs its own dedicated headcount.

In this episode of FinOps in Action, I sit down with cloud architect and engineer Shailaja Beeram to explore a fundamentally different perspective: cost inefficiencies don’t start in the billing file, but in architectural decisions made long before deployment.

Drawing on over 15 years of experience across AWS, Azure, and GCP, Shailaja explains how the way systems are designed, scaled, and evolved over time has a far greater impact on cloud spend than most teams realize.

From “zombie” networking resources to overlooked outbound traffic patterns, this conversation unpacks the hidden layers of cloud cost and why FinOps and architecture must converge to solve them.

Here’s what we talked about:

• Why cloud inefficiencies often stem from architecture and not just unused resources

• How NAT Gateways and private endpoints quietly drive unnecessary spend

• What “zombie resources” are and how they accumulate over time

• Why background traffic can mask true resource idleness

• The importance of revisiting architectural decisions as systems evolve

• The growing convergence of FinOps and cloud architectureQuote of the Show:

“FinOps… it doesn’t need to be separated. It should be a part of how you think about the architecture.” - Shailaja Beeram

Connect with Shailaja:

• LinkedIn: https://www.linkedin.com/in/shailaja-beeram-82324310a/

In this episode of FinOps in Action, Shailaja Beeram draws on over 15 years of cloud architecture experience across Azure, AWS, and GCP to make the case that most cloud inefficiencies don't start with the bill, they start with the architecture. She breaks down why networking costs like NAT Gateways are among the most overlooked sources of waste, how background traffic can mask idle resources and delay cleanup, and why engineers and FinOps practitioners need to converge around cost aware design decisions from the very beginning.

In this episode of FinOps in Action, I sit down with Kevin Mueller, Principal Architect of Cloud FinOps at Presidio, to unpack one of the most talked-about and most misunderstood concepts in FinOps: unit economics.

With over 20 years of experience spanning software engineering, AWS SaaS architecture, and cloud financial management, Kevin shares a grounded, practical perspective on why most organizations struggle to implement unit economics and how to actually get started.

The conversation explores the gap between visibility and action, why traditional cost allocation models fall short, and how engineering, product, finance, and sales all benefit when cloud spend is tied directly to business value. Kevin also dives into real-world examples, from optimizing SQL queries and Kubernetes workloads to navigating the growing complexity of AI-driven infrastructure.

If you’ve ever wondered how to move beyond dashboards and into meaningful optimization, or how to prove that your cloud investment is truly productive, this episode is for you.

Here’s what we talked about:

• Why most companies still aren’t doing unit economics (and what separates those who do it well)

• The difference between unit metrics and unit economics

• How to move from high-level allocation to actionable, engineering-driven insights

• How to approach tagging without overcomplicating your system

• Why AI is the next major FinOps challenge (and opportunity)

• The biggest challenges with shared resources like Kubernetes and what actually drives optimization

Quote of the Show:

“It’s so easy in FinOps to measure all of this stuff, put it in a dashboard and look at it and feel happy that it’s there, but you need to drive towards action.” - Kevin Mueller

Connect with Kevin:

• LinkedIn: https://www.linkedin.com/in/kevinmueller/

• Website: https://www.presidio.com/

In this episode of FinOps in Action, Kevin Mueller draws on over 20 years of engineering leadership to make the case for unit economics as the most powerful and most underutilized tool in FinOps. He breaks down why most companies never get it right, how to start simple and work top down rather than boiling the ocean, and why engineering led optimizations are where meaningful cloud savings actually happen.

In this episode of FinOps in Action, I sit down with Cristian Magherusan-Stanciu, Founder and Cloud Optimization Specialist at Leaner Cloud. With over 20 years in systems engineering and a background at Nokia, HERE Technologies, and AWS, Cristian built AutoSpotting, an open source tool that has helped more than 10,000 customers save money on EC2.

Cristian shared his hard-won framework for turning cloud cost insights into real savings, why automation alone will never be enough, and how AI has multiplied his output as a solo operator by an order of magnitude.

We also covered the hidden business context that can turn a routine optimization into a $30K/month win, and why cost avoidance is just as important as cost reduction when your cloud bill keeps growing.

Here’s what we talked about:

• Dashboards are only half the story and prioritize action over visibility. The biggest gains come to teams that actually implement recommendations, not just track them.

• Right size incrementally, not aggressively. Cutting a resource by 8x in one step is risky instead halve it, observe, and iterate to avoid introducing performance problems.

• Match your optimization approach to the application’s needs. Latency sensitive workloads, HA setups, and DR resources may appear wasteful but serve critical purposes and always bring engineering context into the conversation.

• Look beyond the metric to find the real opportunity. A $30K/month Aurora IO bill pointed to a forgotten database no one needed anymore and the fix wasn’t optimization, it was deletion.

• Track cost avoidance, not just savings. In high-growth environments, a flat or rising bill can still represent millions saved and measure what you would have paid without the work.

• AI has changed the game for solo practitioners. Cristian built in weeks what previously took a year or two, enabling him to ship a SaaS product and 30+ optimization tools as a one-person shop.

Quote of the Show:

"In just a few weeks with AI, I built what previously would have taken me a year or two. It's at least an order of magnitude more output per unit of time." - Cristian Magherusan-Stanciu

Connect with Cristian:

• LinkedIn: https://www.linkedin.com/in/cristimagherusan/

• Website: https://leanercloud.com/

• Website: https://autospotting.io/

In this episode of FinOps in Action, Cristian Magherusan-Stanciu shares how his decades of hands on experience in cloud engineering and optimization have shaped a deeply practical approach to FinOps, and what he’s learned working across startups and complex enterprise environments. He explains why dashboards and cost visibility alone don’t drive results, how meaningful savings come from taking consistent, prioritized action, and why balancing automation with human judgment is key to reducing cloud costs without introducing risk.

In this episode of FinOps in Action, I sit down with Nick Sudarikov, Finance Director of Technology Operations at InvestCloud, to break down how a background in finance and capital markets can actually make you a sharper, more effective FinOps practitioner.

Nick shares how he went from investment banker to hands on FinOps engineer, built a local AI powered stack to query billing data in plain English, and uncovered years of forgotten cloud spend hiding in plain sight.

We also cover why most cost tools will never catch your biggest waste, how to map cloud spend to real business value, and what it actually takes to have meaningful conversations with engineering teams.

Here’s what we talked about:

• Why FinOps practitioners should set up their own cloud accounts and get hands-on experience with the technology

• How understanding engineering workflows changes the conversation with tech teams

• The hidden cost problem that anomaly detection tools will never catch

• How Nick uses DuckDB, Parquet files, and AI to query billing data in plain English

• Why AI is set to boost FinOps productivity by a factor of 10 and which tools risk losing market share if they don’t adapt

• The importance of mapping cloud spend to business value before making any optimization recommendations

Quote of the Show:

"If you haven't started a Docker container ever in your life, probably you're missing out on a lot of things in FinOps." - Nick Sudarikov

Connect with Nick:

• LinkedIn: https://www.linkedin.com/in/nikolaysudarikov/

• Website: https://investcloud.com/

In this episode of FinOps in Action, Nick Sudarikov shares how his background in finance and capital markets, combined with a deep curiosity for engineering, has shaped his approach to FinOps, and what he’s learned working across startups, cloud resellers, and large-scale enterprise environments. He explains why cost optimization is rarely as simple as what shows up in a dashboard, how a lack of technical understanding can limit a practitioner’s impact, and why getting hands-on with the tools engineers use is critical to driving meaningful change.

In this episode of FinOps in Action, I sit down with Jason Ward, Lead FinOps Practitioner at C.H. Robinson, to break down how to take control of cloud spend without slowing down engineering.

Jason shares how he went from mechanic to FinOps leader, built a FinOps function from scratch, and uncovered massive savings, including a 90% cost reduction from a single optimization.

We also cover how to work with engineers (not against them), why most cost recommendations fail, and what it really takes to build a sustainable FinOps practice.

Here’s what we talked about:

• How to cut cloud costs without impacting performance

• Why data, not recommendations should drive decisions

• How to get engineers to actually buy into FinOps

• The importance of tagging and cost visibility early on

• When NOT to optimize and why some “waste” is intentional

• How AI is changing cloud spend and what to watch out for

Quote of the Show:

“Without that executive buy-in… most engineers… don’t care so much about the bottom line.” – Jason Ward

Connect with Jason:

• LinkedIn: https://www.linkedin.com/in/jward2/

• Website: https://www.chrobinson.com/en-us/

In this episode of FinOps in Action, Jason Ward shares how his unconventional path from data center operations into cloud engineering shaped his perspective on FinOps, and what he’s learned after building a practice from the ground up inside a large enterprise. He explains why cost optimization often fails when treated as a purely reactive exercise, how engineers naturally prioritize performance over cost, and why aligning with their perspective is critical to driving meaningful change. The conversation explores the importance of combining cost data with real usage metrics to build trust with engineering teams, how early wins like right-sizing can unlock significant savings, and why consistent engagement is what turns FinOps into a sustainable practice.

In this episode of FinOps in Action, I sit down with Loic Fournier, Senior Technical Account Manager at AWS and 2025 AWS Technical Field Community Legend. With over 25 years in technology, from mainframes to modern cloud, Loic has become one of the most recognized voices in AWS cost optimization.

Loic shared how the biggest mistake organizations make is treating FinOps as a one-time project rather than an ongoing practice, and what it actually takes to build governance that sticks across people, processes, and technology.

We also covered the AWS native tools available to every organization for free, the link between cost optimization and sustainability, and how AI workloads are reshaping FinOps priorities heading into 2026.

Here’s what we talked about:

• Why FinOps fails when treated as a project

• The three pillars of FinOps governance

• How to get executive buy-in

• AWS free tools you should activate today

• The hidden savings hiding in your EBS volumes

• FinOps as a proxy for sustainability

• Managing AI costs before they spiral

Quote of the Show:

“FinOps is a proxy for sustainability. When you help a customer perform better in FinOps, you are also helping save the world a little bit.”- Loic Fournier

Connect with Loic:

• LinkedIn: https://www.linkedin.com/in/loicfournier/

• Website: https://aws.amazon.com/

In this episode of FinOps in Action, Loic Fournier shares how his decades of experience across infrastructure, cloud, and customer advisory roles have shaped his perspective on cost optimization, and what he’s learned after working with organizations that treat FinOps as a one-time project instead of a long-term practice. He explains why sustainable cost optimization starts with governance built on people, processes, and technology, how visibility and cost allocation are foundational to any FinOps journey, and why organizations must invest in education and accountability to scale their efforts. The conversation explores how teams can evolve from reactive cost-cutting to proactive cost management, the role of native cloud tooling in identifying and operationalizing savings, and why celebrating wins is critical to driving cultural adoption. Loic also dives into the growing impact of AI on cloud spend, highlighting the importance of early governance, thoughtful model selection, and efficiency-focused experimentation, while emphasizing that effective FinOps not only reduces cost but also supports broader sustainability goals.

In this episode of FinOps in Action, I sit down with Jai Padmanabhan, who shares why cloud cost is no longer just a finance concern; it’s a core responsibility for engineering teams. Drawing on decades of experience leading platforms in complex and regulated environments, Jai explains how the shift to the cloud fundamentally changed how organizations think about cost, and why treating it as an afterthought leads to reactive, high-pressure optimization later on.

He breaks down how teams can build cost awareness into their day-to-day workflows, from setting budgets and enforcing guardrails to leveraging automation that scales infrastructure up and down based on real demand.

Jai also explores the trade-offs between efficiency and reliability, emphasizing why SLAs should always take priority in production and how over-optimization can introduce unintended consequences. From storage strategies in regulated industries to the hidden complexity of data transfer costs, he shares practical insights on where organizations commonly overspend and how to avoid it.

Here’s what we talked about:

• The dangers of reactive cost optimization after the bill shows up

• Building cost awareness into engineering teams through budgets and guardrails

• How Kubernetes changes the approach to cost optimization

• Storage optimization strategies, especially in regulated industries

• How AI usage is impacting cloud spend (and why it’s still early)

• Why FinOps should be treated as everyone’s responsibility

Quote of the Show:

“ Treat cloud costs as if you were paying a bill from your credit card.” - Jai Padmanabhan

Connect with Jai:

• LinkedIn: https://www.linkedin.com/in/jaishankar-padmanabhan-software-executive/

In this episode of FinOps in Action, Jai Padmanabhan shares how his experience leading engineering and platform teams shaped his perspective on cloud cost, and what he learned after seeing organizations treat optimization as a reactive exercise once the bill arrives. He explains why cost must be treated as a first-class engineering concern alongside performance and security, how the shift to the cloud turned every engineer into a daily decision-maker on spend, and why building guardrails, automation, and accountability into engineering workflows is critical to avoiding unnecessary costs.

In this episode of FinOps in Action, I sit down with Stijn Depril, FinOps Architect at Euroclear and Co-Founder of Costra. He shares why cloud visibility is the starting point for any successful FinOps practice. Drawing on decades of experience in infrastructure and cloud architecture, he explains how many organizations rushed into the cloud without fully understanding the financial implications and why FinOps is ultimately about building the right culture, not just cutting costs.

Stijn discusses the importance of governance, leadership buy-in, and engineering collaboration when managing cloud spend. He also explains why engineers hold more influence over cost optimization than many organizations realize, and how FinOps teams can empower them to make smarter decisions without slowing innovation. The conversation explores what it really takes to move from reactive cost management to a proactive, organization-wide mindset around cloud efficiency.

Here’s what we talked about:

• Why visibility is the foundation of effective FinOps

• The risks of moving to the cloud without cost awareness

• How governance helps guide engineers toward optimization

• Why FinOps is as much a cultural shift as it is a financial discipline

• The role engineers play in controlling cloud spend

• What leadership support means for a successful FinOps program

Quote of the Show:

“I don’t need to have all the trophies on my back here. That’s not necessary. I want the organization and the engineers and the architects to do the right job for the organization.” - Stijn Depril

Connect with Stijn:

• LinkedIn: https://www.linkedin.com/in/sdepril/

• Costra: https://costra.io/

• Website: https://www.euroclear.com/en.html

In this episode of FinOps in Action, Stijn Depril shares how his career evolved from decades in infrastructure and cloud architecture into FinOps, and what he learned after seeing firsthand how many organizations moved to the cloud without fully understanding their costs. He explains why visibility is the foundation of any successful FinOps practice, how a lack of governance leaves teams “flying blind,” and why cost optimization is impossible without first knowing where spend is going.