Shift Left FinOps

Mike Weider

Expert interviews with the Engineering and FinOps leaders moving cloud cost visibility and optimization closer to the point where decisions are made.

Episodes

  1. Oct 20

    How Okta Uses Cost Models to Make Better Engineering Decisions

    Cost models help engineering teams understand the financial impact of a product or architecture before it reaches production. But what makes a model useful, how accurate does it need to be, and what happens when real usage differs from the assumptions? In this episode, Mike speaks with Mariano Szklanny, Principal Operations Manager at Okta, about how cost modelling connects engineering decisions to pricing, margins, and business risk. Mariano shares a real example where a cost model exposed duplicated fixed infrastructure costs early enough for the team to change the architecture. He also explains how Okta decides which projects require modelling, how models are built and validated across teams, and how production data is used to compare assumptions with reality. What You’ll Learn How a cost model changed the architecture of an Okta feature The difference between a cost model, budget, forecast, and cloud calculator Why models should begin with unit cost before forecasting total spend How to identify the fixed and variable costs that matter most How Okta decides which products and features require cost modelling Why cost models are also communication tools for Engineering, Product, Finance, and Pricing How AI reduced the time required to build a model from one or two months to roughly one or two weeks Why Mariano replaced spreadsheets with interactive applications How instrumentation allows teams to compare assumptions with production usage Why cost modelling should enable business decisions rather than simply block spending Episode Chapters 00:00 Why engineering teams need cost models00:49 A cost model that changed an Okta architecture03:06 What Okta does and Mariano’s path into FinOps06:05 What is a cost model?09:10 Cost models versus budgets, forecasts, and cloud calculators11:38 How Mariano builds a cost model13:28 Unit costs and adoption scenarios14:19 How AI has changed the modelling process16:35 How long does a cost model take to build?18:05 Deciding which projects need modelling21:10 How Product, Engineering, Pricing, and Finance work together24:11 Instrumenting products and validating models after deployment27:17 Balancing cost with growth, security, and reliability30:16 Advice for teams getting started About Mariano Szklanny Mariano Szklanny is a Principal Operations Manager at Okta, where he combines a software engineering background, a Computer Systems Analysis degree, and nearly a decade at software consultancy Southworks with financial skills. He builds product cost models that surface financial risks and opportunities early and help protect margin. Mariano joined Auth0 as Engineering Operations Manager in late 2015, reporting to the CTO and co-founder, and has been at Okta since its acquisition of Auth0 in 2021. Cloud cost management found him at Auth0 when he was asked to investigate a runaway AWS bill. The results led Finance to bring him onto the team and expand the work from cost optimization into profitability. Today, he works across Product, Engineering, Pricing and Packaging, and FP&A, connecting engineering and product decisions to what they cost to operate at scale. Mariano Szklanny on LinkedIn This episode is sponsored by Frugal AI.

  2. Oct 6

    Shift Left lessons learned from 30 years of engineering

    Shift Left: Lessons Learned from 30 Years of Building With Dave North, Principal Engineer at Rewind How do you make cost part of engineering without slowing engineers down? Mike Weider talks with Dave North about how Rewind brings cost visibility and ownership into the software development lifecycle. Dave shares practical lessons on modeling costs before building, spotting inefficiencies in production, and getting engineers engaged through visibility and cost champions. They also explore how AI is changing the process, from building interactive cost simulators to automating code reviews, and why small changes to prompts, logs, and database records can have a meaningful impact at scale. About the guest Dave North is a Principal Engineer at Rewind, a SaaS backup company serving more than 25,000 companies worldwide. He brings over 30 years of engineering experience across large corporations and startups. At Rewind, he has built and led CloudOps, platform, security, and IT teams. Today, he focuses on complex problems that span engineering and the wider business. Key takeaways “You build it, you run it, you cost it.” Engineering teams own the cost of their applications, while the platform team supports infrastructure optimization.Model costs before writing code. Early estimates should establish the scale of expected spend, not predict every penny.Close the loop in production. Monthly cost reviews, tagging, and budget alerts help teams compare expectations with actual usage and investigate anomalies.Make costs visible and relevant. Dave describes a previous initiative where team-level reports and a leaderboard helped reduce development-environment costs by roughly 75%.Focus on material opportunities. Cost champions and clear priorities help engineers address meaningful waste without getting distracted by tiny savings.Use AI to explore tradeoffs. Rewind uses AI-built simulators to test worker counts, autoscaling settings, load, and cost before implementation.Optimize AI usage too. Dave reports reducing token usage by approximately 80% in one application through prompt changes that enabled caching.Keep humans involved where risk is high. Rewind’s AI review tool, Diff Vader, handles many pull requests, while sensitive security and backup-path changes still require human review.Small changes add up at scale. Shorter log messages reportedly reduced logging costs by about 20%. Smaller database records can also reduce consumption. Chapters 00:00 Introducing Dave North01:11 Dave’s role and Rewind’s major cost drivers02:47 What shifting left means in practice05:37 Cost ownership and modeling across the SDLC07:42 Catching waste with alerts and monthly reviews09:20 Breaking down costs by service, platform, and customer11:33 Engaging engineers through visibility and cost champions13:40 Balancing cost optimization with shipping speed15:29 AI-powered cost analysis and interactive simulators17:24 Managing AI spend and improving prompt caching19:16 Diff Vader: AI code reviews with human guardrails23:05 Practical savings from logs and database records Links RewindFrugal AIWatch on YouTubeListen on SpotifyListen on Apple Podcasts

  3. Sep 13

    How Roku Uses a Single Metric to Align Engineering Teams on Cost

    Dieter Matzion, a cloud and technology infrastructure leader at Roku with prior engineering leadership roles at Intuit, Netflix, and Google, joins Mike Weider to talk about how FinOps teams can shift cost awareness earlier in the engineering lifecycle. T he conversation covers why engineers often misjudge efficiency, how to create practical cost KPIs, and why AI is pushing the industry toward tokenomics as a separate discipline. We discuss how to make cost another engineering dimension alongside latency and performance, why leadership buy-in matters, and how teams can keep cloud spend aligned with business growth without slowing down innovation. Key topics Why shift-left FinOps delivers the most value when engineers are engaged before architecture decisions are locked inWhy the FinOps community is still early in adoption even though the practice is now part of the core curriculumThe incentive gap in cost optimization, where engineers can save millions without sharing in the upside but can still carry the downside risk of outagesWhy cost should become a standard engineering dimension alongside latency, performance, and reliabilityHow cloud cost literacy helps engineers understand the cost impact of infrastructure choices across the full application lifecyclePractical KPI ideas, including cloud cost per revenue, cost per vCPU hour, and cost per gigabyte storedWhy unit economics must be measured continuously in production, not just modeled at project kickoffHow Roku uses cost per streaming hour as a company-level benchmark and requires new features to be offset by optimizations elsewhereThe difference between FinOps and tokenomics, and why AI introduces a five-layer stack with its own KPIsHow AI coding tools may eventually benefit from a dedicated cost agent that understands cloud economics Timestamps 00:00 - Dieter Matzion’s background in cloud infrastructure and FinOps 00:26 - Where shift-left FinOps stands in the community today 03:35 - Why engineers are often poor judges of efficiency 05:38 - The “good enough” mindset and why it fails for cloud commitments 07:25 - Incentives, downside risk, and why savings are rarely rewarded 09:50 - Making cost a normal engineering optimization dimension 10:47 - Why cloud cost literacy matters across the application lifecycle 12:32 - Basic KPIs teams can start with, from cloud cost per revenue onward 13:23 - Cost per vCPU hour and cost per gigabyte stored 15:09 - Why unit economics must be measured in production, not just planned 16:02 - A real example of AI tuning reducing monthly cost dramatically 16:23 - How a gossip protocol created a major cost explosion 16:50 - Roku’s cost per streaming hour benchmark 19:52 - Using proof of concept work to estimate material cost impact 20:53 - How cost checks happen at rollout level, not just per individual code change 21:53 - Alerts, escalation, and human-in-the-loop response 22:57 - Pure cost anomalies versus KPI-driven cost regressions 23:34 - Why tokenomics exists alongside FinOps 24:04 - FinOps versus tokenomics across the AI stack 25:46 - The five layers of the AI stack and how they change optimization 26:11 - New tokenomics KPIs like cache hit ratio and routing quality failure rate 27:35 - Why AI requires a distinct discipline beyond traditional FinOps 28:40 - How AI coding tools and AI application spend change the shift-left problem 29:19 - Current enterprise limits on AI usage and who drives most spend 30:54 - How agentic workflows could eventually include a cost-aware FinOps agent 31:49 - Best advice for teams in the crawl phase 32:10 - Start small, build successes, and get leadership buy-in 33:28 - Why Roku created cost per streaming hour to keep spend from outpacing revenue

    How Roku Uses a Single Metric to Align Engineering Teams on Cost

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Expert interviews with the Engineering and FinOps leaders moving cloud cost visibility and optimization closer to the point where decisions are made.