Shift Left FinOps

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 North
• 01:11 Dave’s role and Rewind’s major cost drivers
• 02:47 What shifting left means in practice
• 05:37 Cost ownership and modeling across the SDLC
• 07:42 Catching waste with alerts and monthly reviews
• 09:20 Breaking down costs by service, platform, and customer
• 11:33 Engaging engineers through visibility and cost champions
• 13:40 Balancing cost optimization with shipping speed
• 15:29 AI-powered cost analysis and interactive simulators
• 17:24 Managing AI spend and improving prompt caching
• 19:16 Diff Vader: AI code reviews with human guardrails
• 23:05 Practical savings from logs and database records

Links

• Rewind
• Frugal AI
• Watch on YouTube
• Listen on Spotify
• Listen on Apple Podcasts

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 in
• Why the FinOps community is still early in adoption even though the practice is now part of the core curriculum
• The incentive gap in cost optimization, where engineers can save millions without sharing in the upside but can still carry the downside risk of outages
• Why cost should become a standard engineering dimension alongside latency, performance, and reliability
• How cloud cost literacy helps engineers understand the cost impact of infrastructure choices across the full application lifecycle
• Practical KPI ideas, including cloud cost per revenue, cost per vCPU hour, and cost per gigabyte stored
• Why unit economics must be measured continuously in production, not just modeled at project kickoff
• How Roku uses cost per streaming hour as a company-level benchmark and requires new features to be offset by optimizations elsewhere
• The difference between FinOps and tokenomics, and why AI introduces a five-layer stack with its own KPIs
• How 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