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In this closing episode, Bryan Ross (Field CTO at GitLab), Jane Yan (Principal Program Manager at Microsoft), Sean O’Meara (CTO at Mirantis) and William Rizzo (Strategy Lead, CTO Office at Mirantis) discuss how GitOps evolves in practice.How enterprises are embedding Flux into developer platforms and managed cloud services.Why bridging CI/CD and infrastructure remains a core challenge—and how GitOps addresses it.What leading platform teams (GitLab, Microsoft, Mirantis) see as the next frontier for GitOps.SponsorJoin the Flux maintainers and community at FluxCon, November 11th in Atlanta—register hereMore infoFind all the links and info for this episode here: https://ku.bz/tVqKwNYQHInterested in sponsoring an episode? Learn more.

In this episode, Philippe Ensarguet, VP of Software Engineering at Orange, and Arnab Chatterjee, Global Head of Container & AI Platforms at Nomura, share how large enterprises are adopting Flux to drive reliable, compliant, and scalable platforms.How Orange uses Flux to manage bare-metal Kubernetes through its SYLVR project.Why FSIs rely on GitOps to balance agility with governance.How Flux helps enterprises achieve resilience, compliance, and repeatability at scale.SponsorJoin the Flux maintainers and community at FluxCon, November 11th in Atlanta—register hereMore infoFind all the links and info for this episode here: https://ku.bz/tWcHlJm7MInterested in sponsoring an episode? Learn more.

In this episode, Michael Bridgen (the engineer who wrote Flux's first lines) and Stefan Prodan (the maintainer who led the V2 rewrite) share how Flux grew from a fragile hack-day script into a production-grade GitOps toolkit.How early Flux addressed the risks of manual, unsafe Kubernetes upgradesWhy the complete V2 rewrite was critical for stability, scalability, and adoptionWhat the maintainers learned about building a sustainable, community-driven open-source projectSponsorJoin the Flux maintainers and community at FluxCon, November 11th in Atlanta—register hereMore infoFind all the links and info for this episode here: https://ku.bz/bgkgn227-Interested in sponsoring an episode? Learn more.

This episode unpacks the technical and governance milestones that secured Flux's place in the cloud-native ecosystem, from a 45-minute production outage that led to the birth of GitOps to the CNCF process that defines project maturity and the handover of stewardship after Weaveworks' closure.You will learn:How a single incident pushed Weaveworks to adopt Git as the source of truth, creating the foundation of GitOps.How Flux sustained continuity after Weaveworks shut down through community governance.Where Flux is heading next with security guidance, Flux v2, and an enterprise-ready roadmap.SponsorJoin the Flux maintainers and community at FluxCon, November 11th in Atlanta—register hereMore infoFind all the links and info for this episode here: https://ku.bz/5Sf5wpd8yInterested in sponsoring an episode? Learn more.

Mai Nishitani, Director of Enterprise Architecture at NTT Data and AWS Community Builder, demonstrates how Model Context Protocol (MCP) enables Claude to directly interact with Kubernetes clusters through natural language commands.You will learn:How MCP servers work and why they're significant for standardizing AI integration with DevOps tools, moving beyond custom integrations to a universal protocolThe practical capabilities and critical limitations of AI in Kubernetes operationsWhy fundamental troubleshooting skills matter more than ever as AI abstractions can fail in unexpected ways, especially during crisis scenarios and complex system failuresHow DevOps roles are evolving from manual administration toward strategic architecture and orchestrationSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/3hWvQjXxpInterested in sponsoring an episode? Learn more.

Niels Claeys shares how his team at Dataminded built Conveyor, a data platform processing up to 1.5 million core hours monthly. He explains the specific optimizations they discovered through production experience, from scheduler changes that immediately reduce costs by 10-15% to achieving 97% spot instance usage without reliability issues.You will learn:Why the default Kubernetes scheduler wastes money on batch workloads and how switching from "least allocated" to "most allocated" scheduling enables faster scale-down and better resource utilizationHow to achieve 97% spot instance adoption through strategic instance type diversification, region selection, and Spark-specific techniquesNode pool design principles that balance Kubernetes overhead with workload efficiencyPlatform-specific gotchas like AWS cross-AZ data transfer costs that can spike bills unexpectedlySponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/hGRfkzDJWInterested in sponsoring an episode? Learn more.

Vitalii Horbachov explains how Agoda built macOS VZ Kubelet, a custom solution that registers macOS hosts as Kubernetes nodes and spins up macOS VMs using Apple's native virtualization framework. He details their journey from managing 200 Mac minis with bash scripts to a Kubernetes-native approach that handles 20,000 iOS tests at scale.You will learn:How to build hybrid runtime pods that combine macOS VMs with Docker sidecar containers for complex CI/CD workflowsCustom OCI image format implementation for managing 55-60GB macOS VM images with layered copy-on-write disks and digest validationNetworking and security challenges including Apple entitlements, direct NIC access, and implementing kubectl exec over SSHReal-world adoption considerations including MDM-based host lifecycle management and the build vs. buy decision for Apple infrastructure at scaleSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/q_JS76SvMInterested in sponsoring an episode? Learn more.

Ben Poland walks through Faire's complete CI transformation, from a single Jenkins instance struggling with thousands of lines of Groovy to a distributed Buildkite system running across multiple Kubernetes clusters.He details the technical challenges of running CI workloads at scale, including API rate limiting, etcd pressure points, and the trade-offs of splitting monolithic pipelines into service-scoped ones.You will learn:How to architect CI systems that match team ownership and eliminate shared failure points across servicesKubernetes scaling patterns for CI workloads, including multi-cluster strategies, predictive node provisioning, and handling API throttlingPerformance optimization techniques like Git mirroring, node-level caching, and spot instance management for variable CI demandsMigration strategies and lessons learned from moving away from monolithic CI, including proof-of-concept approaches and avoiding the sunk cost fallacySponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/klBmzMY5-Interested in sponsoring an episode? Learn more.

Danyl Novhorodov, a veteran .NET engineer and architect at Eneco, presents his controversial thesis that 90% of teams don't actually need Kubernetes. He walks through practical decision-making frameworks, explores powerful alternatives like BEAM runtimes and Actor models, and explains why starting with modular monoliths often beats premature microservices adoption.You will learn:The COST decision framework - How to evaluate infrastructure choices based on Complexity, Ownership, Skills, and Time rather than industry hypePlatform engineering vs. managed services - How to honestly assess whether your team can compete with AWS, Azure, and Google's managed container platformsEvolutionary architecture approach - Why modular monoliths with clear boundaries often provide better foundations than distributed systems from day oneSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/BYhFw8RwWInterested in sponsoring an episode? Learn more.

Running 30 Kubernetes clusters serving 300,000 requests per second sounds impressive until your Vertical Pod Autoscaler goes rogue and starts evicting critical system pods in an endless loop.Thibault Jamet shares the technical details of debugging a complex VPA failure at Adevinta, where webhook timeouts triggered continuous pod evictions across their multi-tenant Kubernetes platform.You will learn:VPA architecture deep dive - How the recommender, updater, and mutating webhook components interact and what happens when the webhook failsHidden Kubernetes limits - How default QPS and burst rate limits in the Kubernetes Go client can cause widespread failures, and why these aren't well documented in Helm chartsMonitoring strategies for autoscaling - What metrics to track for webhook latency and pod eviction rates to catch similar issues before they become criticalSponsorThis episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/rf1pbWXdNInterested in sponsoring an episode? Learn more.

Jorrick Stempher shares how his team of eight students built a complete predictive scaling system for Kubernetes clusters using machine learning.Rather than waiting for nodes to become overloaded, their system uses the Prophet forecasting model to proactively anticipate load patterns and scale infrastructure, giving them the 8-9 minutes needed to provision new nodes on Vultr.You will learn:How to implement predictive scaling using Prophet ML model, Prometheus metrics, and custom APIs to forecast Kubernetes workload patternsThe Node Ranking Index (NRI) - a unified metric that combines CPU, RAM, and request data into a single comparable number for efficient scaling decisionsReal-world implementation challenges, including data validation, node startup timing constraints, load testing strategies, and the importance of proper research before building complex scaling solutionsSponsorThis episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/clbDWqPYpInterested in sponsoring an episode? Learn more.

If you're running Java applications in Kubernetes, you've likely experienced the pain of slow pod startups affecting user experience during deployments and scaling events.Frédéric Gaudet, Senior SRE at BlaBlaCar, shares how his team solved the cold start problem for their 1,500 Java microservices using Istio's warm-up capabilities.You will learn:Why Java applications struggle with cold starts and how JIT compilation affects initial request latency in Kubernetes environmentsHow Istio's warm-up feature works to gradually ramp up traffic to new podsWhy other common solutions fail, including resource over-provisioning, init containers, and tools like GraalVMReal production impact from implementing this solution, including dramatic improvements in message moderation SLOs at BlaBlaCar's scale of 4,000 podsSponsorThis episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/grxcypt9jInterested in sponsoring an episode? Learn more.

Brian Donelan, VP Cloud Platform Engineering at JPMorgan Chase, shares his ingenious side project that automatically scales Kubernetes workloads based on whether his MacBook is open or closed.By connecting macOS screen lock events to CloudWatch, KEDA, and Karpenter, he built a system that achieves 80% cost savings by scaling pods and nodes to zero when he's away from his laptop.You will learn:How KEDA differs from traditional Kubernetes HPA - including its scale-to-zero capabilities, event-driven scaling, and extensive ecosystem of 60+ built-in scalersThe technical architecture connecting macOS notifications through CloudWatch to trigger Kubernetes autoscaling using Swift, AWS SDKs, and custom metricsCost optimization strategies including how to calculate actual savings, account for API costs, and identify leading indicators of compute demandCreative approaches to autoscaling signals beyond CPU and memory, including examples from financial services and e-commerce that could revolutionize workload managementSponsorThis episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/sFd8TL1cSInterested in sponsoring an episode? Learn more.

Data centers consume over 4% of global electricity and this number is projected to triple in the next few years due to AI workloads.Dave Masselink, founder of Compute Gardener, discusses how he built a Kubernetes scheduler that makes scheduling decisions based on real-time carbon intensity data from power grids.You will learn:How carbon-aware scheduling works - Using real-time grid data to shift workloads to periods when electricity generation has lower carbon intensity, without changing energy consumptionTechnical implementation details - Building custom Kubernetes schedulers using the scheduler plugin framework, including pre-filter and filter stages for carbon and time-of-use pricing optimizationEnergy measurement strategies - Approaches for tracking power consumption across CPUs, memory, and GPUsSponsorThis episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/zk2xM1lfWInterested in sponsoring an episode? Learn more.

Alessandro Pomponio from IBM Research explains how his team transformed their chaotic bare-metal clusters into a well-governed, self-service platform for AI and scientific workloads. He walks through their journey from manual cluster interventions to a fully automated GitOps-first architecture using ArgoCD, Kyverno, and Kueue to handle everything from policy enforcement to GPU scheduling.You will learn:How to implement GitOps workflows that reduce administrative burden while maintaining governance and visibility across multi-tenant research environmentsPractical policy enforcement strategies using Kyverno to prevent GPU monopolization, block interactive pod usage, and automatically inject scheduling constraintsFair resource sharing techniques with Kueue to manage scarce GPU resources across different hardware types while supporting both specific and flexible allocation requestsOrganizational change management approaches for gaining stakeholder buy-in, upskilling admin teams, and communicating policy changes to research usersSponsorThis episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.ioMore infoFind all the links and info for this episode here: https://ku.bz/5sK7BFZ-8Interested in sponsoring an episode? Learn more.

Mac Chaffee, a platform engineer and security champion, examines why developers often underestimate the complexity of running modern applications and how overconfidence leads to expensive technical mistakes.You will learn:Why teams reject Kubernetes then rebuild it piece by piece - understanding the psychological factors, like overconfidence, that drive initial rejection of complex but proven toolsHow to identify the tipping point when DIY solutions become more complex than adopting established orchestration tools, especially around scaling and high availability challengesThe right approach to abstracting Kubernetes complexity - why hiding the Kubernetes API often backfires and how to build effective guardrails instead of reinventing interfacesWhy mentorship gaps lead to poor technical decisions - how the lack of proper apprenticeship programs in tech results in teams making expensive mistakes when building infrastructureSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/9nFPmG85fInterested in sponsoring an episode? Learn more.

Marc Campora, a systems consultant with experience in high-throughput platforms, shares his analysis of a real customer deployment with 500+ microservices. He breaks down the cost implications, technical constraints, and operational trade-offs between Kubernetes containers and AWS Lambda functions based on actual production data and migration assessments.You will learn:Cost analysis frameworks for comparing Lambda vs Kubernetes across different traffic patterns, including specific examples of 3x savings potential and the 80/20 rule for service utilizationMigration complexity factors when moving existing microservices to Lambda, including cold start issues, runtime model changes, and why it's often a complete rewrite rather than a simple portDecision criteria for choosing between platforms based on traffic consistency, computational requirements, and operational overhead toleranceSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/5gMTkzLhVInterested in sponsoring an episode? Learn more.

Molly Sheets, Director of Engineering for Kubernetes at Zynga, discusses her team's approach to platform engineering. She explains why their initial one-cluster-per-team model became unsustainable and how they're transitioning to multi-tenant architectures.You will learn:Why slowing down deployments actually increases risk and how manual approval gates can make systems less resilient than faster, smaller deploymentsThe operational reality of cluster proliferation - why managing hundreds of clusters becomes unsustainable and when multi-tenancy becomes necessaryPractical multi-tenancy implementation strategies including resource quotas, priority classes, and namespace organization patterns that work in productionBetter metrics for multi-tenant environments - why control plane uptime doesn't matter and how to build meaningful SLOs for distributed platform healthSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/Rmpl8948_Interested in sponsoring an episode? Learn more.

A sophisticated GitLab CI/CD pipeline integrated with Argo CD was ultimately rejected in favour of simple FTP deployment, offering crucial insights into the real barriers facing cloud-native adoption in traditional organisations.David Pech, Staff Cloud Ops Engineer at Wrike and holder of all CNCF certifications, shares his experience supporting a PHP team after a company merger. He details how he built a complete cloud-native platform with Kubernetes, Helm charts, and GitOps workflows, only to see it fail against cultural and organizational resistance despite its technical superiority.You will learn:The hidden costs of sophisticated tooling - How GitOps pipelines with multiple moving parts can create trust issues when developers lose local control and must rely on remote processes they don't understandCultural factors that trump technical benefits - Why customer expectations, existing Windows-based infrastructure, and team readiness matter more than the elegance of your Kubernetes solutionPractical strategies for incremental adoption - The importance of starting small, building in-house operational expertise, and ensuring management advocacy at all levels before attempting cloud-native transformationsSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/_MWX5m6G_Interested in sponsoring an episode? Learn more.

If you're tasked with performance testing Kubernetes workloads without much guidance, this episode offers clear, experience-based strategies that go beyond theory.Stephan Schwarz, a DevOps engineer at iits-consulting, walks through his systematic approach to performance testing Kubernetes applications. He covers everything from defining what performance actually means, to the practical methodology of breaking individual pods to understand their limits, and navigating the complexities of Kubernetes-specific components that affect test results.You will learn:How to establish baseline performance metrics by systematically testing individual pods, disabling autoscaling features, and documenting each incremental change to understand real application limitsWhy shared Kubernetes components skew results and how ingress controllers, service meshes, and monitoring stacks create testing challenges that require careful consideration of the entire request chainPractical approaches to HPA configuration, including how to account for scaling latency, the time delays inherent in Kubernetes scaling operations, and planning for spare capacity based on your SLA requirementsThe role of observability tools like OpenTelemetry in production environments where load testing isn't feasible, and how distributed tracing helps isolate performance bottlenecks across interdependent servicesSponsorThis episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.More infoFind all the links and info for this episode here: https://ku.bz/yY-FnmGfHInterested in sponsoring an episode? Learn more.