KubeFM

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.

Thibault Jamet, Head of Runtime at Adevinta, shares his expertise running a multi-tenant Kubernetes platform. He dives into a chaotic incident where the Vertical Pod Autoscaler led to critical pod evictions. Thibault discusses the architecture of VPA and the debugging process that revealed hidden limits in Kubernetes. He emphasizes the importance of monitoring webhook latency and pod eviction rates to catch issues early. Listeners gain invaluable lessons on scaling challenges and operational strategies for maintaining high-performance systems.

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 Salt Lake City—register hereMore infoFind all the links and info for this episode here: https://ku.bz/5Sf5wpd8yInterested in sponsoring an episode? Learn more.

Jorrick Stempher, a junior software engineer and student at Windersheim, discusses his team's innovative predictive scaling system for Kubernetes clusters, leveraging machine learning. They utilize the Prophet model to forecast load patterns, enabling preemptive scaling decisions that improve response times dramatically. Stempher dives into the Node Ranking Index for efficient resource management and shares insights on real-world challenges like data validation and load testing. The conversation highlights practical approaches to optimize Kubernetes scalability in dynamic environments.