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Most teams scale Kubernetes by thinking about pods and nodes. At Render, Brian Stack ran into a different dimension: hundreds of thousands of namespaces per cluster, multiplied across DaemonSets that list-watch every namespace.

Brian explains how Render traced the issue through Calico and Vector, worked with upstream maintainers, and turned memory profiling into operational wins: lower node costs, lighter API-server load, and faster rollouts.

In this interview:

• Why namespaces can become a hidden scaling bottleneck

• How DaemonSets multiply memory and control-plane pressure

• How profiling, staging clusters, and upstream collaboration freed 7 TiB

• Why pushing from an 80% fix to a complete fix can make teams faster

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/0mrvCsXrV

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What happens when an AI agent stops generating Kubernetes YAML and starts operating the cluster directly?

Mike Solomon, software engineer at AIATELLA, explains how his team moved from a sprawling Helm setup to Markdown-driven infrastructure specs that Claude Code can execute, test, and refine.

You will learn

• Why Helm became hard to maintain for a fast-moving medical infrastructure repo

• How Claude debugged Argo, TLS conflicts, kubectl patches, and private registry credentials

• How runbooks plus agent memory files capture failures so deployments become reproducible.

It is a practical look at where Kubernetes automation may be heading: less hand-written YAML, more precise intent, and a sharper definition of when the human must stay in the loop.

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/y70mLvWNs

• Interested in sponsoring an episode? Learn more.

A single Kubernetes CRD for every service request turns small changes into full-platform reconciliations.

Alexander Held, former platform engineer at Mercedes-Benz Tech Innovation, describes a production refactor from a 2,000-line CRD to purpose-built resources and controllers. He shows how teams can model business workflows as Kubernetes APIs and then use owner references, finalizers, and events to keep platform operations predictable.

You will learn:

• Why monolithic CRDs create performance and troubleshooting problems

• How controllers turn database provisioning and backups into reconciliation loops

• How finalizers clean up external resources such as S3 backups

• Why Kubernetes events make platform workflows easier to debug

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/TGy4Qn7Qs

• Interested in sponsoring an episode? Learn more.

Kelsey Hightower, Eric Abercrombie, and Julius Payne II reflect on life after achievement, entering the Kubernetes world for the first time, and how music, creativity, and lived experience shape the way they think about technology.

In this interview:

• Why fundamentals, patience, and repetition still matter more than shortcuts

• How Kubernetes, community, and confidence intersect for people entering cloud-native work

• What hip-hop, production, and storytelling can teach us about ownership, authenticity, and finding your voice

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/czrCCXSLt

• Interested in sponsoring an episode? Learn more.

You're running gRPC services in Kubernetes, load balancing looks fine on the dashboard — but some pods are burning at 80% CPU while others sit idle, and adding more replicas only partially helps.

Rohit Agrawal, a Staff Software Engineer on the traffic platform team at Databricks, explains why this happens and how his team replaced Kubernetes's default networking with a proxy-less, client-side load-balancing system built on the xDS protocol.

In this episode:

• Why KubeProxy's Layer 4 routing breaks down under high-throughput gRPC: it picks a backend once per TCP connection, not per request

• How Databricks built an Endpoint Discovery Service (EDS) that watches Kubernetes directly and streams real-time pod metadata to every client

• How zone-aware spillover cut cross-availability-zone costs without sacrificing availability

• Why CPU-based routing failed (monitoring lag creates oscillation) and what signals to use instead

The system has been running in production for three years across hundreds of services, handling millions of requests.

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/y803JMhBk

• Interested in sponsoring an episode? Learn more.

You're integrating HashiCorp Vault into your Kubernetes cluster and adding a temporary debug log line to check whether the ServiceAccount token is being passed correctly. Three months later, that log line is still in production — and the token it prints has a 1-year expiry with no audience restrictions.

Vincent von Büren, a platform engineer at ipt in Switzerland, lived through exactly this incident. In this episode, he breaks down why default Kubernetes ServiceAccount tokens are a quiet security risk hiding in plain sight.

You will learn:

• What's actually inside a Kubernetes ServiceAccount JWT (issuer, subject, audience, and expiry)

• Why tokens with no audience scoping enable replay attacks across internal and external systems

• How Vault's Kubernetes auth method and JWT auth method compare, and when to choose each

• What projected tokens are, why they dramatically reduce blast radius, and what's holding teams back from using them

• Practical steps for auditing which pods actually need API access and disabling auto-mounting everywhere else

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/LTnB_Ntbc

• Interested in sponsoring an episode? Learn more.

Running GPU workloads on Kubernetes sounds straightforward until you need to isolate multiple tenants on the same server. The moment you virtualize GPUs for security, you lose access to NVIDIA kernel drivers — and almost every tool in the ecosystem assumes those drivers exist.

Landon Clipp built a GPU-based Containers as a Service platform from scratch, solving each isolation layer — from kernel separation with Kata Containers + QEMU to NVLink fabric partitioning to network policies with Cilium/eBPF — and shares exactly what broke along the way.

In this interview:

• Why standard NVIDIA tooling (GPU Operator) fails in multi-tenant setups, and how to use CDI with PCI topology scanning to make GPUs visible to Kubernetes without kernel drivers

• How to partition the NVLink fabric between tenants using a trusted service VM running Fabric Manager, and why the physical PCIe wiring differs between Supermicro HGX and NVIDIA DGX systems

• Why gVisor doesn't work for GPU workloads — NVIDIA's unstable ioctl ABI means Google has to update gVisor for every driver release, and they only support a handful of GPUs

• What caused 8-GPU VMs to take 30+ minutes to boot, and the specific fixes (IOMMUFD, cold plugging, kernel upgrades) that brought it down to minutes

• How Cilium network policies enforce tenant isolation at the Kubernetes identity level instead of fragile IP-based rules

Where Containers as a Service fits best: inference workloads where AI teams want to ship an OCI image without managing infrastructure or signing multi-million dollar cluster contracts.

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/jjK_yJTDz

• Interested in sponsoring an episode? Learn more.

Build failures in Kubernetes CI/CD pipelines are a silent productivity killer. Developers spend 45+ minutes scrolling through cryptic logs, often just hitting rerun and hoping for the best.

Ron Matsliah, DevOps engineer at Next Insurance, built an AI-powered assistant that cut build debugging time by 75% — not as a dashboard, but delivered directly in Slack where developers already work.

In this episode:

• Why combining deterministic rules with AI produces better results than letting an LLM guess alone

• How correlating Kubernetes events with build logs catches spot instance terminations that produce misleading errors

• Why integrating into existing workflows and building feedback loops from day one drove adoption

• The prompt engineering lessons learned from testing with real production data instead of synthetic examples

The takeaway: simple rules plus rich context consistently outperform complex AI queries on their own.

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/PDdYfC00w

• Interested in sponsoring an episode? Learn more.

Managed Kubernetes on a major cloud provider can cost hundreds or even thousands of dollars a month — and much of that spending hides behind defaults, minimum resource ratios, and auxiliary services you didn't ask for.

Fernando Duran, founder of SadServers, shares how his GKE Autopilot proof of concept ran close to $1,000/month on a fraction of the CPU of the actual workload and how he cut that to roughly $30/month by moving to Hetzner with Edka as a managed control plane.

In this interview:

• Why Kubernetes hasn't delivered on its original promise of cost savings through bin packing — and what it actually provides instead

• A real cost comparison: $1,000/month on GKE vs. $30/month on Hetzner with Edka for the same nominal capacity

• What you need to bring with you (observability, logging, dashboards) when leaving a fully managed cloud provider

The decision comes down to how tightly coupled you are to cloud-specific services and whether your team can spare the cycles to manage the gaps.

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/6nSDbz9m4

• Interested in sponsoring an episode? Learn more.

Running multiple Kubernetes clusters on AWS with the cluster autoscaler? Every four months, you face the same grind: upgrading Kubernetes versions, recreating auto scaling groups, and hoping instance type changes stick.

Adhi Sutandi, DevOps Engineer at Beekeeper by LumApps, shares how his team migrated from the cluster autoscaler to Karpenter across eight EKS clusters — and the hard lessons they learned along the way.

In this episode:

• Why AWS auto scaling groups are immutable and how that creates upgrade bottlenecks at scale

• How the latest AMI tag accidentally turned less critical clusters into chaos engineering environments, dropping SLOs before anyone realized Karpenter was the cause

• Why pre-stop sleep hooks solved pod restartability problems that Quarkus's built-in graceful shutdown couldn't

• The case for pod disruption budgets over Karpenter annotations when protecting critical workloads during node rotations

• How Karpenter's implicit 10% disruption budget caught the team off guard — and the explicit configuration that fixed it

Sponsor

This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

• Find all the links and info for this episode here: https://ku.bz/XyVfsSQPr

• Interested in sponsoring an episode? Learn more.