KubeFM

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

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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.