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Karpenter can reduce Kubernetes infrastructure costs, but aggressive node consolidation can also expose workloads that lack disruption safeguards.
Ahmad Asmar explains how Zencity uses Kyverno to automatically generate Pod Disruption Budgets, while accounting for existing PDBs, percentage-based availability targets, single-replica workloads, and environment-specific policies.
In this interview:
How Karpenter consolidation changes the availability risks of cluster operations

Why Kyverno's generated policies can provide safer defaults than manual enforcement

How to handle duplicate PDBs, scaling workloads, and single-replica edge cases

How aggregated ClusterRoles keep custom permissions separate from Helm-managed resources

Sponsor
This episode is sponsored by LearnKube. Download the free book, The Technical Guide to Kubernetes Rightsizing, to understand what Prometheus and Grafana cannot tell you about safely reducing requests and limits.
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Find all the links and info for this episode here: https://ku.bz/xrlPJg54D

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An unmaintained identity component can remain invisible until a routine Kubernetes upgrade turns it into an incident.
Fabián Sellés Rosa, Platform Engineer and Runtime Tech Lead at Adevinta, explains how his team moved from KIAM to EKS Pod Identities without discarding the security boundaries and application interface that their internal platform depended on.
In this interview:
Why KIAM became urgent to replace after years of stable operation

How Crossplane, a custom controller, and KRO with ACK compared against the team's criteria

Why managed EKS Capabilities reduced toil but introduced observability and rollout trade-offs

How Kyverno preserved namespace-level authorization for IAM roles

Sponsor
This episode is sponsored by LearnKube. Download the free book, The Technical Guide to Kubernetes Rightsizing, to understand what Prometheus and Grafana cannot tell you about safely reducing requests and limits.
More info
Find all the links and info for this episode here: https://ku.bz/R_06hwnCn

Interested in sponsoring an episode? Learn more.

GPU inference throughput depends on more than accelerator generation or count.
Memory bandwidth, model parallelism, cache configuration, and the load generator itself all influence measured throughput.
Federico Iezzi, Customer Engineer at Google Cloud, explains how his team achieved 1 million output tokens per second using Qwen 3.5 27B, vLLM, GKE Autopilot, and NVIDIA B200 GPUs.
The discussion covers:
Why memory bandwidth limits decode performance

How Federico chose between tensor and data parallelism

What changed after enabling multi-token prediction and reducing the KV cache footprint with FP8 quantization.

Sponsor
This episode is sponsored by LearnKube. Download the free book, The Technical Guide to Kubernetes Rightsizing, to understand what Prometheus and Grafana cannot tell you about safely reducing requests and limits.
More info
Find all the links and info for this episode here: https://ku.bz/1xD9Md0mb

Interested in sponsoring an episode? Learn more.

Forced platform migrations are usually treated as something to survive. At Scout24, a mandatory OS migration became an opportunity to rethink Kubernetes autoscaling, node provisioning, and infrastructure efficiency.
John Ford explains how Scout24 moved its EKS-based Infinity platform from a polling autoscaler and over-provisioned capacity to Karpenter and Bottlerocket. The result was faster node startup, a safer migration path, and about a 30% infrastructure reduction without major downtime.
In this interview:
Why two-minute node provisioning forced a 25% capacity buffer

How Karpenter made the Bottlerocket migration safer

What broke around EC2 metadata, AWS SDKs, and cgroups

How the new foundation enables Spot, ARM, and GPU workloads

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/DdmVC2_7v

Interested in sponsoring an episode? Learn more.

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

Interested in sponsoring an episode? Learn more.