In this episode, recorded live at KubeCon, Ronald and Jan talk with Jannis Relakis and Michael Seiwald-McCarty, both senior platform engineers at Celonis. Celonis manages over 150 Kubernetes clusters across GKE, AKS, and EKS, but it wasn't always that clean. They started with six different Kubernetes flavors, including Gardener, K-Ops, and OpenShift (both Rosa and ARO), spread across multiple cloud providers.

In their KubeCon talk "No Shame in Just Paying," they shared how they tackled this consolidation project: migrating all workloads to three standardized, fully managed Kubernetes distributions. Key topics include their self-built cross-cluster connectivity tool called "Wormhole" (powered by Envoy's dynamic forward proxy), RabbitMQ federation for seamless message queue migration, and how they used Karpenter and Cilium to align node and network management across clouds.

They also get candid about what went wrong: an accidental ArgoCD sync that caused a 10-minute full environment outage, the pain of "snowflake" environments (including one requiring full HIPAA compliance with Istio mTLS), and the constant fight against scope creep that threatened to derail the entire project.

The episode closes with a forward-looking discussion on FinOps, resource rightsizing, the future of VPA, and whether Kubernetes and serverless can ever truly converge.Powered by ACC ICT

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In this episode, Ronald and Jan sit down with Luigi Nardi, founder and CEO of DB tune, at KubeCon. Luigi brings a rare mix of academic depth (PhD in computer science, postdocs at Imperial College London and Stanford, professor at Lund University) and startup pragmatism. The conversation digs into why database tuning is fundamentally a combinatorial optimization problem that humans aren't wired to solve well, and why AI is uniquely suited for it.

DB tune focuses entirely on Postgres and deploys a narrow, production-safe AI agent that reads performance metrics and iteratively adjusts server parameters (GUCs) until the system converges on an optimal configuration. No LLMs, no hallucinations — just purpose-built ML that operates in a closed feedback loop. The agent integrates with AWS RDS, Aurora, Azure Flexible Server, Google Cloud SQL, and Cloud Native PG (the Kubernetes Postgres operator).

Luigi shares a standout story: a water management company ran the agent on their production system — with a hospital's water supply on the line — and achieved a 2.5x performance improvement in just a few hours. He also explains how tuning isn't a one-time exercise: cloud workloads change, hardware scales up and down, and DB tune's model called "Newton" was specifically engineered to prevent unstable, oscillating parameter changes. The episode closes with a compelling FinOps angle: tuning doesn't just make your database faster, it can also shrink your instance size and cut infrastructure costs — a perfect fit for the Kubernetes-native world.

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In this episode, hosts Ronald and Jan are joined at KubeCon by two guests from Red Hat: Brian Stevens, AI CTO and one of the original architects behind the creation of Kubernetes and the CNCF, and Rob Shaw, co-lead of the vLLM project and maintainer of LMD.

Brian shares the remarkable backstory of how Kubernetes came to be open source, including how Red Hat negotiated a single committer seat before agreeing to be a launch partner, and how he later pushed Google to contribute Kubernetes to the newly formed CNCF rather than keeping it proprietary like TensorFlow.

Rob explains what an inference runtime actually is: the critical piece of software that takes an abstract AI model and runs it as efficiently as possible on a GPU or other accelerator — handling everything from CUDA-level kernel optimization to memory management and concurrent request scheduling. vLLM serves as a "Rosetta Stone" between the ever-growing zoo of models (Llama, DeepSeek, Mistral, Qwen, Nvidia Nemotron) and accelerators (Nvidia, AMD, Intel, Google TPUs).

The conversation covers model compression and quantization how techniques like 4-bit precision can deliver 2x hardware efficiency gains while preserving 99%+ model accuracy. Brian and Rob also address the "big model vs. many small models" debate, recommending to always start with the largest capable model to validate a use case before optimizing down.

Looking ahead, both guests see inference as potentially the single largest workload ever run on Kubernetes, and position LMD (now contributed to the CNCF) as the distributed inference layer that will make this possible across heterogeneous accelerator environments preventing enterprises from ending up with 42 incompatible AI stacks.
The episode closes with a discussion on AI slop, human-in-the-loop thinking, and the future of Kubernetes as the universal platform for running AI agents at scale.

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In Episode 135 of the Dutch Kubernetes Podcast, Ronald Kers and Jan Stomphorst sit down with Mohamed Nasser, CEO of VEXXHOST and OpenInfra Foundation board member, together with Thierry Carrez, General Manager of the OpenInfra Foundation and Linux Foundation Europe. The conversation explores the growing relevance of OpenStack in a world increasingly focused on digital sovereignty, private cloud, AI workloads, and secure infrastructure.

The episode dives into how the industry shifted from private infrastructure toward hyperscalers between 2016 and 2020, and why many organizations are now reconsidering that strategy. Thierry explains how geopolitical tensions, vendor lock-in, and changing licensing models have renewed interest in sovereign cloud solutions powered by open source technologies like OpenStack.

Mohamed and Thierry discuss why OpenStack is still highly relevant at massive scale, especially for organizations requiring multi-tenancy, hardware abstraction, GPU enablement, HPC workloads, and advanced networking performance. They explain how Kubernetes has become the user-facing interface, while OpenStack increasingly operates invisibly underneath many modern platforms. Examples discussed include rail infrastructure, gaming companies, telecom providers, and even government environments.

The discussion also explores how Kubernetes and OpenStack complement each other instead of competing. Mohamed explains how many providers now run OpenStack itself on Kubernetes, leveraging cloud-native tooling such as Prometheus and Loki to simplify operations and observability. The hosts also discuss storage abstraction, CSI drivers, bare-metal provisioning with Ironic, and why virtualization still offers major operational advantages in large-scale Kubernetes environments.

Towards the end of the episode, the conversation shifts toward the future of open infrastructure, including confidential computing, Kata Containers, AI security, GPU orchestration, and the growing collaboration between the Linux Foundation, CNCF, and OpenInfra Foundation. Thierry highlights how secure container isolation and confidential computing are becoming increasingly important as AI workloads spread across Kubernetes platforms.

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In episode 134, Ronald and Jan reconnect with Carl Moberg from Avassa, live from KubeCon Amsterdam, nearly two years after their first spontaneous conversation about edge computing.

Since that first meeting, the edge landscape has evolved rapidly. What was once a niche topic has now become a serious focus area for industries ranging from retail and manufacturing to telecom and AI-driven infrastructure.

Carl shares how organizations are increasingly moving workloads closer to where data is created, whether that is inside factories, retail stores, industrial environments or remote edge locations. The discussion explores the differences between IoT, edge and far edge computing, and why these environments introduce unique operational and security challenges.

A major theme throughout the episode is the role of Kubernetes at the edge. While Kubernetes remains a powerful platform, Carl explains why it is not always the most practical solution for highly distributed or resource-constrained environments. The real challenge is often not starting containers, but everything around them: observability, secrets management, lifecycle management, networking, upgrades and reliability at scale.

The conversation also connects naturally to the previous episode with Neil Cresswell from Portainer. Both episodes explore the same core question from different perspectives:

How do you reliably run modern applications across thousands of edge locations?

An in-depth discussion about containers, operational complexity, AI at the edge, industrial automation and the future of distributed application platforms.

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In this episode, Ronald and Jan talk with Neil Cresswell, CEO, CTO, and co-founder of Portainer. Neil shares how Portainer started as a simple Docker UI and evolved into a platform for managing Docker, Podman, and Kubernetes environments at scale.

A key topic in this episode is Kube Solo, a lightweight Kubernetes distribution that can run on roughly 200 MB of RAM. The goal is to make Kubernetes usable in environments where traditional clusters are too heavy, such as IoT, edge, and far edge use cases. Think of AI-powered cameras, self-checkout systems, Nvidia Jetsons, and even tractors running intelligent workloads.

Neil explains that Kubernetes itself isn’t getting simpler, but it does need to become more intuitive. Portainer aims to make Kubernetes accessible to IT generalists, reducing the need for highly specialized teams while still leveraging the full power of the ecosystem.

This episode explores simplicity, scalability, edge computing, and what it takes to bring Kubernetes everywhere.

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In this episode, Ronald and Jan are joined by Nigel Douglas, Head of Developer Relations at Cloudsmith, to discuss the upcoming Kubernetes 1.36 release and the broader evolution of the Kubernetes ecosystem.

Nigel shares his journey from help desk and cybersecurity roles into open source, eventually working closely with Kubernetes through projects like Calico and Falco within the CNCF ecosystem.

The conversation centers around Kubernetes 1.36, which marks a shift from foundational features toward optimization and new use cases. A major theme in this release is the growing importance of AI workloads. Kubernetes is increasingly positioned as the orchestration platform for AI, with features like Dynamic Resource Allocation (DRA) enabling better management of GPUs and specialized hardware.

Security is another dominant theme. Many of the changes in this release focus on closing gaps and improving control, such as more fine-grained authorization, better admission control during node startup, and addressing previously existing vulnerabilities.

Additionally, the episode highlights several practical improvements, including better snapshot capabilities for stateful workloads, enhanced observability features like native histograms, and improvements in workload scheduling that take hardware topology into account.

The discussion also touches on a common challenge in the Kubernetes world: upgrading. Many organizations still run older versions due to the complexity of dependencies and ecosystem changes, making transitions non-trivial.

Looking ahead, Nigel emphasizes the need for more standardization within Kubernetes to make it easier for organizations to adapt when components change or become deprecated, reinforcing the importance of a stable and predictable ecosystem.

‍

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In this episode, Ronald and Jan talk with Zahra Dehghanpour (platform engineer at bol.com) and Feike Wierda (Senior DevOps Engineer @Bol. via HCS Company) about software supply chain security in Kubernetes environments.

Zahra shares her journey from development to platform engineering, driven by the constant challenges and unpredictability of working with infrastructure. Her earlier experience working in Iran, where infrastructure had to be built and maintained under constraints, shaped her approach to designing resilient and fault-tolerant systems.

Feike explains that software supply chain security covers everything that touches your software, from dependencies and tooling to people and processes. At bol.com, this is addressed by standardizing pipelines, controlling dependencies through internal repositories, and applying security scanning early in the process.

A key theme is balance: developers need freedom, but within secure guardrails. That’s why pipelines are not immediately blocked on vulnerabilities, but first used to provide visibility and gradually increase maturity.

The episode also highlights that security is never “done.” It’s an ongoing process where automation, better tooling, and AI will play an increasingly important role, especially in areas like code review and vulnerability management.

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