With the Ingress-NGINX retirement scheduled for March 2026, the Kubernetes networking landscape is at a turning point. For most organizations, the question isn't whether to migrate to Gateway API, but how to do so safely. Migrating from Ingress to Gateway API is a fundamental shift in API design. Gateway API provides a modular, extensible API with strong support for Kubernetes-native RBAC. Conversely, the Ingress API is simple, and implementations such as Ingress-NGINX extend the API through esoteric annotations, ConfigMaps, and CRDs. Migrating away from Ingress controllers such as Ingress-NGINX presents the daunting task of capturing all the nuances of the Ingress controller, and mapping that behavior to Gateway API. Ingress2Gateway is an assistant that helps teams confidently move from I

The landscape of artificial intelligence is undergoing a massive architectural shift. In the early days of generative AI, interacting with a model was often treated as a transient, stateless function call: a request that spun up, executed for perhaps 50 milliseconds, and terminated. Today, the world is witnessing AI v2 eating AI v1. The ecosystem is moving from short-lived, isolated tasks to deploying multiple, coordinated AI agents that run constantly. These autonomous agents need to maintain context, use external tools, write and execute code, and communicate with one another over extended periods. As platform engineering teams look for the right infrastructure to host these new AI workloads, one platform stands out as the natural choice: Kubernetes. However, mapping these unique agentic

During production debugging, the fastest route is often broad access such as cluster-admin (a ClusterRole that grants administrator-level access), shared bastions/jump boxes, or long-lived SSH keys. It works in the moment, but it comes with two common problems: auditing becomes difficult, and temporary exceptions have a way of becoming routine. This post offers my recommendations for good practices applicable to existing Kubernetes environments with minimal tooling changes: Least privilege with RBAC Short-lived, identity-bound credentials An SSH-style handshake model for cloud native debugging A good architecture for securing production debugging workflows is to use a just-in-time secure shell gateway (often deployed as an on demand pod in the cluster). It acts as an SSH-style “front door”

Every container image you pull from registry.k8s.io got there through kpromo, the Kubernetes image promoter. It copies images from staging registries to production, signs them with cosign, replicates signatures across more than 20 regional mirrors, and generates SLSA provenance attestations. If this tool breaks, no Kubernetes release ships. Over the past few weeks, we rewrote its core from scratch, deleted 20% of the codebase, made it dramatically faster, and nobody noticed. That was the whole point. A bit of historyThe image promoter started in late 2018 as an internal Google project by Linus Arver. The goal was simple: replace the manual, Googler-gated process of copying container images into k8s.gcr.io with a community-owned, GitOps-based workflow. Push to a staging registry, open a PR

The community around Kubernetes includes a number of Special Interest Groups (SIGs) and Working Groups (WGs) facilitating discussions on important topics between interested contributors. Today, we're excited to announce the formation of the AI Gateway Working Group, a new initiative focused on developing standards and best practices for networking infrastructure that supports AI workloads in Kubernetes environments. What is an AI Gateway?In a Kubernetes context, an AI Gateway refers to network gateway infrastructure (including proxy servers, load-balancers, etc.) that generally implements the Gateway API specification with enhanced capabilities for AI workloads. Rather than defining a distinct product category, AI Gateways describe infrastructure designed to enforce policy on AI traffic, i

As announced November 2025, Kubernetes will retire Ingress-NGINX in March 2026. Despite its widespread usage, Ingress-NGINX is full of surprising defaults and side effects that are probably present in your cluster today. This blog highlights these behaviors so that you can migrate away safely and make a conscious decision about which behaviors to keep. This post also compares Ingress-NGINX with Gateway API and shows you how to preserve Ingress-NGINX behavior in Gateway API. The recurring risk pattern in every section is the same: a seemingly correct translation can still cause outages if it does not consider Ingress-NGINX's quirks. I'm going to assume that you, the reader, have some familiarity with Ingress-NGINX and the Ingress API. Most examples use httpbin as the backend. Also, note tha

This is the fifth interview of a SIG Architecture Spotlight series that covers the different subprojects, and we will be covering SIG Architecture: API Governance. In this SIG Architecture spotlight we talked with Jordan Liggitt, lead of the API Governance sub-project. IntroductionFM: Hello Jordan, thank you for your availability. Tell us a bit about yourself, your role and how you got involved in Kubernetes. JL: My name is Jordan Liggitt. I'm a Christian, husband, father of four, software engineer at Google by day, and amateur musician by stealth. I was born in Texas (and still like to claim it as my point of origin), but I've lived in North Carolina for most of my life. I've been working on Kubernetes since 2014. At that time, I was working on authentication and authorization at Red Hat,

In the standard Kubernetes model, a node’s suitability for workloads hinges on a single binary "Ready" condition. However, in modern Kubernetes environments, nodes require complex infrastructure dependencies—such as network agents, storage drivers, GPU firmware, or custom health checks—to be fully operational before they can reliably host pods. Today, on behalf of the Kubernetes project, I am announcing the Node Readiness Controller. This project introduces a declarative system for managing node taints, extending the readiness guardrails during node bootstrapping beyond standard conditions. By dynamically managing taints based on custom health signals, the controller ensures that workloads are only placed on nodes that met all infrastructure-specific requirements. Why the Node Readiness Co

I'm excited to announce the implementation of an improved conversion formula from cgroup v1 CPU shares to cgroup v2 CPU weight. This enhancement addresses critical issues with CPU priority allocation for Kubernetes workloads when running on systems with cgroup v2. BackgroundKubernetes was originally designed with cgroup v1 in mind, where CPU shares were derived from a container's CPU requests using the following formula: $$cpu.shares = milliCPU \times \frac{1024}{1000}$$Note that the value 1024 in this formula is the default cpu.shares value in cgroup v1, and is unrelated to millicores. For example, a container requesting 1 CPU (1000m) would get (cpu.shares = 1000 \times 1024 / 1000 = 1024), and a container requesting 100m would get (cpu.shares = 100 \times 1024 / 1000 = 102). After a whil

In March 2026, Kubernetes will retire Ingress NGINX, a piece of critical infrastructure for about half of cloud native environments. The retirement of Ingress NGINX was announced for March 2026, after years of public warnings that the project was in dire need of contributors and maintainers. There will be no more releases for bug fixes, security patches, or any updates of any kind after the project is retired. This cannot be ignored, brushed off, or left until the last minute to address. We cannot overstate the severity of this situation or the importance of beginning migration to alternatives like Gateway API or one of the many third-party Ingress controllers immediately. To be abundantly clear: choosing to remain with Ingress NGINX after its retirement leaves you and your users vulnerabl

This document will guide you through setting up a local experimental environment with Gateway API on kind. This setup is designed for learning and testing. It helps you understand Gateway API concepts without production complexity. Caution:This is an experimentation learning setup, and should not be used for production. The components used on this document are not suited for production usage. Once you're ready to deploy Gateway API in a production environment, select an implementation that suits your needs. OverviewIn this guide, you will: Set up a local Kubernetes cluster using kind (Kubernetes in Docker) Deploy cloud-provider-kind, which provides both LoadBalancer Services and a Gateway API controller Create a Gateway and HTTPRoute to route traffic to a demo application Test your Gateway

Cluster API brings declarative management to Kubernetes cluster lifecycle, allowing users and platform teams to define the desired state of clusters and rely on controllers to continuously reconcile toward it. Similar to how you can use StatefulSets or Deployments in Kubernetes to manage a group of Pods, in Cluster API you can use KubeadmControlPlane to manage a set of control plane Machines, or you can use MachineDeployments to manage a group of worker Nodes. The Cluster API v1.12.0 release expands what is possible in Cluster API, reducing friction in common lifecycle operations by introducing in-place updates and chained upgrades. Emphasis on simplicity and usabilityWith v1.12.0, the Cluster API project demonstrates once again that this community is capable of delivering a great amount o