22 posts tagged with "General"

As organizations increasingly move AI workloads into production, they need consistent and interoperable infrastructure they can rely on. The Cloud Native Computing Foundation (CNCF) launched the Kubernetes AI Conformance Program to address this need by creating open, community-defined standards for running AI workloads on Kubernetes. See CNCF Kubernetes AI Conformance Announcement at KubeCon North America 2025.

Azure Kubernetes Service (AKS) is proud to be among the first platforms certified for Kubernetes AI Conformance, demonstrating our commitment to providing customers with a verified, standardized platform for running AI workloads.

We're pleased to share the newly updated guidance on deploying PostgreSQL with CloudNativePG on Azure Kubernetes Service (AKS).

What if you need to collect your own custom metrics from workloads or nodes in AKS, but don't want to run a full monitoring stack? In this post, we will discuss how to integrate custom metrics into Azure's managed monitoring stack with minimal setup using Telegraf DaemonSet, for flexible metric collection, Azure Monitor managed service for Prometheus, for scraping and storage, and Azure Managed Grafana for visualization and alerting.

Introduction​

Last year we announced the general availability of Azure Container Storage, the industry’s first platform-managed container native storage service in the public cloud. This solution delivers high performance and scalable storage that can effectively meet the demands of containerized environments. Today we are announcing a new v2.0.0 release of Azure Container Storage for Azure Kubernetes Service (AKS). It builds on the foundation of previous release and takes it further by focusing on higher performance, lower latency, efficient resource management and a Kubernetes native user experience for managing stateful workloads on AKS.

DNS performance issues can cripple production Kubernetes clusters, causing application timeouts and service outages. LocalDNS in AKS solves this by moving DNS resolution directly to each node, delivering 10x faster queries and improved reliability. In this post, we share the results from our internal tests showing exactly how much of an improvement LocalDNS can make and how it can benefit your cluster.

What are Kubernetes Extensions?​

Kubernetes extensions (or cluster extensions) are pre-packaged applications that simplify the installation and lifecycle management of Azure capabilities on Kubernetes clusters. Examples include Azure Backup, GitOps (Flux), and Azure Machine Learning. Third-party extensions (or Kubernetes apps), such as Datadog AKS Cluster Extension and Isovalent Cilium Enterprise, are also available in the Azure Marketplace.

In London at KubeCon EU 2025, AKS announced our expansion of what AKS Long Term Support (LTS) includes. Today, we're sharing more details about this offering that addresses one of the most critical challenges enterprises face when running Kubernetes at scale.

PostgreSQL is one of the most popular stateful workloads on Azure Kubernetes Service (AKS). Thanks to the support of a vibrant community, we now have a strong PostgreSQL operator ecosystem that makes it easier for everyone to self-host PostgreSQL on Kubernetes.

Helping you deploy on AKS​

Building on our initial announcement for Deploying Open Source Software on Azure Azure is excited to announce we have expanded our library of technical best practice deployment guides for stateful workloads on AKS. We have developed a comprehensive guide for deploying Kafka on AKS, and updated our Postgres guidance with additional storage considerations for data resiliency, performance and cost. We have also added Terraform templates to our Mongo DB and Valkey guides for automated deployments.

While AKS does not provide access to the cluster's managed control plane, it does provide access to the control plane component logs via diagnostic settings. The easiest option to persist and search this data is to send it directly to Azure Log Analytics, however there is a large amount of data in those logs, which makes it cost prohibitive in Log Analytics. Alternatively, you can send all the data to an Azure Storage Account, but then searching and alerting can be challenging.

To address the above challenge, one option is to stream the data to Azure Event Hub, which then gives you the option to use Azure Stream Analytics to filter out events that you deem important and then just store the rest in cheaper storage (ex. Azure Storage) for potential future diagnostic needs.

In this walkthrough we'll create an AKS cluster, enable diagnostic logging to Azure Stream Analytics and then demonstrate how to filter out some key records.

With the fast-paced advancement of AI workloads, building and fine-tuning of multi-modal models, and extensive batch data processing jobs, more and more enterprises are leaning into Kubernetes platforms to take advantage of its ability to scale and optimize compute resources. With AKS, you can manage up to 5,000 nodes (upstream K8s limit) in a single cluster under optimal conditions, but for some large enterprises, that might not be enough.

At Azure Kubernetes Service (AKS), we deeply recognize how crucial observability is for running stable Kubernetes environments. Given our extensive reliance on Kubernetes internally, we're continually innovating to ensure you have robust, clear, and actionable insights into your cluster health and performance. Observability—the ability to monitor, understand, and manage your systems effectively—is a foundational pillar for AKS product vision to enable our users to achieve more.

Azure Kubernetes Service (AKS) is a highly managed platform that simplifies deploying, managing, and scaling containerized applications using Kubernetes on Azure. When paired with Bitnami's open-source solutions available on Azure Marketplace, AKS becomes an even more powerful platform for accelerating the deployment of Kubernetes workloads that rely on popular open-source projects.

In the ever-evolving world of cloud native technologies, managing multiple Kubernetes clusters efficiently is a common challenge that still does not have a well received community driven solution.

We're pleased to share new guidance on deploying open-source Apache Airflow on Azure Kubernetes Service (AKS).

As we start 2025, we are thrilled to announce a new initiative from the Azure Kubernetes Service (AKS) Product Group: The AKS Community Calls. These monthly sessions are designed to foster a closer connection with our community, providing a platform to discuss the product roadmap and address your questions directly

Welcome back to our series on migrating Amazon Elastic Kubernetes Service (EKS) workloads to Azure Kubernetes Service (AKS). In Part 1 we explored migrating and Event Driven Workload using Karpenter and KEDA from EKS to AKS. Next, we look into a more complex migration scenario with a common Kubernetes workload the n-tier web application.

Developing applications for Kubernetes can mean a lot of time spent waiting and relatively little time spent writing code. Whenever you want to test your code changes in the cluster, you usually have to build your application, deploy it to the cluster, and attach a remote debugger (or add a bunch of logs). These iterations can be incredibly time-consuming. Thankfully, there is a way to bridge the gap between your local environment and a remote cluster, making them feel seamlessly connected. mirrord, which can be used as a plugin for VSCode or IntelliJ or directly in the CLI, is an open-source tool that does exactly that (and much more).

Many companies use multiple clouds for their workloads. Some of these companies need to accommodate the cloud preferences of their customers. Kubernetes plays a central role in multi-cloud workloads due to its ability to provide a consistent and portable environment across different cloud providers.

We would like to share the first in a new documentation series designed specifically for customers already using Amazon EKS, to help them replicate or migrate their workloads to AKS: Replicate an AWS event-driven workflow (EDW) workload with KEDA and Karpenter in Azure Kubernetes Service (AKS)

Even with Kubernetes’ portable API, moving between clouds can be challenging. Each cloud has its own unique concepts, behaviors, and characteristics that will seem unfamiliar when you’re accustomed to another cloud’s approach. This is not unlike the experience of learning a new language or visiting a new country for the first time. This series will be your local guide to the world of Azure. The samples in this series begin with infrastructure and code on EKS and end with equivalently functional infrastructure and code on AKS, while explaining the conceptual differences between AWS and Azure throughout.

Last May, we announced the preview of Azure Container Storage with backing storage options including Ephemeral Disks, Azure Disks, and Azure Elastic SAN. Earlier today we announced the general availability of Azure Container Storage, the industry’s first platform-managed container native storage service in the public cloud providing highly scalable storage that can keep up with the demands of a containerized environment. With this announcement, Azure Disks and Ephemeral Disks are now generally available, while Azure Elastic SAN remains in preview and is expected to reach general availability soon. In this post, we'll explore the benefits of Azure Container Storage, the inspirations behind its development, and the new features in the GA release.

You may have heard about AKS Automatic in the Build keynote today. We thought we'd share a bit of the thinking that went into it and why we think it can be a game changer for you.

Automatic is a new experience for Azure Kubernetes Service (AKS) that lets you create and manage production-ready clusters with minimal effort and added confidence. This means you can focus on developing and running your applications, while AKS handles the rest for you.

Hi! My name is Jorge Palma, I’m a PM Lead for AKS and I’m excited to be inaugurating our new AKS Engineering Blog. In this new blog, we will complement and extend some of our existing channels, providing extra context to announcements, sharing product tips & tricks that may not fit in our core documentation, and giving you a peak behind the curtain of how we build the product.

In this initial post, taking inspiration from Mark Russinovich who has named so many similar series and talks like this, I hope to take you on a (shortened) journey through the history of Azure Kubernetes Service. We will talk about the past, how we started and how we got here, where we are today and also some thoughts about what the future holds for AKS.