Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has quickly made itself known as the de facto platform for today’s applications and the most common way to build an infrastructure platform for application developers. Kubernetes offers immense flexibility and power, but it can introduce its own unique set of operational challenges. If you find yourself spending more time chasing down cluster issues than helping your developers work hassle-free, this guide is for you.

I was lucky enough to serve on the v1.33 Release Team as Comms Shadow, and it was truly awe-inspiring to see the inner workings of the world’s biggest open-source project. There is a lot to cover around the structure, governance, processes, and maintenance of the Kubernetes project, but in this blog post, I want to focus on the exciting new features that v1.33 brings and what it means for all of us. Check out the official Kubernetes release blog for more details!

Kubernetes autoscalers like Cluster Autoscaler (CAS) and Karpenter have evolved significantly to manage the sprawling Kubernetes ecosystem, which has grown far beyond a simple container orchestration platform to include a vast array of add-ons, operators, CRDs, and third-party integrations. These autoscalers play a crucial role in ensuring K8s workloads get the resources they need, precisely when they need them, without creating excess and waste.

Configuration drift is a silent but persistent challenge in managing Kubernetes environments at scale. Whether you’re running workloads across multiple clusters in on-premises data centers, cloud providers, or edge locations, the risk of drift increases exponentially as environments grow. According to a Komodor survey, 40% of Kubernetes users report that configuration drift negatively impacts the stability of their environments.

When the increasingly popular strategy of configuration as code (CaC) is used to develop infrastructure, it’s known as infrastructure as code (IaC). Today, IaC is quickly becoming entrenched in development processes, especially in conjunction with Terraform and Kubernetes. Yet, although IaC (and CaC) bring immense value, they can also lead to a major problem: configuration drift.

With the growing complexity of cloud-native applications, DevOps teams often face challenges when setting up and maintaining Kubernetes observability. AIOps (artificial intelligence for IT operations) makes the process more manageable using AI and machine learning for monitoring, troubleshooting, and performance optimization. In this article, you’ll learn about the common challenges in Kubernetes observability and how AIOps can provide proactive and effective solutions.

Kubernetes has been facilitating container orchestration for around a decade for both stateful and stateless application workloads. With the recent rise of AI and the advent of tools like Kubeflow and Argo Workflows, Kubernetes is also becoming a first-class citizen when it comes to running AI workloads. When you are training a model on K8s, you may be tweaking many parameters and have to test each of them one by one.

Following all the hype and bluster with DeepSeek’s arrival in the AI landscape––and its ability to crash the poster child of AI’s share value overnight (Nvidia), we wanted to conduct a rigorous evaluation at Komodor. We tested DeepSeek’s models head-to-head against industry leaders in solving real-world Kubernetes challenges.

Recently we’ve explored the evolving role of Kubernetes as a full ecosystem, rather than just a platform, diving into the power and complexity of add-ons. These tools, as highlighted previously, are key to augmenting Kubernetes core capabilities, and adding-on (as their name implies) essential capabilities not supported directly by Kubernetes itself.

Managing DNS records in Kubernetes at scale is complex, especially as clusters grow and the number of applications increases. Enter ExternalDNS—a tool designed to automate DNS record synchronization with Kubernetes resources, providing the agility and scalability needed for modern application environments.