History has a tendency to repeat itself. This is because bad habits and anti-patterns are hard to break. And this remains the case with the latest sought-after engineering unicorn––the “Kubernetes expert”. These days, there is a veritable gold rush to hire the best and brightest Kubernetes wizards. Like all forms of expertise––this gold is rare, and as a result––is also costly. But this isn’t a new phenomenon in the technology world.

Containers are standard, executable units of software in which application code is packed with all the dependencies, libraries, and other necessary elements required for the code to run in any environment quickly and with ease. Containers package the application code with all the necessary dependencies required and can easily be distributed and deployed anywhere, eliminating the need for additional infrastructure requirements.

Kubernetes has become the go-to solution for container orchestration, but managing Kubernetes costs effectively is challenging for businesses aiming to optimize their IT expenditures. Let’s go through various types of costs associated with running Kubernetes and explore some key strategies and practical tips for Kubernetes cost optimization.

Qovery offers a streamlined, powerful platform for developers. Its goal is to simplify DevOps processes and enhance the overall developer experience. For those new to Qovery or considering adopting it, there's a simple way to test its capabilities on your local machine. Using the "qovery demo up" command, you can create a non-production environment to explore Qovery's features without any complicated setups. Here is a step-by-step guide to help you get started.

Whether you're building the next massive AI platform, encoding thousands of images, or a hundreds of other use cases, the ability to quickly create ephemeral tasks can be a game changer for engineers solving complex tasks.

Kubernetes is very straightforward for deploying and managing stateless applications. Consequently, proper backups of the Kubernetes environment have often been postponed or neglected. Today, however, more and more applications running on Kubernetes are stateful, making backup and recovery solutions for these systems critical. Neglecting backup is risky: as applications become more complex and need to preserve their states, the challenges of recovering a setup increase.

As AI and machine learning workloads continue to grow in complexity and size, the need for efficient and scalable infrastructure becomes more important than ever. In this tutorial, I will show you how to deploy AI applications on AWS Elastic Kubernetes Service (EKS) with Karpenter from scratch, leveraging GPU resources for high-performance computing.

On this episode of Densify Talks, we welcome Gokul Naidu, Senior Manager, Cloud Operations for SAP’s SuccessFactors Product Suite. Andrew and Gokul discuss an array of topics in the episode. The general theme of the discussion focuses on cost management and the importance of being prepared, aware, and executing the right planning in order to avoid sticker shock.

Kubernetes annotations are key-value pairs attached to Kubernetes objects, providing a flexible way to extend the functionality of your Kubernetes resources without altering their internal specifications. These annotations serve as a tool to store additional metadata to tailor behavior, orchestrate tools, and interact seamlessly with third-party utilities that complement your Kubernetes environment.

In the last years, Kubernetes has grown tremendously and is considered by most companies to be the best platform to run applications today. In simple words, Kubernetes is an open-source container orchestration platform that allows you to run and manage containerized applications at scale. In this article, I will explain how you can run Kubernetes on AWS in 3 different ways. But before getting down the road, let me explain why it does make sense to run Kubernetes on AWS.