An Internal Developer Platform (IDP) is a powerful tool for providing self-service infrastructure for developers. It allows developers to manage application lifecycles without the constant need for IT team involvement, streamlining the process from development to production. With its widespread adoption, Kubernetes has become the standard for running application workloads across various organizations.

Qovery is an Internal Developer Platform (IDP) designed to make Kubernetes developer-friendly. It offers self-service capabilities, allowing developers to efficiently manage and scale their applications. One of the standout features of Qovery is the ability to create Ephemeral Environments, which we will focus on in this guide. So keep reading to see how you can get a fully operational Ephemeral Environments system on your Kubernetes Cluster in less than 10 minutes.

Managing multiple Kubernetes clusters presents a significant challenge, primarily due to operational overheads, complexity, and the steep learning curve associated with Kubernetes' complex ecosystem. As organizations scale and deploy across various environments; production, staging, and development, the need for a robust tool to streamline management becomes crucial.

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.

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.

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.

Ephemeral environments are temporary, isolated, but self-contained deployment environments crucial for development and testing within software projects. While I already discussed the basics and benefits of Ephemeral Environments, today I will go through the practical steps of implementing ephemeral environments in your CI/CD pipeline using Kubernetes. I will start with how you can do it with Kubernetes native tools, and how you can automate the ephemeral environments in your CI/CD.

The motivation behind automating Kubernetes deployments stems from the need to accelerate development cycles, minimize manual errors, and achieve consistent deployments. In today’s application development where container orchestration is a crucial aspect, automating the Kubernetes deployment is inevitable. Despite challenges such as maintaining security and handling complex dependencies, automation remains a powerful solution.