When creating production-level applications, enterprises want to ensure the high availability of services. This often results in a lengthy development process that requires extensive testing for the applications or a new release. This involves testing the behavior of the application under load, measuring the performance metrics, and accounting for the resource consumption. All this is done to ensure that the application does not behave unexpectedly when being used by clients.

As we wrap up 2024, we are proud of everything we’ve accomplished together. This year was all about listening to our users, solving real problems, and continuing to build the tools you need to move fast without the headaches of DevOps complexity. From launching new features to expanding partnerships and empowering teams worldwide, 2024 was packed with milestones. None of this would have been possible without you, our amazing community.

Llama 3.3 just arrived, and it’s making waves in the AI community. This new version update takes it a step further, offering better performance, more efficiency, and greater flexibility than the last version. Whether you’re a developer exploring the latest machine learning tools or a business looking to streamline workflows with smarter systems, Llama 3.3 has a lot packed in for you.

Performance tests, end-to-end tests, integration tests. There are many different types of tests you can run on your infrastructure. One of the most overlooked kinds is load testing. Failure to include load tests in your supply chain can be detrimental, as you will be missing out on a number of benefits. Some of the big advantages of load testing Kubernetes are.

The importance of artificial intelligence in driving business success has never been clearer. Yet, for too long advanced AI capabilities have been the preserve of tech giants and well-funded enterprises. This concentration of resources has created inequality that threatens to leave many businesses behind. But the importance of democratizing AI access extends far beyond individual company success - it's about fostering a vibrant, competitive ecosystem where innovations can emerge from unexpected places.

With the promise of auto-provisioning and self-healing, Kubernetes environments can be an attractive option for hosting your application platform. However, with increasing budget restrictions, the competitive cloud providers and offerings, and the need to do more with less, engineers are looking to get a handle on their resource utilization.

Etcd plays a critical role in your Kubernetes setup: it stores the ever-changing state of your cluster and its objects, and the API server uses this data to manage cluster resources. As your applications thrive and your Kubernetes clusters see more traffic, etcd handles an increasing amount of data. But etcd’s storage space is limited: the recommended maximum is 8 GiB, and a large and dynamic cluster can easily generate enough data to reach that limit.

For scaling businesses, transitioning from PaaS (Platform as a Service) to IaaS (Infrastructure as a Service) is less about a choice and more about necessity. Staying on PaaS too long can result in skyrocketing costs, limited flexibility, and performance bottlenecks — challenges that only grow as your workloads and team scale.

At Qovery, we are closely following the development of EKS Auto Mode, a new feature from AWS designed to simplify Kubernetes management by automating various foundational components. While we recognize the effort AWS has put into this, our initial evaluation shows that EKS Auto Mode is still in its early stages and does not yet offer sufficient value to be a strong consideration for our users.