Stop me if you’ve heard this one before: you just pushed and deployed your latest change to production, and it’s rolling out to your Kubernetes cluster. You sip your coffee as you wrap up some documentation when a ping in the ops channel catches your eye—a sales engineer is complaining that the demo environment is slow. Probably nothing to worry about, not like your changes had anything to do with that… but, minutes later, more alerts start to fire off.

Kubernetes, often abbreviated as K8s, has revolutionised the way we manage containerized applications. It provides a robust platform for orchestrating and managing containers at scale. One of the key features that sets Kubernetes apart is its powerful metadata system, which includes labels and annotations. In this blog post, we’ll take a comprehensive look at how labels and annotations work in Kubernetes and how you can leverage them to enhance the management of your applications.

Greetings! My name is Matt Krauser and I’m a Strategic Sales Executive here at Cycle. After participating in nearly a hundred demos of the platform, I wanted to step back from my day to day grind and share a little about my experience thus far. My goal in writing this is to compare Cycle and Kubernetes, discuss the primary pain points I hear from prospects and K8s users on a daily basis, and explain how Cycle can alleviate these challenges.

When things go wrong, we’d all love the ability to go back in time, return things to the way they were, and fix whatever issues pop up at the start so they never happen in the first place. This is no different when maintaining complex microservices-based architectures. With any complex system, things are bound to go wrong from time to time.

At Grafana Labs we meet our users where they are. We run our services in every major cloud provider, so they can have what they need, where they need it. But of course, different providers offer different services — and different challenges. When we first landed on AWS in 2022 and began using Amazon Elastic Kubernetes Service (Amazon EKS), we went with Cluster Autoscaler (CA) as our autoscaling tool of choice.