In an ideal world, you want to have precisely the capacity to manage the requests of your users, from peak periods to off-peak hours. If you need three servers to attend to all the requests at peak periods and just one server at off-peak hours, running three servers all the time is going to drive up expenses, and running just one server all the time is going to mean that during peak periods, your systems will be overwhelmed and some clients will be denied service.

Have you ever experienced the problem where your code is broken in production, but everything runs correctly in your dev environment? This can be really challenging because you have limited information once something is in production, and you can't easily make changes and try different code. Speedscale production data simulation lets you securely capture the production application traffic, normalize the data, and replay it directly in your dev environment. There are a lot of challenges with trying to replicate the production environment in non-prod.

When building cloud-based applications, managing the infrastructure becomes a bigger challenge as you scale. Kubernetes brings order to the chaos, letting you control and automate the containers used to deploy your application. Debugging in the cloud presents further challenges, and the complexities of distributed applications make it hard for many debugging setups to keep pace. Tools designed to run locally aren't effective. However, there are Kubernetes debugging tools that can handle the shift in paradigm. In this article, you'll read about several options that make debugging Kubernetes applications much easier.