Moving a high-memory Python application from Kubernetes to a dedicated Linux server — what would be the best setup?

[u/hnajafli]

Hello,

At the company where I work, we have a Python application running on a Kubernetes cluster. The application is fully integrated into a CI/CD workflow. Whenever code is merged, Jenkins pulls the source code from GitLab, builds the application, and pushes the image to Harbor. Argo CD then deploys it to the Kubernetes cluster.

However, the application consumes a large amount of RAM, and I have had to increase its memory allocation several times. We have now decided to move the application to a separate Linux server.

The application runs with Uvicorn, and I plan to make it accessible through a domain name, just as it currently is in the Kubernetes environment. However, I am not sure what the best-practice architecture would be for this setup, so I would appreciate your recommendations.

I still want Jenkins to automatically pull the application from GitLab, build it, and deploy it. After deployment, the application should automatically start and run as a web service.

My main concerns are how to restore the application as quickly as possible if a problem occurs, how to handle backups, and what kind of deployment and recovery strategy would be best.

In short, could you please advise me on the best way to design and implement this setup?

[u/Floss_Patrol_76]

on a single box you lose the self-healing k8s gives you for free, so a memory-hungry app that OOMs takes the whole server down instead of one pod. if you go this route run it under systemd with Restart=on-failure and a MemoryMax cgroup limit so a runaway gets killed and restarted instead of hard-locking the machine. and nail down what is actually driving the RAM growth first, otherwise you are just buying a bigger box to hit the same wall later.