Recently, I’ve had a number of folks ask for some more details about how SimKube can be used to predict or forecast your Kubernetes expenditures, and I realized that I’ve said you can do this several times, but I’ve never actually gone through the details! So this post will show you how.

Kanidm is a simple and secure identity management platform, allowing other applications and services to offload the challenge of authenticating and storing identities to Kanidm.

The goal of this project is to be a complete identity provider, covering the broadest possible set of requirements and integrations. You should not need any other components (like Keycloak) when you use Kanidm - we already have everything you need!

To achieve this we rely heavily on strict defaults, simple configuration, and self-healing components. This allows Kanidm to support small home labs, families, small businesses, and all the way to the largest enterprise needs.

If you want to host your own authentication service, then Kanidm is for you!

Declarative node network configuration driven through Kubernetes API.

OpenObserve Kide is a lightweight and fast Kubernetes IDE.

zot: a production-ready vendor-neutral OCI image registry - images stored in OCI image format, distribution specification on-the-wire, that's it!

JuiceFS is a high-performance POSIX file system released under Apache License 2.0, particularly designed for the cloud-native environment. The data, stored via JuiceFS, will be persisted in Object Storage (e.g. Amazon S3), and the corresponding metadata can be persisted in various compatible database engines such as Redis, MySQL, and TiKV based on the scenarios and requirements.

With JuiceFS, massive cloud storage can be directly connected to big data, machine learning, artificial intelligence, and various application platforms in production environments. Without modifying code, the massive cloud storage can be used as efficiently as local storage.

A set of Grafana dashboards and Prometheus alerts for Kubernetes Autoscaling using the metrics from Kube-state-metrics, Karpenter, and Cluster-autoscaler.

Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting.