Headscale is an open source, self-hosted control server compatible with the Tailscale clients. It lets you run your own Tailnet and have full control over users, nodes, keys, and routing policies without relying on Tailscale’s hosted control plane. This post introduces the tailscale-exporter and shows how to collect Headscale metrics via the Headscale gRPC API, and visualize everything in Grafana using dashboards and alerts bundled in the mixin.

Envoy is a popular open source edge and service proxy that's widely used in modern cloud-native architectures. Envoy gateway is a controller that manages Envoy proxies in a Kubernetes environment. Monitoring Envoy and Envoy gateway is crucial for ensuring the reliability and performance of your applications. In this blog post, we'll explore how to monitor Envoy and Envoy gateway using Prometheus and Grafana and we'll also introduce a new monitoring-mixin for Envoy.

With the retirement of ingress-nginx, many users are looking for alternatives for ingress controllers. Envoy gateway is a great option for those who want to leverage the power of Envoy in their Kubernetes clusters. I recently migrated from ingress-nginx and you can read more about it here.

Syncthing is a popular open-source file synchronization tool that allows users to securely sync files across multiple devices. Monitoring Syncthing is crucial to ensure that your files are being synchronized correctly and to identify any potential issues. In this guide, we'll explore how to monitor Syncthing using Prometheus and Grafana.

Tailscale is a popular VPN solution that allows you to create secure, encrypted connections between devices. It is based on the WireGuard protocol and is designed to be easy to use and configure. Recently, I've started using Tailscale more extensively both in my personal projects and at work. As a result, I wanted to visualize my Tailnet in Grafana to get better insights into its performance and usage. This post introduces the tailscale-exporter, a tool I built to collect Tailnet metrics directly from the Tailscale API. I’ll also show how to enable scraping of Tailscale client metrics and visualize everything in Grafana for complete observability across your Tailnet.

Cluster autoscaler is a popular tool for automatically adjusting the size of a Kubernetes cluster based on the current workload. It helps ensure that your applications have enough resources to run efficiently while minimizing costs by scaling down unused nodes. However, monitoring the cluster autoscaler is crucial to ensure that it is functioning correctly and that your applications are running smoothly.

KEDA is a tool that provides event-driven autoscaling for Kubernetes, allowing you to scale your applications based on external metrics. It uses the Kubernetes Horizontal Pod Autoscaler (HPA) to adjust the number of pods in a deployment based on metrics like CPU usage, memory usage, or custom metrics from external sources. It also supports scaling based on event sources like message queues, databases as a job and defines a new Custom Resource Definition (CRD) called ScaledJob to configure the scaling behavior. Monitoring KEDA effectively is crucial to ensure that your autoscaling policies are working as expected and that your applications are performing optimally.

Not long ago, I shared how to monitor Kubernetes costs using OpenCost, Prometheus, and Grafana. In this follow-up, I want to highlight a specific feature: setting up proactive budget alerts using OpenCost and Prometheus. It's a simple but powerful way to catch cost overruns early.

In modern DevOps workflows, GitOps has emerged as a powerful model for managing infrastructure and application deployments using Git as the single source of truth. One common challenge in GitOps is how to promote changes across environments - from staging to production - while maintaining traceability, automation, and control. In this post, we’ll explore how to implement environment promotions using GitHub Releases and Semantic Versioning (SemVer) to streamline delivery and improve reliability.

This approach is tailored for small and mid-sized teams that want to implement reliable, controlled GitOps promotions without the overhead of enterprise-scale CI/CD systems.

With the release of Karpenter v1 we have stable Prometheus metrics, but the Grafana dashboards are not that great and there are no open source alerts. Therefore, I decided to create a monitoring-mixin that provides a set of Prometheus rules and Grafana dashboards for Karpenter. This blog post will introduce the kubernetes-autoscaling-mixin - a set of Prometheus rules and Grafana dashboards for Kubernetes autoscaling, but we will only write about Karpenter monitoring in this blog post.

The kube-prometheus-stack Helm chart, which deploys the kubernetes-mixin, is designed for standard Kubernetes setups, often pre-configured for specific cloud environments. However, these configurations are not directly compatible with k3s, a lightweight Kubernetes distribution. Since k3s lacks many of the default cloud integrations, issues arise, such as missing metrics, broken graphs, and unavailable endpoints (example issue). This blog post will guide you through adapting the kube-prometheus-stack Helm chart and the kubernetes-mixin to work seamlessly in k3s environments, ensuring functional dashboards and alerts tailored to k3s.