Kubernetes Network Observability: Comparing Calico, Cilium, Retina, and Netobserv

Feature Comparison Matrix Understanding Flow Types Aggregated Flows Individual Flows Calico Observability Stack How it works Key Data Types Cilium Observability Stack How it Works Key Data Types Microsoft Retina How it Works Key Data Types Netobserv (Red Hat) How it Works Key Data Types Conclusion The 3 Rules of Kubernetes Network Observability 1. The Native Stack Rule 2. The Cloud Pragmatist Rule 3. The Red Hat Rule Take the Next Step Calico, Cilium, Retina, and Netobserv: Which Observability Tool is Right for Your Kubernetes Cluster? Network observability is a tale as old as the OSI model itself and anyone who has managed a network or even a Kubernetes cluster knows the feeling: a service suddenly can’t reach its dependency, a pod is mysteriously offline, and the Slack alerts start rolling in. Investigating network connectivity issues in these complex, distributed environments can be incredibly time consuming. Without the right tools, the debugging process often involves manually connecting to each node, running tcpdump on multiple machines, and piecing together logs to find the root cause. A path that often leads to frustration and extended downtime. tcpdump This is the problem that Kubernetes Network Observability was built to solve. By deploying distributed observers, these cloud-native solutions take the traditional flow entries and enrich them with Kubernetes flags and labels to allow Kubernetes users to get insight into the inner workings of their clusters. This blog post aims to give you a rundown of the leading solutions in the CNCF ecosystem, and compare how they track a packet’s journey across your cluster. Before diving into the specifics, let’s look at how these four major players ( Calico , Cilium , Microsoft Retina , and Netobserv) stack up against one another. * Microsoft Retina has a couple of modes, one of these modes offers a smaller set of features but allows you to use Hubble as its UI.