Top Kubernetes Observability Tools and their Usage

There is a wide variety of Kubernetes observability tools available. Using these tools can help you monitor and troubleshoot your applications running on Kubernetes.

Recommended: Observability vs. Monitoring: Similarities, Differences, and Use Cases

In this blog post, we will discuss some of the most popular observability tools for monitoring and troubleshooting an application running on Kubernetes.


Prometheus is an open-source monitoring system and time series database. Prometheus is very easy to set up and has a powerful query language.

The two main components of Prometheus are the Prometheus server that collects metrics from your applications, and the Alertmanager which handles alerts sent by your applications. If you’re using Kubernetes or Swarm, then you’ll need to add Grafana as well.

The major benefits of using Prometheus include:

  • It’s scalable out of the box (with very little configuration). To scale up, all you need to do is increase its storage capacity or increase the number of nodes running it; no changes required for other components like Alertmanager or Grafana!
  • It’s self-healing: if one node fails, an external load balancer will automatically detect this failure and restart the failed instance on another node in your cluster (did we mention that they’re also highly available?). This can be done multiple times until all instances are healthy again — there’s no need for manual intervention!


Jaeger is an open source distributed tracing system for monitoring microservices-based applications.

Jaeger records the transaction data and traces generated by your application and then stores them on disk or in a time series database. It provides an HTTP API to serve these traces, which enables you to visualize them in any modern web browser using Jaeger’s built-in UI or any other compatible tool like Grafana.

In addition, Jaeger supports exporting its data via Open Tracing with JSON/HTTP(S) formats as well as Zipkin compatible BAM format for further analysis and visualization tools like Kibana (Logstash).

Elastic Stack

Elastic Stack is a collection of products and services that can be used to collect, store, analyze and visualize data. It includes the following products: Elasticsearch, Logstash, Kibana (or X-Pack), Beats. Each product has its own use cases.

For example, if you want to analyze your logs or get a better understanding of your infrastructure performance then you would use Kibana or Beats respectively; if you want more insights into your application performance than what can be provided by good old top command in Linux then you should look into using either cAdvisor (for Docker containers) or DataDog/Prometheus (for non-Docker applications).


Kibana is a visualization tool for Elasticsearch. It can be used to investigate and analyze logs collected by Elasticsearch. Kibana can be used to visualize data from other sources as well.

Kibana provides several features like:

  • An easy-to-use interface that makes it simple for users to understand the data and make informed decisions quickly.
  • Advanced search, charting and graphing capabilities to help users explore their data at scale.
  • The ability to share dashboards with others.


Grafana is an open source, feature rich metrics dashboard and graph editor for time series data. Grafana is commonly used as a visualization layer for time series data. It provides many ways to visualize your data like line graphs, area graphs, scatter plots, heat maps and more.

Grafana users have the ability to create their own dashboards from scratch using panels or import them from other sources like kubernetes by using kibana dynamic dashboards In this article we will learn how to use grafana as a monitoring tool for kubernetes cluster

InfluxData’s TICK Stack

The TICK stack is a powerful tool for monitoring Kubernetes. It consists of four components:

Telegraf — collects and sends metrics to InfluxDB, a time-series database. Telegraf can be configured in many ways, including the type of data it collects. For example, you can configure Telegraf to collect CPU usage on your system, or any other metric that might be useful for monitoring your environment.

This includes metrics from applications running on your cluster such as load and performance information for each container running on the host machine. Another example would be container health checks and metrics about resource utilization like memory usage or disk space.

Additionally, you could also use custom plugins which collect custom metrics from your applications using a variety of protocols (e.g., HTTP/s) or third party APIs (e.g., Amazon CloudWatch). You can view what custom plugins are available here .



Director of BI and Analytics

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store