EKS Monitoring: Tools, Metrics & Best Practices

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What Is AWS EKS? 

Amazon Web Services Elastic Kubernetes Service (AWS EKS) is a managed service that lets you run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane or nodes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.

AWS EKS runs the Kubernetes control plane for you across multiple AWS availability zones to ensure high availability. It also automates key tasks such as patching, node provisioning, and updates. Because EKS is often used to run mission-critical production workloads, it is crucial to monitor EKS clusters and the applications they run, identify operational issues, and respond to them.

Learn more in our detailed guide to AWS EKS architecture 

The Importance of Monitoring AWS EKS 

Here are the primary reasons you must have robust monitoring in place for your EKS clusters.


Monitoring AWS EKS can aid in troubleshooting issues that arise in your environment. By keeping an eye on key metrics, you can identify anomalies, spot trends, and diagnose problems. Monitoring can alert you to issues like high CPU usage, memory pressure, and network congestion, enabling you to take corrective action quickly. Furthermore, having historical data at your fingertips can help you understand the root cause of problems and prevent them from reoccurring.

Performance Optimization

By tracking metrics like CPU utilization, memory usage, and network traffic, you can identify resource bottlenecks and improve the efficiency of nodes, applications, and clusters. With real-time visibility into your environment, you can make data-driven decisions to optimize your resource allocation and improve the performance of your workloads on AWS EKS.

Capacity Planning and Scalability

By tracking the growth of your workloads over time, you can predict future resource needs and plan accordingly. AWS EKS supports auto-scaling, which automatically adjusts the number of nodes in your cluster based on the demand. However, to make the most of this feature, you need to monitor your usage patterns and configure your auto-scaling policies appropriately.

Security and Compliance

By monitoring access logs, network traffic, and configuration changes, you can detect and respond to security threats. Furthermore, by monitoring AWS EKS, you can create an audit trail which is necessary for many industry standards and regulations. You can track changes to your environment, audit your activities, and generate reports for compliance audits.

Top Monitoring Methods for EKS

Amazon CloudWatch

CloudWatch is a monitoring service for AWS resources and the applications you run on AWS. It allows you to collect and track metrics, collect and monitor log files, and set alarms. It’s a powerful tool for EKS monitoring.

For example, you can use CloudWatch to collect metrics about your EKS clusters, including CPU and memory usage, disk space, and network traffic. You can also use it to monitor the health and performance of your applications running on EKS.

CloudWatch also lets you set alarms based on thresholds that you define. If a metric reaches a certain level, an alarm is triggered and a notification is sent to you. This means you can take immediate action to prevent or mitigate issues.

Amazon Managed Service for Prometheus

Prometheus is an open-source systems monitoring and alerting toolkit that is very popular within the Kubernetes community. Amazon Managed Service for Prometheus (AMP) is a Prometheus-compatible monitoring service for containerized applications on AWS, including EKS.

With AMP, you can automatically scale your monitoring setup based on the load. This means you don’t have to worry about managing the infrastructure for your monitoring system. AMP provides a highly available, multi-AZ architecture out of the box, ensuring your monitoring data is secure and available when you need it.

You can also use AMP to query your monitoring data using the popular Prometheus Query Language (PromQL). This allows you to gain deep insights into the performance of your EKS clusters and applications.


AWS X-Ray helps developers analyze and debug distributed applications, such as those built using a microservices architecture. With X-Ray, you can trace requests from start to finish across all touchpoints, allowing you to identify bottlenecks, latencies, or errors that are impacting the performance of your applications.

When applied to EKS monitoring, X-Ray provides insights into how your containerized applications and microservices are interacting and performing. This includes information about request rates, latencies, and HTTP status codes.

X-Ray also provides a service map, a visual representation of your application’s underlying components. This helps you understand the interactions and dependencies between different services, which is incredibly valuable when troubleshooting complex issues.

AWS App Mesh

AWS App Mesh is a service mesh that provides observability, network traffic controls, and security for your applications. When integrated with EKS, App Mesh can provide detailed insights into the behavior of your applications and the network traffic between them.

App Mesh allows you to control and monitor the network traffic at a fine-grained level. This includes the ability to control the routing of requests, implement retries for failed requests, and enforce policies for network traffic.

App Mesh also integrates with other AWS services for EKS monitoring, like CloudWatch and X-Ray. This means you can get a comprehensive view of your applications’ performance, from the network level to individual requests.


In order to keep an eye on the many different services these applications are composed of, distributed tracing is critical to keep applications up and running smoothly. Lumigo is a cloud native observability platform that delivers automated distributed tracing, purpose-built for distributed applications, including those running on ECS and EKS. Lumigo provides deep visibility into applications and infrastructure with all the relevant information on each component, enabling you to easily monitor and troubleshoot container applications.
  • Automatically correlate metrics, events and traces and delivers visualizations of end-to-end requests in one complete view
  • Drill down into application performance and monitor clusters as well as underlying services in real-time
  • Set up customized alerts in notification platforms (ie Slack) and go from alert to root cause in just a few clicks

Key Metrics to Monitor in EKS

Control Plane Metrics

Control plane metrics give you visibility into the performance of your Kubernetes control plane. These include metrics like API server latency, etcd latency, and scheduler latency. By monitoring these metrics, you can ensure that your control plane is functioning optimally. 

Node Metrics

Node metrics provide insights into the performance and health of your nodes. These include metrics like CPU usage, memory usage, disk I/O, and network traffic. By monitoring these metrics, you can understand how your nodes are performing and identify any resource constraints.

Pod Metrics

Pod metrics give you an overview of the performance of your pods. These include metrics like CPU usage, memory usage, disk I/O, and network traffic. Monitoring these metrics can help you understand how your applications are performing and identify any bottlenecks.

Workload-Specific Metrics

In addition to the standard Kubernetes metrics, you should also monitor workload-specific metrics. These are metrics that are specific to your applications and services. For example, if you are running a web application, you might want to monitor metrics like request rate, error rate, and response time.

Best Practices for EKS Monitoring

Here are a few best practices that will help you effectively monitor your EKS clusters.

Setting up Alerts for Key Metrics

Setting up alerts for key metrics is another best practice. This can help you detect and respond to issues in a timely manner. You should set up alerts for critical metrics like CPU usage, memory usage, disk I/O, and network traffic. You should also set up alerts for abnormal behavior, like sudden spikes in resource usage or a high rate of errors.

Regularly Update Metric Selection

You should regularly review which metrics you are tracking in your EKS clusters, and which ones you use as critical metrics for alerting. As your clusters evolve and grow, especially if you start running new types of applications, some metrics might become less relevant and others might become critical to monitor.

Optimizing for Cost

Monitoring can help you optimize your costs. By tracking your resource usage, you can identify opportunities to reduce your costs. For example, you might find that some of your nodes are underutilized and can be downsized. Or, you might discover that auto-scaling is not configured optimally and is leading to unnecessary costs.

Monitor Security and Compliance

Monitoring your environment for security and compliance includes monitoring access logs, network traffic, and configuration changes. If you are on the DevOps team, coordinate closely with security teams to understand the metrics they need to identify and respond to attacks on EKS clusters. You should also regularly audit your activities and generate reports for compliance audits. 


Monitoring your Amazon Web Services Elastic Kubernetes Service (AWS EKS) clusters is critical for maintaining high availability, security, and performance in your cloud-based applications. Monitoring aids in troubleshooting operational issues, ensuring optimal performance, and maintaining a secure and compliant environment.

AWS provides multiple tools, such as CloudWatch, Amazon Managed Service for Prometheus (AMP), AWS X-Ray, and AWS App Mesh, to facilitate robust EKS monitoring. These tools enable alerting, metrics collection, and analysis of application performance. Moreover, third-party solutions like Lumigo can complement AWS’s native offerings, providing additional capabilities.

In addition, best practices like setting up alerts for key metrics, regularly updating your metric selection, and optimizing for costs can help you react swiftly to issues, and also anticipate them before they impact your applications, improving cluster operations and enhancing user experience.

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