EKS Monitoring: The Key to Unlocking Kubernetes Performance

Last Updated: May 9, 2026

Among the many platforms for deploying containerized applications, one of the most popular is Amazon Elastic Kubernetes Service, also known as EKS. As a managed Kubernetes solution, EKS makes it easy to set up and operate Kubernetes clusters with minimal effort on the part of users.

However, the fact that EKS is a managed Kubernetes platform doesn't mean users bear no responsibility for monitoring and managing it. On the contrary, EKS logging and monitoring are just as important as logging and monitoring for any type of Kubernetes environment.

This article walks through everything you need to know about getting started with Amazon EKS, as well as how to monitor the platform. It walks through the EKS architecture, explains what to monitor in EKS and discusses tips and best practices for getting the most out of EKS logging and monitoring.

What is EKS? A Guide to EKS Monitoring

Amazon Elastic Kubernetes Service (EKS) is AWS's managed Kubernetes platform designed to simplify the deployment, management, and scaling of containerized applications in the AWS cloud. Elastic Kubernetes Service (EKS) provides a fully managed environment for running Kubernetes workloads.

EKS includes two major components:

• A Kubernetes control plane: The Amazon EKS control plane is a managed, core component of the Elastic Kubernetes Service (EKS), responsible for running key Kubernetes components such as the API server, etcd, and other Kubernetes components that make up the control plane. The API server serves as the primary management interface, handling API requests and exposing metrics for Amazon EKS clusters. Since it’s a fully managed service, all you need to do is turn it on; there is nothing to install or set up. The Amazon EKS control plane supports logging, providing audit and diagnostic logs directly from the control plane to CloudWatch Logs. Users can select specific log types and send them as log streams to a group for each EKS cluster. Amazon EKS clusters are managed through the Amazon EKS console, which offers a user interface for monitoring, logging, and API access.
• Nodes: EKS provides nodes, which are the servers that are clustered together to build Kubernetes environments. In EKS, nodes are based on EC2 virtual machine instances.  EKS manages nodes for you, so you don’t have to worry about launching or configuring them manually. You can also use EKS’s autoscaling features to add or remove nodes from your cluster automatically in response to changes in demand.

EKS is not the only container service available from AWS. Amazon also offers Elastic Container Service, or ECS, which is a managed container platform that is based on proprietary orchestration technology rather than Kubernetes. However, because Kubernetes has become the go-to container orchestrator, EKS has gained a larger following than ECS.

Note, too, that EKS is not the only way to deploy Kubernetes on the AWS cloud. You can also set up your own Kubernetes cluster on virtual machines hosted on Amazon EC2. If you take this approach, you get what’s called a self-managed cluster, as opposed to a Kubernetes environment that is managed by Amazon.

Benefits of EKS

Self-hosted clusters have some advantages over EKS. Above all, they give you more control over how your Kubernetes environment is configured and managed, and you get the flexibility to use a Kubernetes distribution of your choosing.

However, compared to self-hosted Kubernetes, EKS and other managed Kubernetes solutions (such as Azure AKS and Google GKE, which are similar to EKS in that they are also managed Kubernetes platforms on major public clouds and require their own AKS monitoring best practices and tools), offer several important advantages:

• ‍High availability: Amazon EKS provides a highly available Kubernetes control plane, with automated replication and recovery of control plane components. It's an easy way to build a highly reliable Kubernetes environment without having to configure complex backup and replication routines on your own.‍
• Cost efficiency: Amazon EKS offers a cost-effective way to run Kubernetes clusters, as the major cost is for the underlying EC2 instances. There is an additional fee of $0.10 per hour per cluster, or about $73 per month, but that is relatively low – less than the licensing cost of many Kubernetes distributions that you'd have to use in a self-hosted Kubernetes environment.‍
• Security: EKS provides enhanced security for your Kubernetes clusters, with automated security patching, IAM authentication, and encryption of data in transit. You can also take advantage of role-based access control (RBAC) frameworks and network segmentation to add more layers of security to your clusters. Enabling audit logging is recommended to track all EKS API calls and cluster changes for compliance and security, utilizing control plane logs. AWS CloudTrail can be used to record API calls and audit user and service activities within EKS. Monitoring control plane logs and security events is essential for maintaining compliance and detecting potential threats. Additionally, enabling Amazon GuardDuty helps detect anomalous behavior and threats at both the container and control plane levels.‍
• Elasticity and scalability: Amazon EKS allows you to scale your Kubernetes clusters up or down easily to meet your changing resource needs.‍
• Ease of use: EKS makes it easy to deploy and manage Kubernetes clusters. You can launch clusters in minutes with just a few commands or clicks in the AWS Console. You also get automated updates, patching and built-in logging and monitoring, all of which make for an even more user-friendly Kubernetes experience.

If you don't mind sacrificing a bit of control and flexibility, EKS and similar Kubernetes solutions provide the easiest onramp to launching Kubernetes clusters that are secure, cost-effective and reliable.

What do you need to monitor on EKS?

The fact that EKS does most of the heavy lifting required to set up and operate Kubernetes clusters doesn't mean that users don't have to worry about EKS monitoring. On the contrary, even with a fully managed Kubernetes platform like EKS, a variety of things can go wrong. If you don't monitor your EKS environment, you won't know about these problems in time to respond to them.

That's why it's important to monitor the following aspects of EKS:

• ‍Cluster and node health: Although EKS should automatically scale clusters up and down as needed, sometimes that is not the case; for instance, a misconfiguration in your autoscaling policies or a problem with the OS configuration running on nodes could lead to cluster or node stability issues. To track performance and identify problems like node failures or a lack of available resources for workloads, you should monitor overall cluster performance, including CPU usage and memory usage at both the cluster and individual node level. Monitoring these metrics helps prevent resource issues and ensures optimal operation.‍
• Pod and container health: EKS manages the control plane and infrastructure required to run containers, but it doesn’t guarantee the health of containers that you deploy on the platform. Buggy containers or misconfigured Pods could lead to a wide variety of performance issues with applications deployed on EKS, and EKS won’t fix them for you, so understanding Kubernetes container events, failures and restarts is critical for troubleshooting. It’s on you to make sure you have the Pod and container insights necessary to ensure that, for instance, your containers are starting properly, and their resource utilization remains stable. Monitoring the current state of your Kubernetes workloads is essential for maintaining application reliability.‍
• Network and ingress monitoring: Monitoring network traffic and ingress in EKS environments is important both for detecting networking issues, like high latency, that could undercut application performance, and for catching potential security problems, such as attempted DDoS attacks.‍
• Kubernetes objects monitoring: Use cluster state metrics generated by kube-state-metrics to gain insights into the health and performance of Kubernetes objects such as deployments, replica sets, nodes, and pods. Monitoring these metrics is crucial for effective pod scheduling and understanding the current state of your cluster.

Effective monitoring of Amazon EKS clusters requires a multi-layered approach that combines infrastructure metrics, application logs, and network performance. Monitor at three primary levels, infrastructure layer (worker node health, CPU, memory, disk, and network utilization, plus underlying AWS service metrics), platform layer, and application layer, to ensure a holistic view of your cluster's health. Focus on tracking latency, traffic, errors, and saturation (CPU/memory) to align with business Key Performance Indicators (KPIs).

The takeaway here is that although EKS is designed to make it easy to run containerized apps, it's not a hands-off solution. You need to monitor your clusters, nodes, Pods, containers and networks, just as you would in any type of Kubernetes environment.

EKS monitoring and logging tools

Tools for monitoring EKS fall into two main categories.

The first consists of tools and services available from AWS itself. The most important is Amazon CloudWatch, which can collect a variety of metrics related to EKS cluster, node, Pod and container performance. Amazon CloudWatch Container Insights is a fully managed service for monitoring, collecting, and analyzing metrics and logs for containerized applications on Amazon EKS, providing performance monitoring, troubleshooting, and alarm capabilities. AWS Distro for OpenTelemetry (ADOT), a supported version of the OpenTelemetry project, enables users to send correlated metrics and traces to Amazon Managed Service for Prometheus and AWS CloudWatch, enhancing observability. AWS X-Ray provides distributed tracing to track requests across microservices, generating service maps and enabling end-to-end tracking for performance optimization. The container image scanner built into Amazon ECR (a managed container registry that can be used to host images that you deploy into EKS) is useful for detecting malware or vulnerabilities inside container images. Effective Amazon EKS monitoring combines native AWS tools with industry-standard open-source tools to track cluster health, resource usage, and application performance.

The other category of EKS monitoring tools includes third-party solutions, some of which (like Prometheus) are open source and others of which are available from commercial vendors that focus on Kubernetes observability best practices and tooling. Prometheus is an open-source monitoring and alerting toolkit that collects time-series metrics by scraping Kubernetes nodes, pods, and services, making it useful for proactive issue detection and monitoring EKS clusters. It integrates with Grafana for visualization, forming a powerful monitoring stack that allows Kubernetes operators to monitor cluster health and understand application behavior. Tools like node exporter and kube-state-metrics are used for collecting node and cluster state metrics, providing essential insights into cluster health, node status, and scheduling performance. Grafana dashboards, including prebuilt dashboards specifically designed for Kubernetes and EKS monitoring, enable visualization of EKS metrics and cluster health, supporting troubleshooting and security analysis. Monitoring container logs and infrastructure metrics together provides comprehensive cluster visibility.

Centralizing log management by aggregating control plane, node, and pod logs into a single searchable location streamlines troubleshooting. Fluent Bit can be used to stream container logs to CloudWatch or Elastic/OpenSearch for long-term storage and analysis, and formatting logs as JSON ensures they are easily searchable and queryable in tools like CloudWatch Logs Insights. Structured logging and data ingestion are important for capturing, forwarding, and analyzing log data, which directly impacts troubleshooting, cost management, and operational analysis.

Observability in Kubernetes (and EKS) refers to the ability to understand the internal state of your cluster and applications by analyzing outputs such as logs, metrics, and traces—key pillars that enable DevOps teams to detect, debug, and optimize Kubernetes workloads effectively. Monitoring has evolved into observability, allowing infrastructure and application owners to actively troubleshoot and debug their systems by analyzing patterns derived from metrics, logs, and traces. Data ingestion and analysis are crucial for troubleshooting and optimizing EKS environments.

The tradeoff is that third-party tools are somewhat harder to set up because you need to configure them to work with your EKS clusters. In contrast, you can launch most of the Amazon monitoring tools by simply turning them on in the AWS Console, after which they integrate with your EKS clusters automatically.

Best practices for EKS monitoring

Whether you choose to monitor EKS using Amazon’s tools, third-party tools, or a combination thereof, maintaining an effective monitoring stack—such as Prometheus and Grafana—requires ongoing management and regular updates. Implementing a multi-layered strategy that covers the infrastructure, the Kubernetes platform, and the applications themselves is essential. When deploying applications, especially microservices, on EKS, it’s important to monitor them using observability tools to ensure their health and performance. To get the most out of your monitoring strategy, define meaningful alerting rules in Prometheus for critical metrics, and use Alertmanager to route alerts, preventing fatigue and ensuring rapid response to issues. Set meaningful thresholds and prioritize alerts based on severity to avoid alert fatigue, focusing on critical business impact rather than every minor spike in CPU. Additionally, be selective about log levels, use log sampling for high-volume logs, and leverage tools like Kubecost to track and optimize monitoring spend. For more, see Kubernetes monitoring challenges and best practices.

Configure alerts

Setting up EKS logging and monitoring tools is a first step toward ensuring you can react to problems. But on their own, logging and monitoring solutions don't necessarily alert you when something is wrong. And if they do, their alerting policies may be based on generic rules that aren't adapted for your specific workloads or cluster configurations.

For that reason, it's important to configure alerts based on your unique requirements, following Kubernetes alerting best practices for actionable notifications. Consider factors like what level of performance and availability risk you can tolerate, then set up alerts accordingly.

Monitor cluster and application health

In the context of EKS, infrastructure monitoring and application monitoring should not be either-or propositions. You need to monitor both.

Monitor cluster health and performance by tracking the CPU, memory and network usage of your EKS worker nodes. At the same time, collect performance metrics from your applications, including resource utilization rates, as well as the availability and response time for services.

Monitoring the complete EKS stack is important because even though Amazon manages the infrastructure for you, various problems could arise within the stack, and you won't be able to pinpoint their source if you're not monitoring both the cluster infrastructure and the workloads running on it.

Configure effective log retention

Most EKS monitoring tools (including CloudWatch) allow you to retain EKS logs and metrics data, but the longer you store that information, the more you'll pay.

You should therefore configure log retention policies in your monitoring tools that ensure you'll have access to your EKS data for as long as you need it, but that you don't store it unnecessarily. You can also consider pushing older EKS monitoring data to lower-cost storage, like a data lake that you use to host archived information.

Monitoring Kubernetes with eBPF

In addition to standard Kubernetes logging tools, there's a powerful, next-generation tool that can supercharge Kubernetes monitoring.

It's called eBPF, and it allows you to deploy programs that attach to the Linux kernel in order to monitor networking interfaces and system call entry points from a low level using modern eBPF tracing techniques. With eBPF, you can collect information very efficiently and securely to track EKS networking performance, node health and Pod and container operations, enabling comprehensive eBPF-based observability across infrastructure and applications.

Although eBPF isn't the only way to monitor Kubernetes, it provides advantages that conventional approaches to Kubernetes monitoring – which usually involve deploying monitoring agents into user space on nodes and/or as "sidecar" containers that run alongside actual workloads – lack, especially when you leverage eBPF for Kubernetes observability and security:

• ‍Access: Because eBPF programs run in kernel space, they can collect virtually any data you might want to monitor. They're not limited to data exposed in user space.‍
• Efficiency: eBPF programs are lightweight and efficient, so they impose minimal overhead while supporting high‑granularity eBPF profiling for system resources. The result is more resources for your actual workloads, and less money wasted on EKS infrastructure.‍
• Security: eBPF programs run in sandboxed environments, which minimizes security risks even though the programs operate in kernel space.

Because eBPF remains a pretty new technology, it hasn't traditionally been at the core of EKS monitoring and logging tooling. Indeed, most EKS monitoring solutions, including CloudWatch, offer no support for eBPF.

However, a new generation of monitoring tools, like groundcover's full‑stack eBPF observability platform, are emerging that leverage eBPF to revolutionize how EKS – along with any platform or application that runs on top of Linux-based servers – is monitored and observed. With the help of eBPF, these tools make it possible to perform deep, secure, highly efficient monitoring across the entire stack, from infrastructure to applications.