Observability Tools for Amazon EKS: A Guide to Monitoring Your Kubernetes Cluster on AWS

Over the past year or so we’ve accelerated the use of containers in our environment, mostly relying on a standardized set of Elastic Kubernetes Service (EKS) clusters in AWS.  As part of our organization’s adoption of this robust service, observability has been a very hot topic.  

Observability goes beyond basic monitoring—it encompasses metrics, logs, and traces, giving you a full picture of your system’s health, performance, and behavior. With the right observability tools in place, you can troubleshoot issues faster, optimize performance, and maintain system reliability.

In this post, I’ll explore a range of tools you can use to achieve end-to-end observability with your EKS cluster. These tools cover metrics, logging, tracing, and application performance monitoring (APM), and they integrate well with the AWS ecosystem.

1. Amazon CloudWatch Container Insights

Purpose: Metrics and logging
Category: AWS-native

Amazon CloudWatch Container Insights is an AWS-native solution that helps you collect, aggregate, and visualize metrics and logs from your EKS clusters. It’s tightly integrated with AWS services and supports out-of-the-box dashboards for CPU, memory, network usage, and container-level logs.

Key Features:

• Automatically collects metrics from EKS using the CloudWatch agent and Fluent Bit.
  
• Dashboards for nodes, pods, and containers.
  
• Integration with AWS X-Ray for tracing.
  
• No need to manage external services.
  

Best For: Teams that prefer staying within the AWS ecosystem and want a managed, native observability solution.

2. Prometheus and Grafana

Purpose: Metrics and visualization
Category: Open-source

Prometheus is the de facto standard for metrics collection in Kubernetes. It scrapes metrics from services, nodes, and pods and stores them in a time-series database. Grafana is typically used alongside Prometheus to create dashboards and visualize this data.

Key Features:

• Pull-based metrics collection.
  
• Kubernetes-specific exporters (e.g., kube-state-metrics, node-exporter).
  
• Powerful querying with PromQL.
  
• Grafana provides highly customizable dashboards.
  

Deployment Options:

• Use the Helm charts to deploy Prometheus and Grafana into your EKS cluster.
  
• Alternatively, use Amazon Managed Service for Prometheus and Amazon Managed Grafana for a managed experience.
  

Best For: Teams that want open-source flexibility or already use Prometheus/Grafana outside AWS.

3. OpenTelemetry (OTel)

Purpose: Tracing, metrics, and logs
Category: Open-source

OpenTelemetry (OTel) is a CNCF project that provides a standardized, vendor-neutral way to collect telemetry data—metrics, traces, and logs—from cloud-native applications. It has quickly become the go-to framework for observability in distributed systems, including Kubernetes workloads.

Key Features:

• Unified instrumentation across programming languages and services.
  
• Supports collection of all three pillars of observability: metrics, logs, and traces.
  
• Exporters available for popular backends like Prometheus, Jaeger, Zipkin, and commercial tools (Datadog, New Relic, etc.).
  
• Automatic and manual instrumentation options.
  

Deployment Tips:

• Deploy the OTel Collector in your EKS cluster as a DaemonSet or sidecar.
  
• Use exporters to route telemetry data to CloudWatch, Prometheus, or third-party services.
  
• Combine with auto-instrumentation libraries for Java, Node.js, Python, and Go.
  

Best For: Teams looking for a standardized, open-source framework that offers flexibility and avoids vendor lock-in.

4. Fluent Bit and Fluentd

Purpose: Log collection and processing
Category: Open-source

Fluent Bit and Fluentd are popular logging agents for Kubernetes. Fluent Bit is lightweight and ideal for edge log forwarding, while Fluentd is more feature-rich and suited for complex log transformations.

Key Features:

• Collect logs from pods, nodes, and system components.
  
• Process, enrich, and route logs to multiple destinations (e.g., CloudWatch, Elasticsearch, S3).
  
• Used by AWS as the default logging agent in CloudWatch Container Insights.
  

Best For: Custom log pipelines or when integrating with multiple logging backends.

5. AWS X-Ray

Purpose: Distributed tracing
Category: AWS-native

AWS X-Ray helps developers analyze and debug distributed applications. When integrated with EKS, it traces requests through services, records latency, and helps pinpoint failures and performance bottlenecks.

Key Features:

• Automatic tracing with AWS SDKs and OpenTelemetry exporters.
  
• Visual service maps.
  
• Latency and error tracking for microservices.
  

Limitations:

• Best suited for applications using AWS SDKs or instrumented with OpenTelemetry.
  
• Not as feature-rich as commercial APM tools.
  

Best For: Teams invested in AWS and already using X-Ray in other services.

6. OpenSearch (formerly Elasticsearch) + Kibana

Purpose: Log analytics and visualization
Category: Open-source / AWS-native

Amazon OpenSearch Service (a fork of Elasticsearch) and Kibana provide powerful search and visualization for logs. With Fluent Bit or Fluentd shipping logs to OpenSearch, you can analyze and visualize them through Kibana dashboards.

Key Features:

• Full-text search on log data.
  
• Filters, aggregations, and visualizations.
  
• Kibana dashboards for infrastructure and application logs.
  

Best For: Teams that need deep log analysis capabilities and full-text search.

7. Datadog, New Relic, and Other Third-Party APMs

Purpose: Full-stack observability
Category: Commercial SaaS

Several third-party vendors provide comprehensive observability platforms with EKS support. These tools usually combine metrics, logs, and traces into a single pane of glass with built-in alerting, dashboards, and anomaly detection.

Popular Options:

• Datadog: Auto-instrumentation, Kubernetes-aware dashboards, APM, and synthetic monitoring.
  
• New Relic: Telemetry pipelines, distributed tracing, and service maps.
  
• Dynatrace, Splunk Observability, AppDynamics are also widely used.
  

Pros:

• Quick setup and powerful features.
  
• Excellent user experience.
  
• Integrations with AWS and Kubernetes.
  

Cons:

• Cost can scale quickly.
  
• Less flexibility than open-source options.
  

Best For: Teams that want quick time to value and a unified observability platform.

8. Kubernetes Dashboard + kubectl Top

Purpose: Quick diagnostics and resource inspection
Category: Native tools

While not full observability platforms, tools like the Kubernetes Dashboard and kubectl top can help with real-time cluster inspection.

Features:

• Inspect pod logs and resource usage.
  
• View deployments, services, and events.
  
• Basic CPU/memory metrics with kubectl top.
  

Best For: Developers or operators who want lightweight, quick-glance tools.

Putting It All Together

A solid observability strategy for EKS typically involves a mix of tools. But you don’t need to adopt everything at once. Start small—instrument a few services with OpenTelemetry, use CloudWatch for basic visibility, and add complexity as needed.

Final Thoughts

Observability is not just about collecting data—it’s about turning that data into actionable insight. With EKS, you have a rich ecosystem of both AWS-native and open-source tools at your disposal. Whether you want a tightly integrated managed solution or prefer open-source flexibility, there’s a path that fits your architecture and team capabilities.

As your infrastructure grows more complex, investing in observability will pay dividends in uptime, customer satisfaction, and engineering velocity. Start early, build iteratively, and keep your feedback loops tight.