Monitoring Container Health in Amazon EKS

Running containerised applications on Amazon EKS (Elastic Kubernetes Service) has become standard practice for modern DevOps teams. While EKS simplifies Kubernetes control plane management, monitoring container health remains a shared responsibility and often the most complex part of operating production workloads.

With multiple moving components such as pods, nodes, services, and underlying cloud infrastructure, visibility becomes critical. Without proper monitoring, small issues can escalate into outages, performance degradation, or user dissatisfaction.

This guide explains how to monitor container health in EKS effectively what to monitor, how to design your monitoring stack, key tools, best practices, common challenges, and FAQs to help teams operate confidently at scale.

1. Why Monitoring Container Health in EKS Matters

Kubernetes introduces abstraction layers that improve scalability and resilience but also add complexity:

• Containers run inside pods

• Pods run on nodes

• Nodes operate inside clusters

• Clusters rely on cloud infrastructure

A failure at any layer can cascade across the system.

For internal platforms, customer-facing applications, or training portals, monitoring ensures:

• High availability with minimal downtime

• Predictable performance under load

• Clear visibility into failures and bottlenecks

• Actionable alerts instead of vague error signals

EKS manages the control plane, but day-2 operations monitoring, alerting, and remediation remain your responsibility. Proactive monitoring allows teams to identify issues early and respond before users are impacted.

2. Monitoring Layers: Infrastructure → Cluster → Workload

Effective monitoring in EKS must span all layers. Focusing on only one leads to blind spots.

2.1 Infrastructure Layer (Nodes and Cloud Resources)

Monitor the foundation that supports your workloads:

• Node CPU and memory utilisation

• Disk I/O and latency

• Network throughput and packet errors

• Node availability and auto-scaling events

Problems here often surface later as pod crashes or slow application performance.

2.2 Kubernetes Platform Layer

This layer reflects how Kubernetes schedules and manages workloads:

• Node readiness and status

• Pod lifecycle states (Running, Pending, Failed)

• Deployment replica health

• Resource usage by namespace

• Scheduling failures and pod evictions

• Control plane responsiveness

These signals help identify capacity issues, misconfigurations, or orchestration problems.

2.3 Application and Container Layer

This is where user experience is directly affected:

• Container CPU and memory usage

• Restart counts and OOM kills

• Request throughput

• Error rates

• Latency (p95, p99)

• Application-level logs and traces

A healthy cluster does not always mean a healthy application. Monitoring must capture business-impacting metrics, not just system metrics.

3. Key Metrics and What They Reveal

3.1 Node-Level Metrics

• CPU saturation: Sustained high usage increases scheduling delays

• Memory pressure: Leads to pod eviction and instability

• Disk latency: Affects persistent workloads and logging

• Network errors: Impact service communication inside the cluster

3.2 Kubernetes Metrics

• Pod restarts: Indicate crashes, configuration errors, or resource limits

• Unschedulable pods: Signal capacity shortages or taint issues

• Node readiness changes: Reduce cluster capacity

• Control plane errors: Affect scheduling and deployments
  
  

3.3 Application Metrics

Use service-oriented indicators:

• Requests per second

• Error rates (4xx/5xx)

• Latency percentiles

• Resource usage per container

These metrics show how users actually experience your system.

3.4 Logs and Events

Logs and events provide context behind metric anomalies:

• Crash logs and stack traces

• Pod eviction and termination events

• Configuration or startup errors

• System component warnings

Metrics show what happened; logs explain why it happened.

4. Monitoring Tools for Amazon EKS

EKS supports both managed and open-source observability tools.

4.1 Managed and Native Tooling

• Container-level metrics and logs

• Centralised log storage and querying

• Managed time-series metric storage

• Dashboarding and visualisation

• Distributed tracing across services

These tools reduce operational overhead and integrate tightly with AWS infrastructure.

4.2 Open-Source and Hybrid Approaches

• Prometheus for metrics collection

• Grafana for dashboards

• kube-state-metrics for cluster state

• Log collectors such as Fluent Bit

• Distributed tracing using OpenTelemetry-based tools

Many teams combine managed services with open-source components for flexibility.

4.3 Choosing the Right Stack

• Managed tools suit teams prioritising simplicity and speed

• Open-source stacks suit teams needing deeper customisation

• Hybrid approaches balance control with reduced maintenance

The best choice depends on scale, team expertise, and operational maturity.

5. Setting Up Monitoring: A Practical Workflow

Step 1: Instrument the Cluster

• Enable container-level metrics and logs

• Deploy metric collectors and exporters

• Enable Kubernetes control plane logs

Step 2: Collect Metrics and Logs

• Set collection frequency based on criticality

• Route logs centrally for analysis

• Capture events from system and workloads
  
  

Step 3: Build Dashboards

Visualise:

• Node health and saturation

• Pod restarts and failures

• Application latency and error trends

• Resource utilisation by namespace

Dashboards should answer operational questions at a glance.

Step 4: Configure Alerts

Create alerts for:

• Sustained high resource usage

• Pod crash loops

• Node failures

• Sudden error spikes

Alerts should be actionable, not noisy.

Step 5: Investigate and Remediate

• Use logs and traces to identify root cause

• Correlate failures across layers

• Automate recovery where possible (scaling, restarts, node replacement)

Step 6: Review and Improve

• Refine alert thresholds

• Remove unused metrics

• Optimise log retention to manage cost

• Update dashboards as workloads evolve
  
  

6. Best Practices for Monitoring Container Health in EKS

6.1 Monitor End-to-End

Cover infrastructure, Kubernetes, and applications together to avoid blind spots.

6.2 Use Proven Metric Models

• USE for systems (utilisation, saturation, errors)

• RED for services (requests, errors, duration)

These models help prioritise meaningful signals.

6.3 Add Context with Labels and Tags

Segment metrics by environment, team, service, and namespace for clarity.

6.4 Define Intelligent Thresholds

Base alerts on historical patterns, not arbitrary values. Reduce false positives.

6.5 Manage Cost Proactively

Use tiered retention strategies for logs and metrics. Not all data needs long-term storage.

6.6 Automate Where Possible

Monitoring should trigger remediation, not just notifications.

6.7 Share Dashboards Across Teams

Make observability a shared responsibility among Dev, Ops, and SRE teams.

6.8 Continuously Iterate

Monitoring evolves with architecture changes. Regular reviews keep it effective.

7. Common Challenges and Solutions

Metrics Overload

Solution: Start with service-critical metrics, expand gradually.

Application Blind Spots

Solution: Instrument business-level metrics alongside infrastructure metrics.

Cost Escalation

Solution: Use different resolutions and retention periods for critical vs non-critical data.

Root Cause Identification

Solution: Correlate metrics, logs, and traces across layers.

Node vs Fargate Monitoring

Solution: Focus on container-level metrics consistently, adapting to the execution model.

8. Real-World Example: Training Platform on EKS

Scenario:
A training platform runs microservices on EKS and releases new content frequently.

What Monitoring Reveals:

• Increased pod restarts after a deployment

• API latency crossing defined thresholds

• Node CPU saturation triggering auto-scaling

• Disk pressure detected before outages occur

Outcome:
The issue is identified early, fixed quickly, and users experience no downtime. Monitoring enables proactive action rather than reactive firefighting.

9. Integrating Monitoring into DevOps and CI/CD

• Observe metrics during deployments

• Use alerts as release health gates

• Trigger rollbacks automatically if error rates spike

• Track SLOs as part of delivery reviews

Monitoring becomes a core delivery signal, not just an operational concern.

10. Key Takeaways

• Monitoring in EKS must span infrastructure, Kubernetes, and applications

• Metrics, logs, and events together provide full visibility

• Dashboards and alerts enable fast detection and response

• Automation turns observability into reliability

• Continuous review keeps monitoring effective and cost-efficient

A mature monitoring strategy transforms DevOps teams from reactive to proactive.

Frequently Asked Questions (FAQ)

Q1. What are the most important EKS monitoring metrics?
Pod restarts, unschedulable pods, node resource usage, application error rates, and latency.

Q2. Should I use managed or open-source tools?
Both are valid. Managed tools simplify operations; open-source tools offer flexibility. Many teams use a hybrid approach.

Q3. How frequently should metrics be collected?
Critical services benefit from high-frequency metrics; less critical workloads can use lower resolution.

Q4. How is container health different from node health?
Node health reflects resource pools, while container health reflects application stability and behaviour.

Q5. How do I avoid alert fatigue?
Limit alerts to high-impact signals, tune thresholds based on trends, and prioritise actionable events.

Q6. How does monitoring help with cost optimisation?
Resource usage metrics reveal over-provisioning and idle capacity, enabling smarter scaling decisions.

Q7. How do I keep monitoring relevant long-term?
Review dashboards, metrics, and alerts regularly as workloads and architectures change.