Kubernetes Monitoring: Best Practices and Tools

Kubernetes may be a powerful solution for enterprise development, but that doesn’t mean it’s always predictable. Workloads scale up and down, traffic shifts between services, and clusters themselves can be short-lived, appearing and disappearing under the control of automation. That’s a lot of variables to keep tabs on. Without a solid monitoring solution in place, you're essentially guessing what's going on, and that guesswork doesn’t fly in production. 

Effective K8s monitoring helps you spot critical issues early, gather insights into clusters' performance, and avoid any surprises, ultimately strengthening visibility and control. 

Let’s take a closer look at Kubernetes monitoring best practices and explore the best tools to maximize its effectiveness.

Key highlights:

• Kubernetes environments are dynamic and unpredictable, making real-time K8s monitoring essential to maintain stability and performance.

• To be effective, Kubernetes monitoring must include logging, alerting, cost visibility, and application-level insights.

• The best Kubernetes monitoring tools deliver native integration, scalability, real-time dashboards, and security.

• Mirantis delivers enterprise-grade Kubernetes monitoring through its Kordent Observability and FinOps framework, offering unified control, visibility, and cost efficiency across clusters.

What Is Kubernetes Monitoring?

Kubernetes monitoring is the process of collecting and reviewing operational data across clusters, nodes, pods, and containers to understand how workloads are running. It gives teams a way to identify issues, track app performance, and stay ahead of problems before they escalate.

Because Kubernetes environments are constantly changing, monitoring must extend beyond basic uptime checks. It typically spans three main pillars:

1. K8s Logging, Metrics, and Alerting

Start with the basics: pod health, container restarts, node usage, and cluster events. These core signals help you flag early signs of trouble. With the right alerts in place, your team gets notified the moment something breaks or performance starts to slip. Skip this layer, and you’re left reacting only after users feel the impact.

2. Application Insights and Observability

Observability builds on basic monitoring by showing how applications behave over time. It captures data like latency, error rates, and throughput, giving teams the context they need to pinpoint where performance breaks down, making it easier to trace issues back to the exact service or dependency.

Most platforms don’t offer this level of visibility by default. Reaching it typically requires tools built for application-level observability.

3. Kubernetes Cost Monitoring and Resource Visibility

K8s is built for flexibility, but that can create price (in high-cost public clouds) or utilization (anywhere) shock if teams aren't paying attention. Kubernetes resource monitoring helps track what each workload is using and shows where you're using more resources (hence possibly spending) more than you need to. In complex environments, especially those spread across public clouds, this visibility is essential for keeping costs under control – in fact, top monitoring solutions provide means for converting utilization metrics to costs in real-time, helping you keep track of spend, optimize your setup for long-term savings, and project expenditures based on usage.

Learn how to monitor Kubernetes costs using Kubecost and Mirantis Kubernetes Engine.

Why Monitoring Kubernetes Is Important for Your Organization 

Kubernetes management tools make it easy to deploy containers at scale, but that flexibility introduces new layers of complexity. Clusters grow and shrink on demand, pods shift constantly, and traffic patterns change without warning. Without visibility into costs, all this automation means issues can quickly spiral into major disruptions or expenditures before anyone realizes what’s going on.

When Kubernetes monitoring is limited or missing entirely, impacts tend to show up quickly. Most common consequences include:

• Service Disruptions: Outages happen when failures go undetected until they affect user-facing systems.

• Delayed Incident Response: Without early signals, teams often troubleshoot too late or guess at the root cause.

• SLA Violations and Compliance Risks: Gaps in monitoring can cause missed uptime guarantees or audit issues.

• K8s Security Vulnerabilities: Lack of visibility makes it harder to detect suspicious behavior or lateral movement in clusters.

• Wasted Spending: Over-provisioned Kubernetes resources or runaway workloads drive up cloud costs without anyone noticing.

What Are the Benefits of Effective K8s Monitoring?

With the right solution for monitoring Kubernetes in place, you can mitigate issues as they happen, reduce overhead spending, and gain better control over how clusters run within your environment.

Here’s what strong monitoring unlocks:

• Enhanced Reliability and Troubleshooting: Kubernetes applications (particularly those that take advantage of a cloud native or microservices architecture) can be especially complicated, and if something goes wrong, tracking down the source of the issue can be difficult. Appropriate K8s visibility lets you see where problems may be occurring (or about to happen), and monitoring enables you to take action.

• Kubernetes Performance Tuning: Knowing what’s going on inside your Kubernetes cluster will enable you to make decisions that make the most of your hardware without compromising the performance of your applications.

• Improved Cost Management: If you’re running Kubernetes on a public cloud infrastructure, it’s essential to keep track of how many nodes (servers) you’re running. Even if you’re not running in the public cloud, it’s critical to know whether you’re over-resourced.

• Simplified Chargebacks: In some situations, you will want to know what groups have used what resources so that Kubernetes monitoring can provide you with information on usage statistics for chargebacks or showbacks, or simply for Kubernetes cost analysis.

• Better Security: In today’s environment, it’s crucial to be able to know what’s running where, to spot unauthorized jobs, or to spot DOS attacks. Kubernetes monitoring can’t solve all of your security issues, but without it, you’re at a definite disadvantage.

What Visibility Is Required to Monitor K8s Properly

Of course, you can’t monitor what you can’t see, so Kubernetes visibility is a huge part of Kubernetes monitoring. What you’re looking for is going to depend on the level at which you’re looking.

Here are the main types of monitoring:

• Container monitoring: At the container level, there’s not much you can look into besides the basics, such as how much CPU the container is using while it’s running. Containers are ephemeral, so once a container stops, you can’t log into it to see what’s going on.

• Application monitoring: Your application is, of course, written by you, and may not have built-in monitoring hooks, but you can expose any metrics that you feel are appropriate according to the business rules of the application. You’ll want to ensure that you persistently do this by integrating with a monitoring system (we’ll get to that in a minute) rather than within the ephemeral environment of the container.

• Pod monitoring: Pods have their statistics, such as their state and the number of replicas running versus the number that were requested. You’ll want to keep track of that to watch for problems caused by misconfigurations or resource depletion.

• Node monitoring: Your applications ultimately run on nodes, so it’s crucial to monitor those nodes to ensure that they’re healthy. Metrics that should be part of your Kubernetes monitoring include CPU utilization, storage availability, and network status.

• Cluster monitoring: Kubernetes monitoring at the cluster level should be more than just an aggregation of metrics from the other levels. Ideally, you should have an overall view using a dashboard that enables you to make sense of utilization and identify anomalies before they become issues

How to Monitor Kubernetes: Key Steps

Kubernetes monitoring is about capturing the right signals, triggering action when things go wrong, and continuously optimizing as workloads shift. These five steps provide a strong foundation for understanding and managing your clusters:

1. Collect Core Metrics and Logs from the K8s Cluster

The starting point for any monitoring strategy is data collection. This information lays the groundwork for comprehensive visibility, helping teams understand what’s happening in real time and catch issues before they grow into bigger problems.

Focus on collecting the following signals to build your monitoring baseline:

• CPU, memory, and disk usage across nodes and individual pods

• Containerized application logs and Kubernetes component logs (e.g., kubelet, controller manager)

• Events from the Kubernetes API that highlight changes or failures

2. Establish Meaningful Alerts That Trigger Dev Team Action

Metrics are only beneficial if they produce actionable insights. Once core data is flowing, teams need to define alerting thresholds and escalation paths. These alerts should reflect real service degradation—not just random spikes—so teams can respond quickly and confidently to signals that matter.

To configure alerts in a meaningful way (and avoid burnout from too-frequent alerts), start by setting up notifications for:

• Critical failures like pod restarts, node unavailability, or persistent crash loops

• Resource exhaustion, such as CPU or memory saturation

• Threshold-based events tied to SLAs or user experience metrics

3. Expose Application-Level Performance Signals

Your infrastructure may be healthy, but what about your app? That’s why monitoring shouldn’t stop at the cluster layer. Teams need visibility into service performance to catch regressions and diagnose slowdowns before they impact users.

To track what your app is doing, configure monitoring to report signals like:

• Request duration, error rates, and throughput

• Application logs enriched with context for traceability

• Traces that show how requests flow across services (via OpenTelemetry or mesh-based tooling)

4. Track Resource Usage and Control Costs Across Applications

Kubernetes can scale rapidly, but scaling without visibility leads to waste. By monitoring resource usage and cloud consumption, teams can avoid unnecessary costs while still maintaining performance.

Make sure your monitoring system captures usage patterns such as:

• Overprovisioned pods or idle deployments that waste CPU/memory

• Namespace-level or workload-specific usage to guide right-sizing

• Trends that can support autoscaler tuning or budget forecasting

5. Review and Refine Your K8s Monitoring Strategy Regularly

As workloads shift and traffic grows, the dashboards and alerts you used to rely on can quickly fall out of sync. To stay ahead of issues, it’s essential to review your monitoring regularly and make adjustments based on how your clusters are running.

When reviewing your approach, focus on these areas:

• Are metrics still aligned with current workloads and goals?

• Are alerts triggering appropriately, or being ignored?

• What gaps were uncovered during incidents or postmortems?

Kubernetes Visibility vs Monitoring: Main Differences

It’s important to understand that while the two are related, there is a difference between Kubernetes visibility and monitoring. Here’s how they differ:

• Visibility is how the data is made available by the application

• Monitoring is how it’s made available to a human 

For example, Kubernetes provides a set of limited metrics, such as CPU usage and memory usage, via the in-memory metrics-server. This component collects information such as CPU and memory usage, and is how components such as the Horizontal Pod Autoscaler know what’s going on within the cluster. 

Kubernetes provides several ways to get this kind of “live” visibility, such as:

Kubernetes Liveness and Readiness Probes

When you define a container in Kubernetes, you can also specify a programmatic way to determine whether the container is ready and still alive. Consider this example:

apiVersion: v1
kind: Pod
metadata:
  labels:
    test: liveness
  name: liveness-exec
spec:
  containers:
  - name: liveness
    image: k8s.gcr.io/busybox
    args:
    - /bin/sh
    - -c
   - touch /tmp/healthy; sleep 30; rm -rf /tmp/healthy; sleep 600
   livenessProbe:
    exec:
      command:
     - cat 
     - /tmp/healthy 
   initialDelaySeconds: 5 
   periodSeconds: 5

In this case, Kubernetes will look for a /tmp/healthy file every 5 seconds, and if it doesn’t find one, it will assume the container has died and will kill it and create a new one. For this example, the container will appear to be healthy, and then, when the file is removed, it will appear to have crashed and will be replaced.

Kubernetes uses this information to determine the specific state of the container, but unless the actual probes are designed to do so, their information doesn’t connect to other systems, and is localized in influence to the container or pod.

Kubernetes-metrics-server

This add-on component generates an in-memory look at the Kubernetes cluster as a whole, including pod statistics, memory and CPU usage, and so on. It can provide a stream of data to another application that is asking for it.

Kubernetes Dashboard

This is a separate component that you can install to see a live version of what’s going on inside your cluster. It lists workloads, nodes, and so on, and also enables you to take actions such as creating or destroying objects, so if you install it, ensure that your security is set up correctly.

How Monitoring K8s Complements Visibility Tools 

The problem with visibility solutions is that they are only a “live” view of what’s going on in the cluster; they don’t save this data, so there’s no way to use it to see trends or understand what happened before a catastrophic failure. To do that, we need to export all of those metrics from Kubernetes to a time-series database such as InfluxDB, with a front end that enables you to create dashboards to see what’s going on.

One of the most popular ways to do Kubernetes monitoring is to use a tool called Prometheus with a GUI called Grafana. For example, Mirantis StackLight uses these tools together to provide visibility into MKE 4k Kubernetes clusters, precisely indicating which service caused a failure. It also offers built-in alerts on anomaly and fault detection (AFD) metrics that can be extended to create custom alerts. The alarms can be exported to other systems via standard protocols such as SNMP.

K8s Monitoring Best Practices for Enterprises

Kubernetes monitoring isn’t something you configure once and forget. Environments shift constantly, so your monitoring approach has to evolve with them. The goal isn’t just collecting data; it’s making sure that data leads to better outcomes across performance, reliability, and cost.

Follow these five Kubernetes monitoring best practices to get the most from your monitoring setup:

Kubernetes Monitoring Best Practice #1: Treat K8s Tracking as an Iterative Process

Monitoring needs evolve as workloads shift. What triggered an alert last month might be noise today. Regularly audit your dashboards and alert thresholds to avoid blind spots or alert fatigue. Use incident postmortems to identify what monitoring failed to catch, and revise accordingly to prevent similar issues from recurring.

Kubernetes Monitoring Best Practice #2: Capture Both Infrastructure and Application Signals

Monitoring the health of your cluster isn't enough—you also need to understand how your application behaves in production. Combining system-level metrics with application-level insights gives your team a clearer picture and helps isolate the actual cause of performance issues.

Here’s what a balanced monitoring approach should include:

• Track infrastructure performance by monitoring pod status, node usage, container restarts, and key Kubernetes events.

• Expose application behavior with request latency, error rates, throughput, and distributed traces across services.

Kubernetes Monitoring Best Practice #3: Separate Monitoring from Observability

Monitoring is reactive. It collects metrics, watches logs, and fires off alerts when thresholds are breached, but when something unexpected happens—like a latency spike buried deep in a service mesh—monitoring alone won’t explain it. That’s where observability comes in. It pulls in traces, spans, and time-series analytics to provide context, such as:

• How requests move through your systems 

• Where slowdowns start

• What dependencies are involved

Don’t force one tool to do both. Instead, leverage monitoring for alerts and uptime, and observability for root cause analysis and optimization.

Kubernetes Monitoring Best Practice #4: Prioritize Cost Awareness

Kubernetes makes scaling easy, even for a small team, but that flexibility often hides inefficiencies. Monitor resource usage across workloads, namespaces, and environments to spot overprovisioned deployments or idle containers. This visibility supports better capacity planning and cost forecasting, especially in multi-cloud or hybrid setups where usage can spiral out of control.

Kubernetes Monitoring Best Practice #5: Integrate with Broader Platform Strategies

For organizations running distributed or hybrid environments, monitoring must align with the overall platform strategy. Solutions like Mirantis KOF help centralize telemetry, simplify alerting, and maintain consistency across child clusters. This unified approach keeps large-scale operations manageable without fragmenting visibility or control.

Kubernetes Monitoring Software Options for Enterprises

With so many moving parts in an environment, choosing the right software to monitor Kubernetes is critical. Enterprise teams need tools that go beyond basic metrics, offering deep visibility, customization, and scalability across clusters. 

Here are the best Kubernetes monitoring tools:

Choosing the Best Monitoring Tools for Kubernetes 

Not every monitoring tool is built for Kubernetes. To get meaningful visibility, teams need solutions that can keep up with the rapid pace of change in containerized environments. That means choosing tools that are purpose-built for K8s, integrate cleanly with your stack, and offer the right level of control.

When evaluating Kubernetes monitoring tools, consider the following criteria:

• Native Kubernetes Integration: Explicitly designed for containerized environments to surface relevant metrics without extensive customization

• Scalability Across Clusters: Able to adapt as clusters expand, shrink, or shift without overwhelming dashboards or users

• Readable, Real-Time Dashboards: Provide clear visualizations that highlight performance trends and surface anomalies quickly

• Smart Alerting and Automation: Support for real-time notifications, playbooks, and incident routing to reduce manual intervention

• Built-In Security and Compliance Visibility: Help teams track access patterns, detect suspicious activity, and support audit readiness

You’ll also want to balance feature depth with ease of use. The most powerful solution isn’t always the best fit, especially if it’s hard to implement or maintain. Dev and Sec teams should focus on what they need today, while embedding monitoring that can evolve with them. Once a tool is deeply integrated, teams rarely switch, so it pays to start with a solution that’s built for long-term control and visibility.

Stay Secure with Kubernetes Monitoring Solutions from Mirantis 

With the Kordent Observability and FinOps framework, platform teams get more than basic monitoring. Instead, they gain a scalable, secure observability layer purpose-built for modern, distributed Kubernetes environments.

Key features include:

• Template-Driven Multi-Cluster Management: Standardize operations and accelerate provisioning with GitOps-ready templates.

• Native K8s Event Tracking and Real-Time Dashboards: Visualize deployment behavior and system health in real-time.

• Consistent Telemetry Across All Environments: Maintain control and compliance across all your infrastructure.

• Self-Managed Control Plane Architecture: Operate a centralized “mothership” for secure, low-friction management.

• Integrated Metrics and Alerts: Detect anomalies, performance regressions, and cost drivers before they escalate.

Book a demo today and see how Mirantis can help your team enhance Kubernetes monitoring with enterprise-grade control, visibility, and support.