Kubernetes Monitoring Tools: What They Are, Metrics To Track, and How To Choose One

Kubernetes is an autonomous system with lots of components that continuously produce data. Without monitoring it carefully, you will not be able to manage your cluster as you won’t know what it’s doing. As Kubernetes continues to power more and more cloud-native applications, keeping your clusters healthy and performant has never been more important. But monitoring Kubernetes isn’t like monitoring traditional infrastructure — it requires tools that can keep up with its dynamic, containerized and often complex environments. 

In this guide, we’ll explore why Kubernetes monitoring matters, what makes it unique, which metrics to track and the key features to look for when choosing the right monitoring tools for your team.

The basics of Kubernetes monitoring tools

Kubernetes monitoring tools are platforms or utilities that track the health, performance and resource usage of Kubernetes. They often track all Kubernetes components, including clusters, nodes, pods and containers and the applications and services running on them. 

Because Kubernetes orchestrates large, dynamic environments, monitoring is essential for ensuring that applications run smoothly, detecting failures early, optimizing resource use and maintaining service reliability.

Kubernetes monitoring tools typically provide:

• Metrics collection (CPU, memory, disk, network usage)
• Pod and node health status
• Log aggregation and analysis
• Alerting and notifications
• Visual dashboards and observability
• Anomaly detection and performance tuning
• Integration with DevOps/CI-CD pipelines

Why are Kubernetes monitoring tools important?

Kubernetes monitoring tools are critically important because Kubernetes itself is a complex, dynamic system that manages containers at scale. Without the right monitoring in place, issues can go unnoticed until they become outages and opportunities to optimize performance or cost can be missed.

Here are some specific reasons Kubernetes monitoring is important:

They detect issues early

Whether it’s pods crashing or nodes running out of memory, Kubernetes monitoring is important because it can catch onto these issues and bottlenecks before they impact users. The faster the detection, the more you can decrease incident response time and decrease downtime.

They troubleshoot and analyze root causes

Monitoring tools also play a crucial role in troubleshooting and root cause analysis. When something breaks, it’s not always obvious why. A sudden CPU spike in a pod, a misconfigured service causing network latency or a memory limit crash…all of these become much easier to diagnose when logs, metrics and traces are collected and visualized in one place. This shortens the time to resolution and improves developer productivity.

They offer increased visibility

Kubernetes is a powerful but highly abstracted system that automates many aspects of container orchestration, such as scheduling, scaling, networking and health checks. While this automation makes it easier to manage large-scale environments, it also means that issues can be buried beneath the surface. Monitoring tools provide critical visibility into what’s happening inside your clusters — tracking node and pod health, container resource usage, network latency and application performance. Without this insight, teams are essentially flying blind, unable to proactively manage or optimize their infrastructure.

They support SLAs and compliance

Many organizations operate under strict service-level agreements (SLAs) or compliance requirements. Monitoring tools help support these obligations by providing real-time metrics, historical data and detailed audit trails. Teams can set alerts for SLA thresholds, generate uptime reports and track system health over time. This kind of visibility is not only useful for internal accountability but also necessary for passing audits and demonstrating compliance with industry standards and regulations.

They optimize performance

Monitoring tools provide the metrics needed to fine-tune performance across clusters. By analyzing trends in CPU, memory, disk and network usage, teams can identify containers that are over-consuming resources or services that aren’t scaling properly. This insight helps optimize autoscaling rules, balance workloads across nodes and avoid resource contention. Ultimately, performance monitoring enables organizations to run their infrastructure more efficiently, which translates into cost savings and a better experience for users.

How does a Kubernetes monitoring tool work?

Kubernetes monitoring works by collecting, analyzing and visualizing data from various components of your cluster, including nodes, pods, containers and the Kubernetes control plane. This is typically done using a combination of metrics, logs, events and traces. Monitoring tools deploy agents or exporters within the cluster that expose performance data in standardized formats, often via HTTP endpoints. 

The collected data is then stored in a time-series database or centralized log system where it can be queried and analyzed. Visualization tools display real-time dashboards that help teams understand how their infrastructure and applications are performing. Alerts can also be configured within these systems to notify teams when something goes wrong.

It’s important to note that Kubernetes monitoring is different from traditional monitoring. There are some of the key differences between the two.

Ephemeral and dynamic infrastructure

In traditional environments, infrastructure is relatively static: a server stays online for months or years. In Kubernetes, containers and pods are short-lived and constantly changing. Monitoring tools must automatically detect these changes in real time, or they’ll miss critical customer data.

Higher granularity

Rather than monitoring just servers or virtual machines, Kubernetes monitors activity at multiple levels: cluster → node → pod → container → application. This fine-grained visibility is necessary to understand how workloads behave under varying conditions.

Service-oriented focus

Traditional monitoring is typically host-centric (focused on CPU, memory and disk). Kubernetes monitoring is service-centric, focusing on how microservices perform and interact (e.g., request latency between services, availability of endpoints, autoscaling effectiveness).

Decentralized and multi-layered architecture

Kubernetes introduces multiple control layers and orchestrated components. Monitoring must cover the control plane (like kube-scheduler), worker nodes, container runtimes and applications, all of which can fail independently.

Built for DevOps and observability

Kubernetes monitoring is more closely integrated with DevOps practices and CI/CD pipelines, enabling faster release cycles and real-time feedback. Tools are often cloud-native and support metrics, logs, and traces (the observability trifecta) in a unified view.

Scalability requirements

Monitoring in Kubernetes must be highly scalable to support hundreds or thousands of pods spinning up and down constantly, especially in large enterprise or multi-tenant environments.

Top metrics to watch in Kubernetes monitoring

Monitoring tools for Kubernetes can track a wide variety of metrics. The exact metrics that you should pay attention to may vary depending on your goals and how your organization is set up. However, there are some metrics that are generally helpful to pay attention to: 

Cluster health and control plane metrics

The following aspects help you understand the overall state of your Kubernetes environment and whether the control plane is functioning properly:

• API server availability and request latency: Measures the responsiveness of the control plane.
• Scheduler and controller manager health: Indicates if Kubernetes is scheduling pods and managing workloads correctly.
• etcd performance and size: etcd is the key-value store that backs Kubernetes state—its health is critical.
• Number of active nodes and pods: Helps you understand resource allocation and workload distribution.

Node metrics

Nodes are the machines (physical or virtual) that run your workloads. Key node-level metrics include:

• CPU usage and throttling: Tracks how much CPU is being used and whether it’s being limited
• Memory usage and pressure: Helps identify memory leaks, limits breaches, or out-of-memory (OOM) kills
• Disk I/O and space utilization: Useful for detecting slow storage or full volumes

• Node conditions (e.g., Ready, DiskPressure, MemoryPressure): Show whether a node is healthy or struggling

Pod and container metrics

These metrics tell you how your workloads (apps and services) are behaving:

• Pod restarts: Frequent restarts usually signal a crash loop or misconfiguration
• Container CPU and memory usage: Tracks resource consumption relative to requests/limits
• Pod phase/status: Indicates if a pod is running, pending, succeeded, or failed.
• Network usage (in/out): Important for diagnosing connectivity issues or bottlenecks
• Readiness and liveness probe failures: Helps detect unresponsive or unhealthy apps

Application-level metrics

While Kubernetes tracks infrastructure, you’ll also want to observe your applications using custom or built-in metrics:

• Request rate, error rate, and latency (a.k.a. the “golden signals”): Essential for monitoring service health.
• Throughput and response time: Particularly for APIs or web services.
• Custom business logic metrics: These vary by application but can include queue length and job execution time.

Autoscaling metrics

If you’re using Horizontal Pod Autoscaling (HPA) or Cluster Autoscaler you should monitor:

• CPU and memory utilization thresholds
• Replica count over time
• Scaling events and delays

Scheduling and resource utilization

To optimize cluster efficiency and cost you can consider:

• Node and pod resource requests vs. actual usage: Helps detect over-provisioned or underutilized resources.
• Pending pods and scheduling delays: Indicates insufficient resources or taints/tolerations issues.
• Quota usage per namespace: Ensures teams stay within allocated limits.

Choosing the best Kubernetes monitoring tools

Your organization has unique business needs, so you’ll need to choose your Kubernetes monitoring tool carefully. Certain features will help you choose the best monitoring tool for Kubernetes for your company.

Scalability

Your monitoring solution should be able to handle dynamic, high-volume environments with thousands of pods and containers. As your cluster grows (or shrinks), the tool should continue to provide reliable performance, low-latency metrics collection and high-resolution data retention. Look for support for multi-cluster setups and auto-discovery of resources to keep up with Kubernetes’ dynamic nature.

Easy integration with Kubernetes

Good open source Kubernetes monitoring tools should offer native integration with Kubernetes, meaning it can:

• Automatically discover new nodes, pods and services
• Pull metrics using Kubernetes APIs and exporters
• Monitor Kubernetes components like kubelet, etcd and the API server
• Bonus points for tools that support Helm charts, Kubernetes operators or easy installation via YAML manifests.

Real-time metrics and dashboards

You need the ability to view live data about resource usage, application health and infrastructure performance. Pre-built dashboards are a plus, but the ability to customize views and queries based on your workloads is essential. Low-latency metrics updates allow you to catch and fix problems fast.

Centralized logging and tracing

Look for a tool that supports log aggregation and distributed tracing, ideally integrated with metrics. This unified observability — metrics, logs and traces in one place — helps reduce the time it takes to investigate and resolve incidents. Tools like Grafana Loki, Jaeger or integrations with OpenTelemetry are excellent examples.

Alerting and incident management

Effective monitoring tools let you define custom alerts and thresholds for key events (e.g., high CPU usage, pod restarts, failed probes). Look for features like:

• Multi-channel alert delivery (email, Slack, PagerDuty, etc.)
• Alert deduplication and noise reduction
• Integration with incident response tools

Resource usage and cost optimization

A good monitoring tool will not only help you track performance but also identify over-provisioned or underutilized resources. This is key for optimizing autoscaling settings and reducing cloud costs. Look for insights into resource requests vs. actual usage, as well as support for visualizing trends over time.

Multi-cluster and multi-cloud support

If you’re running workloads across multiple clusters or cloud providers, make sure the tool can aggregate data across environments. It should offer unified visibility and allow you to compare performance or usage between clusters.

Role-based access control (RBAC) and security

For enterprise environments, monitoring tools should support RBAC so different users or teams can access the data relevant to them. Audit logs, SSO integration and secure API endpoints are also important for maintaining compliance and governance.

Easy setup and maintenance

Time to value is important. A great tool should offer:

• Simple installation options (Helm, Operator, YAML)
• Minimal manual configuration
• Auto-updates or easy version upgrades
• Cloud-native or SaaS-based tools often reduce the maintenance burden compared to self-hosted stacks

SUSE Rancher Prime: Built for fast Kubernetes observability 

Kubernetes observability with SUSE Rancher Prime offers a powerful and centralized way to gain visibility into your Kubernetes environments that goes beyond monitoring with SUSE Observability. Whether you’re managing one cluster or hundreds across hybrid and multi-cloud infrastructure, Rancher Prime allows you to collect, visualize and alert on key metrics like CPU usage, memory consumption, pod health and cluster resource utilization. 

Through its user-friendly UI and automated service discovery, Rancher Prime simplifies the setup of observability tools, making it easy for platform teams to monitor application performance and cluster health at scale. Combined with role-based access controls, multi-cluster views and enterprise-grade support, SUSE Rancher Prime helps organizations confidently run production workloads with deeper insight, faster troubleshooting and enhanced operational efficiency.

Beyond monitoring: Integrated observability with SUSE

At SUSE, we go far beyond basic monitoring. We deliver platform-wide observability—giving teams not just metrics, but context, correlation, and actionable insight across every layer of their Kubernetes environment. From infrastructure to workloads to services, SUSE Observability and SUSE Cloud Observability (SaaS) provide a unified, scalable, and Kubernetes-native experience designed for modern DevOps and platform engineering teams.

SUSE Rancher Prime plays a foundational role by managing multi-cluster environments at scale. But observability across those clusters is elevated through native integrations and extended capabilities offered by the SUSE Observability suite.

These integrations reduce complexity, accelerate issue resolution, and support proactive operations by unifying monitoring, tracing, logging, and event correlation—all in one experience. The result is less time troubleshooting, more time delivering value.

Prometheus and Grafana integrations

While SUSE Cloud Observability extends deep into OpenTelemetry-based tracing, time-travel debugging, and guided root cause analysis, Prometheus and Grafana remain core building blocks—seamlessly integrated into Rancher Prime environments:

Prometheus

Prometheus is the de facto standard for Kubernetes metrics and alerting. In Rancher Prime, it’s easily deployed across clusters for scraping and storing time-series data. It powers resource usage monitoring, performance alerting, and capacity planning—customizable via PromQL and integrated with Rancher’s alerting system.

Grafana

Grafana offers interactive, real-time dashboards built on Prometheus data and more. Within Rancher Prime, it enables out-of-the-box visibility with customizable dashboards for applications, workloads, and infrastructure metrics. Its plugin ecosystem makes it easy to visualize data from multiple sources—integrating observability across your stack.

Observability built for the enterprise

Together, SUSE Rancher Prime and SUSE Observability offer a comprehensive solution that empowers teams to detect, investigate and resolve issues faster—across containers, virtual machines, and hybrid cloud environments.

This is more than monitoring. This is enterprise observability built into your cloud-native platform.

How SUSE supports your Kubernetes goals 

Kubernetes has transformed how organizations build and run applications, but its dynamic, distributed nature makes observability more critical—and more challenging—than ever. Choosing the right monitoring tools is essential for gaining visibility into your clusters, keeping workloads healthy, and ensuring reliable, performant applications. Whether you’re just getting started with Kubernetes or managing large-scale production environments, monitoring should be a foundational part of your infrastructure strategy.

Ultimately, the best Kubernetes monitoring solution is one that fits your organization’s scale, complexity, and operational style. Look for tools that offer real-time metrics, automated discovery, seamless Kubernetes integration, and the ability to unify logs and traces for full-stack observability. With the right monitoring setup in place, your team will be empowered to move faster, respond to issues more efficiently, and run Kubernetes with confidence.

To learn more about managing Kubernetes, check out SUSE’s cloud-native container management solutions. 

FAQs on Kubernetes monitoring tools

Why is Kubernetes monitoring important?

Kubernetes monitoring is essential because it provides visibility into the health, performance, and resource usage of your containerized applications and infrastructure. In dynamic environments where workloads constantly scale and shift, monitoring helps teams detect issues early, troubleshoot faster, and optimize resource allocation. Without proper monitoring, it’s easy to miss failures, performance bottlenecks, or inefficient configurations—putting reliability, security, and user experience at risk. Effective monitoring ensures your Kubernetes clusters run smoothly, remain resilient, and support continuous delivery with confidence.

How is Kubernetes monitoring different from traditional monitoring?

Kubernetes monitoring differs from traditional monitoring in that it must track highly dynamic, short-lived resources like pods and containers, rather than static servers or VMs. Traditional monitoring focuses on host-based metrics, but Kubernetes monitoring requires visibility across multiple layers—including clusters, nodes, services, and microservices—often with automated discovery and real-time updates. It’s also more focused on application performance, service-to-service communication, and infrastructure orchestration, making it essential for managing modern, cloud-native environments.

Which is the best Kubernetes monitoring tool?

The best Kubernetes monitoring tool isn’t necessarily a single product, but rather the one that offers the right set of features for your environment and operational needs. A strong monitoring solution should include real-time metrics collection, automated service discovery, and support for multi-cluster environments. It should also offer easy integration with Kubernetes, customizable dashboards, alerting capabilities, and ideally, unified observability — combining metrics, logs, and traces in one interface. Tools that scale effortlessly, support RBAC, and integrate with your existing DevOps workflows will provide the greatest long-term value and performance visibility.