Why your bare metal kubernetes cluster needs better monitoring tools

Monitoring tools are among the most overlooked components when it comes to bare metal Kubernetes deployments, despite their critical importance to infrastructure stability. 

Unlike cloud-based Kubernetes environments, bare metal clusters lack built-in monitoring capabilities, creating significant observability challenges. 

When running Kubernetes on dedicated servers, you need specialized solutions like Prometheus and Grafana to track everything from hardware metrics to application performance. 

This article examines why traditional monitoring approaches fall short for bare metal environments, identifies the critical metrics you need to track, and evaluates the top monitoring solutions for 2025. 

Reasons Why Monitoring is More Critical in Bare Metal Clusters

No built-in cloud monitoring support

In cloud environments, providers automatically integrate monitoring capabilities into their Kubernetes services. However, bare metal clusters operate without these built-in safety nets. 

This absence creates immediate visibility gaps that must be addressed through the use of third-party monitoring tools.

Kubernetes architecture itself provides only basic monitoring through components like kubelet and kube-proxy. These components handle fundamental operations such as ensuring containers run in pods and maintaining network rules on nodes, but they don't offer comprehensive monitoring capabilities. 

Kubelet verifies that containers described in PodSpecs are running and healthy, while kube-proxy maintains network rules for pod communication. Both components function primarily as operational tools rather than monitoring solutions.

Consequently, administrators must implement comprehensive monitoring stacks, such as Prometheus and Grafana, to gain visibility into cluster performance. These tools fill the gap left by the absence of cloud provider monitoring, collecting metrics from multiple layers of the infrastructure.

Risk of hardware failures

In a way, physical hardware introduces possible failure points that don't exist in virtualized environments. Each server component (processors, memory modules, storage devices, network interfaces) represents a potential point of failure that requires monitoring.

Kubernetes' self-healing capabilities partially address this issue by restarting failed containers and replacing unhealthy pods. 

As the official documentation states, "Kubernetes restarts containers that fail, replaces containers, kills containers that don't respond to your user-defined health check, and doesn't advertise them to clients until they are ready to serve." 

However, these mechanisms operate at the container level, not the hardware level.

To mitigate hardware risks, bare metal environments need monitoring tools that track:

• CPU temperature and utilization

• Memory errors and allocation

• Disk health metrics and I/O performance

• Network interface errors and throughput

Without these insights, hardware issues can cascade into application failures before Kubernetes' self-healing mechanisms have a chance to respond. 

This is particularly important because hardware failures in bare metal environments often require physical intervention, making early detection crucial.

Manual provisioning increases complexity

Setting up a bare metal Kubernetes cluster involves numerous manual steps, each introducing potential configuration errors. The network configuration alone requires careful setup of CNI plugins, service proxying, and NetworkPolicy implementations.

Additionally, bare metal environments often utilize specialized Gateway API implementations that are explicitly focused on "bare metal" environments, rather than cloud environments. These implementations require proper configuration and monitoring to ensure they function correctly.

Manual provisioning also means administrators must configure and maintain multiple components that would be automatically managed in cloud environments. This includes setting up the Container Runtime Interface, configuring storage systems, and establishing proper network rules.

Moreover, the variety of potential configurations increases the monitoring surface area. Each custom configuration choice (from networking plugins to storage systems) needs specific monitoring considerations. Node exporters become essential for collecting hardware-level metrics, while kube-state-metrics provides visibility into the status of Kubernetes objects.

All these factors make comprehensive monitoring not just beneficial but absolutely essential for bare metal Kubernetes deployments.

Understanding the Gaps in Default Kubernetes Monitoring

What kube-proxy and kubelet do (and do not)

The kubelet works as an agent running on each node, ensuring containers operate within their designated pods. It takes PodSpecs through various mechanisms and verifies that the containers described in those specifications are running and healthy. 

Essentially, kubelet functions as a container supervisor, not a monitoring solution. It doesn't track performance metrics or resource utilization trends over time, nor does it provide alerting capabilities.

Meanwhile, kube-proxy serves as a network proxy on each node, implementing part of the Kubernetes Service concept. It maintains network rules that allow network communication to pods from inside or outside the cluster.

Kube-proxy uses the operating system's packet filtering layer when available or forwards traffic itself. Although vital for networking, kube-proxy doesn't offer insights into network performance, latency issues, or throughput bottlenecks.

Why logs alone aren't enough

Firstly, logs are reactive rather than proactive. They tell you what has already happened, not what might happen soon. Secondly, logs typically lack context about system resource utilization, making it challenging to correlate application issues with infrastructure problems.

Thirdly, the distributed nature of Kubernetes creates a fragmented logging experience. Pods can be rescheduled across nodes, containers can restart, and logs can disappear with them unless you've implemented a centralized logging solution.

To clarify, logs serve as just one piece of the monitoring puzzle. A comprehensive monitoring strategy requires metrics collection, alerting, visualization, and analysis capabilities that default Kubernetes components simply don't provide.

Key Areas That Require Better Monitoring

Effective monitoring of bare metal Kubernetes clusters requires focusing on five critical areas that often receive insufficient attention. 

Understanding these crucial monitoring domains enables administrators to implement comprehensive observability solutions tailored to the specific needs of physical infrastructure.

Node health and hardware metrics

Successful bare metal deployments depend on monitoring physical server components that cloud-based solutions typically abstract away. Node health monitoring should track CPU temperature, memory errors, disk health metrics, and network interface errors. 

These hardware-level metrics require specialized exporters since Kubernetes itself primarily focuses on container orchestration rather than hardware management.

Pod-level resource usage

Pod resource monitoring goes beyond simple CPU and memory snapshots. Effective monitoring tracks resource allocation versus actual usage, helping identify both overprovisioned and resource-starved containers.

This becomes particularly important in bare metal environments where physical resources are finite and must be carefully managed.

Resource usage monitoring should capture:

• Container CPU throttling events

• Memory pressure indicators

• Quality of Service (QoS) class violations

• Network bandwidth consumption per pod

Kubernetes' Pod Quality of Service Classes determine how pods are treated during resource contention, making their monitoring essential for understanding application behavior under pressure.

Network traffic and latency

Network monitoring in bare metal Kubernetes involves multiple layers. The Container Networking Interface (CNI) plugins handle pod networking, while kube-proxy maintains network rules for service communication. Monitoring must cover both infrastructure and service-level metrics.

Critical network metrics include pod-to-pod latency, service response times, and the effectiveness of network policies. Monitoring tools should also track EndpointSlice objects, which Kubernetes uses to manage service backends and update the list of healthy endpoints.

Notably, bare metal environments often implement specialized Gateway APIs explicitly designed for physical infrastructure, requiring additional monitoring considerations compared to cloud environments.

Security and access logs

Security monitoring for bare metal clusters must cover both Kubernetes API access and container-level activities. Without cloud provider security boundaries, comprehensive logging becomes crucial for detecting unauthorized access attempts.

Effective security monitoring encompasses authentication mechanisms, role-based access control (RBAC) events, and Network Policy enforcement. Tracking pod security standard violations helps prevent privilege escalation attacks unique to container environments.

Storage performance

Storage monitoring for bare metal Kubernetes encompasses both physical disks and abstract storage concepts like PersistentVolumes. Administrators must track I/O performance, volume utilization, and storage capacity across the cluster.

Volume health monitoring becomes particularly important for workloads with specific storage requirements. Tools should capture metrics for storage classes, dynamic provisioning events, and CSI (Container Storage Interface) operations to provide complete visibility into the storage subsystem.

Altogether, these five monitoring domains form the foundation of comprehensive observability for bare metal Kubernetes deployments. By implementing tools that address these specific areas, administrators can maintain a reliable infrastructure while maximizing the benefits of running Kubernetes on dedicated hardware.

Top Monitoring Tools for Bare Metal Kubernetes in 2025

The monitoring landscape for Kubernetes has evolved significantly, with specialized tools now addressing the unique challenges of bare metal deployments. Here’s our compilation of the most effective solutions for 2025.

Prometheus + Grafana

This powerful combination serves as the foundation for most Kubernetes monitoring stacks. Prometheus excels at collecting and storing metrics, while Grafana provides visualization capabilities through customizable dashboards. 

Together, they deliver comprehensive visibility into both container and node-level metrics. Their strength lies in the extensive exporter ecosystem that collects metrics from virtually any system component.

OpenTelemetry

As an observability framework rather than a single tool, OpenTelemetry standardizes the collection of traces, metrics, and logs. This vendor-neutral approach prevents vendor lock-in and provides consistency across heterogeneous environments. 

For bare metal clusters, its flexibility enables monitoring both legacy and containerized workloads through a unified pipeline.

VictoriaMetrics

Designed specifically for high-performance time-series data storage, VictoriaMetrics requires significantly fewer resources than traditional solutions. Its architecture makes it particularly suitable for bare metal environments where efficient resource utilization is crucial.

Thanos

Addressing Prometheus' scaling limitations, Thanos enables long-term storage and global querying across multiple Prometheus instances. This capability is essential for large bare metal deployments spanning multiple data centers.

Fluent Bit

Though primarily a log processor, Fluent Bit complements metrics-focused tools by collecting, parsing, and forwarding logs with minimal resource consumption. Its lightweight design makes it ideal for resource-sensitive environments.

Observability on Bare Metal with Latitude.sh

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If you want to learn more about setting up observability on your Kubernetes infrastructure, Latitude.sh offers a comprehensive guide on implementing the powerful combination of Prometheus and Grafana.

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