Top 10 Kubernetes Monitoring & Observability Tools

Updated on Jul 4, 2025 7 min read grafana kubernetes logging metrics monitoring observability prometheus

Effective monitoring and observability are critical for running Kubernetes clusters in production. The cloud-native ecosystem offers a rich set of tools for collecting metrics, visualizing data, and gaining insights into cluster and application performance. Here are the top 10 monitoring and observability tools that every Kubernetes operator should know.

1. Prometheus - The Metrics Foundation

The de facto standard for collecting metrics in Kubernetes clusters.

Prometheus is the cornerstone of Kubernetes monitoring, providing a powerful time-series database and query language for collecting and analyzing metrics. Its pull-based architecture and service discovery make it ideal for dynamic container environments.

Key Features:

Time-series data collection
Powerful query language (PromQL)
Service discovery integration
Alerting capabilities
High availability support

Installation:

# Using Helm
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install prometheus prometheus-community/kube-prometheus-stack

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/prometheus-operator/kube-prometheus/main/manifests/setup/
kubectl apply -f https://raw.githubusercontent.com/prometheus-operator/kube-prometheus/main/manifests/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod

Learn more about Prometheus

2. Grafana - Visualization and Dashboards

Visualization layer commonly paired with Prometheus and Loki.

Grafana transforms raw metrics data into actionable insights through beautiful dashboards and visualizations. It supports multiple data sources and provides powerful querying capabilities.

Key Features:

Rich dashboard creation
Multiple data source support
Alerting and notifications
User management and permissions
Plugin ecosystem

Installation:

# Using Helm
helm repo add grafana https://grafana.github.io/helm-charts
helm install grafana grafana/grafana

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/grafana/helm-charts/main/charts/grafana/templates/deployment.yaml

Dashboard Configuration:

apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-dashboards
data:
  kubernetes-cluster.json: |
    {
      "dashboard": {
        "title": "Kubernetes Cluster",
        "panels": [
          {
            "title": "CPU Usage",
            "type": "graph",
            "targets": [
              {
                "expr": "sum(rate(container_cpu_usage_seconds_total{container!=\"\"}[5m]))"
              }
            ]
          }
        ]
      }
    }

Explore Grafana

3. Loki - Log Aggregation System

A log aggregation system by Grafana Labs, designed to integrate with Prometheus.

Loki provides efficient log aggregation and querying capabilities, designed to work seamlessly with Prometheus and Grafana. It’s optimized for Kubernetes environments and provides cost-effective log storage.

Key Features:

Efficient log storage
PromQL-like query language (LogQL)
Kubernetes-native design
Cost-effective scaling
Integration with Grafana

Installation:

# Using Helm
helm repo add grafana https://grafana.github.io/helm-charts
helm install loki grafana/loki

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/grafana/loki/main/production/helm/loki/templates/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: loki-config
data:
  loki.yaml: |
    auth_enabled: false
    server:
      http_listen_port: 3100
    ingester:
      lifecycler:
        address: 127.0.0.1
        ring:
          kvstore:
            store: inmemory
          replication_factor: 1
        final_sleep: 0s
      chunk_idle_period: 5m
      chunk_retain_period: 30s
    schema_config:
      configs:
        - from: 2020-05-15
          store: boltdb-shipper
          object_store: filesystem
          schema: v11
          index:
            prefix: index_
            period: 24h

Discover Loki

4. Thanos - Long-term Metrics Storage

Long-term, highly available Prometheus setup for global metrics.

Thanos extends Prometheus with long-term storage capabilities and global querying across multiple clusters. It’s essential for organizations running multiple Kubernetes clusters or requiring long-term metric retention.

Key Features:

Long-term metric storage
Global querying
High availability
Multi-cluster support
Cost-effective storage

Installation:

# Using Helm
helm repo add thanos https://charts.bitnami.com/bitnami
helm install thanos thanos/thanos

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/thanos-io/thanos/main/examples/k8s/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: thanos-query-config
data:
  thanos-query.yaml: |
    type: s3
    config:
      bucket: "thanos-metrics"
      endpoint: "s3.amazonaws.com"
      access_key: "your-access-key"
      secret_key: "your-secret-key"

Learn about Thanos

5. VictoriaMetrics - High-performance Alternative

Fast, scalable alternative to Prometheus with long-term storage.

VictoriaMetrics provides a high-performance, cost-effective alternative to Prometheus with built-in long-term storage and enhanced query performance.

Key Features:

High-performance storage
Built-in long-term retention
Prometheus compatibility
Cost-effective scaling
Enhanced query performance

Installation:

# Using Helm
helm repo add vm https://victoriametrics.github.io/helm-charts/
helm install victoria-metrics vm/victoria-metrics-single

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/VictoriaMetrics/helm-charts/main/charts/victoria-metrics-single/templates/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: victoria-metrics-config
data:
  victoria-metrics.yaml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod
        relabel_configs:
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
            action: keep
            regex: true

Explore VictoriaMetrics

6. OpenTelemetry Collector - Unified Observability

Foundation for tracing and metrics instrumentation.

OpenTelemetry Collector provides a unified approach to collecting traces, metrics, and logs. It’s becoming the standard for observability data collection in cloud-native environments.

Key Features:

Unified data collection
Multiple format support
Flexible processing
Vendor-agnostic
High performance

Installation:

# Using Helm
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm install otel-collector open-telemetry/opentelemetry-collector

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/open-telemetry/opentelemetry-helm-charts/main/charts/opentelemetry-collector/templates/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: otel-collector-config
data:
  config.yaml: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
    
    processors:
      batch:
        timeout: 1s
        send_batch_size: 1024
    
    exporters:
      prometheus:
        endpoint: "0.0.0.0:9464"
      otlp:
        endpoint: "tempo:4317"
        tls:
          insecure: true
    
    service:
      pipelines:
        traces:
          receivers: [otlp]
          processors: [batch]
          exporters: [otlp]
        metrics:
          receivers: [otlp]
          processors: [batch]
          exporters: [prometheus]

Learn about OpenTelemetry

7. kube-state-metrics - Cluster State Metrics

Exposes cluster state as metrics for Prometheus.

kube-state-metrics translates Kubernetes objects into Prometheus metrics, providing insights into cluster state, resource usage, and object lifecycle.

Key Features:

Kubernetes object metrics
Resource state tracking
Custom resource support
Prometheus integration
Real-time updates

Installation:

# Using Helm
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install kube-state-metrics prometheus-community/kube-state-metrics

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/kubernetes/kube-state-metrics/main/examples/standard/

Configuration Example:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: kube-state-metrics
spec:
  replicas: 1
  selector:
    matchLabels:
      app: kube-state-metrics
  template:
    metadata:
      labels:
        app: kube-state-metrics
    spec:
      containers:
      - name: kube-state-metrics
        image: registry.k8s.io/kube-state-metrics/kube-state-metrics:v2.8.0
        ports:
        - name: metrics
          containerPort: 8080
        livenessProbe:
          httpGet:
            path: /healthz
            port: 8080
          initialDelaySeconds: 5
          timeoutSeconds: 5
        readinessProbe:
          httpGet:
            path: /
            port: 8080
          initialDelaySeconds: 5
          timeoutSeconds: 5

Get kube-state-metrics on GitHub

8. Metrics Server - Resource Usage Metrics

Lightweight aggregator of resource usage for HPA and dashboarding.

Metrics Server provides core resource usage metrics (CPU and memory) for Kubernetes objects, enabling Horizontal Pod Autoscaler (HPA) and resource monitoring dashboards.

Key Features:

Resource usage metrics
HPA support
Lightweight design
Real-time data
Kubernetes-native

Installation:

# Using kubectl
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

# Using Helm
helm repo add metrics-server https://kubernetes-sigs.github.io/metrics-server/
helm install metrics-server metrics-server/metrics-server

Configuration Example:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: metrics-server
  namespace: kube-system
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: metrics-server
  namespace: kube-system
spec:
  replicas: 1
  selector:
    matchLabels:
      app: metrics-server
  template:
    metadata:
      labels:
        app: metrics-server
    spec:
      serviceAccountName: metrics-server
      containers:
      - name: metrics-server
        image: registry.k8s.io/metrics-server/metrics-server:v0.6.4
        args:
        - --kubelet-insecure-tls
        - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
        ports:
        - name: main-port
          containerPort: 4443

Get Metrics Server on GitHub

9. Kubewatch - Real-time Notifications

Real-time resource change notifications via Slack or webhooks.

Kubewatch monitors Kubernetes events and sends real-time notifications to various channels, helping teams stay informed about cluster changes and potential issues.

Key Features:

Real-time event monitoring
Multiple notification channels
Customizable filters
Webhook support
Slack integration

Installation:

# Using Helm
helm repo add kubewatch https://charts.bitnami.com/bitnami
helm install kubewatch kubewatch/kubewatch

# Using kubectl
kubectl apply -f https://raw.githubusercontent.com/bitnami-labs/kubewatch/main/deploy/

Configuration Example:

apiVersion: v1
kind: ConfigMap
metadata:
  name: kubewatch-config
data:
  .kubewatch.yaml: |
    handler:
      slack:
        token: "your-slack-token"
        channel: "#kubernetes"
        title: "Kubernetes Event"
    
    resources:
      - deployment
      - pod
      - service
    
    events:
      - create
      - update
      - delete

Get Kubewatch on GitHub

10. Weave Scope - Visual Cluster Exploration

Visualizes processes, containers, and services in real-time.

Weave Scope provides a visual interface for exploring and monitoring Kubernetes clusters, making it easier to understand application topology and troubleshoot issues.

Key Features:

Visual cluster exploration
Real-time topology mapping
Container and process monitoring
Interactive debugging
Performance insights

Installation:

# Using kubectl
kubectl apply -f "https://cloud.weave.works/k8s/scope.yaml?k8s-version=$(kubectl version | base64 | tr -d '\n')"

# Using Helm
helm repo add weaveworks https://weaveworks.github.io/helm-charts
helm install weave-scope weaveworks/weave-scope

Configuration Example:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: weave-scope-app
  namespace: weave
spec:
  replicas: 1
  selector:
    matchLabels:
      app: weave-scope-app
  template:
    metadata:
      labels:
        app: weave-scope-app
    spec:
      containers:
      - name: scope
        image: weaveworks/scope:latest
        ports:
        - containerPort: 4040
        env:
        - name: WEAVE_SCOPE_DISCOVERY_URL
          value: "weave-scope-app.weave.svc.cluster.local:4040"

Explore Weave Scope

Building a Complete Monitoring Stack

Recommended Architecture

Metrics Collection: Prometheus + kube-state-metrics + Metrics Server
Logging: Loki + Promtail
Tracing: Jaeger or Zipkin with OpenTelemetry
Visualization: Grafana
Alerting: Prometheus Alertmanager + Grafana Alerts
Long-term Storage: Thanos or VictoriaMetrics

Implementation Steps

Start with Core Metrics: Deploy Prometheus and kube-state-metrics
Add Visualization: Install Grafana and create basic dashboards
Implement Logging: Deploy Loki and configure log collection
Set Up Alerting: Configure Alertmanager with meaningful alerts
Add Tracing: Implement OpenTelemetry for distributed tracing
Scale and Optimize: Add long-term storage and optimize performance

Best Practices

Resource Planning: Allocate sufficient resources for monitoring components
Security: Implement proper RBAC and network policies
Retention Policies: Configure appropriate data retention periods
Alert Fatigue: Design meaningful alerts to avoid notification overload
Documentation: Maintain clear documentation for dashboards and alerts
Testing: Regularly test monitoring and alerting systems

Conclusion

A comprehensive monitoring and observability strategy is essential for running Kubernetes clusters in production. The tools outlined above provide the foundation for understanding cluster health, application performance, and user experience.

Start with the core tools (Prometheus, Grafana, kube-state-metrics) and gradually expand your observability stack based on your specific needs. Remember that effective monitoring is not just about collecting data—it’s about providing actionable insights that help you maintain reliable, performant applications.

For organizations running multiple clusters or requiring enterprise-grade features, consider managed solutions that build upon these open-source tools while providing additional features like global aggregation, advanced analytics, and professional support.

For more information about Kubernetes monitoring and observability, visit the official Kubernetes documentation and the CNCF observability landscape.