As cloud-native architectures grow increasingly complex, monitoring multiple Kubernetes clusters simultaneously becomes a critical challenge. Traditional single-cluster Prometheus setups fall short when scaling across multi-region or multi-cloud deployments. In this blog post, we’ll explore how to configure Prometheus for multi-cluster monitoring, leveraging cloud-native tools and best practices to achieve scalable observability. This guide targets intermediate to advanced users looking to enhance their monitoring strategy with robust, multi-cluster Prometheus configurations.

Why Multi-Cluster Monitoring Matters

Modern enterprises often deploy applications across multiple Kubernetes clusters for high availability, disaster recovery, and geo-distribution. Monitoring these clusters individually leads to fragmented metrics, inconsistent alerts, and operational overhead. A unified Prometheus setup enables:

  • Centralized visibility across clusters
  • Cross-cluster correlation of metrics
  • Simplified alerting and dashboarding
  • Efficient resource utilization and cost management

Understanding the technical implications and configuration nuances is essential to implement a scalable, maintainable multi-cluster monitoring infrastructure.

Core Approaches to Multi-Cluster Prometheus Architecture

There are several architectural patterns to consider:

  1. Federation
    Prometheus federation aggregates metrics from multiple cluster-local Prometheus instances into a central Prometheus server. This approach is simple and scalable but can introduce latency and increased network load.

  2. Remote Write
    Cluster-local Prometheus instances push metrics to a centralized long-term storage or a global Prometheus instance via the remote_write API. This offers better scalability and reliability, especially when paired with scalable backends like Thanos or Cortex.

  3. Multi-Tenant Monitoring Systems
    Solutions like Thanos or Cortex extend Prometheus to support global queries, high availability, and deduplication across clusters.

In this post, we will focus on a hybrid approach combining federation and remote write to balance real-time querying and long-term storage needs.

Prerequisites and Assumptions

  • Kubernetes clusters with Prometheus Operator or kube-prometheus-stack installed
  • Basic knowledge of Prometheus configuration and Kubernetes CRDs
  • Access to a cloud environment with networking configured for inter-cluster communication
  • Familiarity with TLS and authentication mechanisms for secure metric scraping

Step 1 Setting up Cluster-Local Prometheus Instances

Each Kubernetes cluster should run its own Prometheus instance to locally scrape metrics from pods and nodes.

  • Use the Prometheus Operator for easier management and automatic service discovery.
  • Configure service monitors and pod monitors to target cluster workloads.
  • Tune retention policies based on resource availability and data importance.

Example snippet for a ServiceMonitor targeting application metrics:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: app-metrics
  labels:
    release: prometheus
spec:
  selector:
    matchLabels:
      app: my-application
  endpoints:
  - port: metrics
    interval: 30s

Step 2 Configuring Federation for Central Aggregation

Set up a central Prometheus instance to scrape cluster-local Prometheus endpoints using federation:

  • Expose the /federate endpoint on cluster-local Prometheus instances with restricted access.
  • Use federate scrape configs on the central Prometheus to selectively retrieve metrics.

Example federation scrape config:

scrape_configs:
- job_name: 'federate-cluster1'
  honor_labels: true
  metrics_path: '/federate'
  params:
    'match[]':
      - '{job="kubernetes-nodes"}'
      - '{job="kubernetes-pods"}'
  static_configs:
    - targets:
      - 'prometheus-cluster1.example.com:9090'

Security note: Protect federation endpoints with mTLS or authentication proxies to prevent unauthorized access.

Step 3 Implementing Remote Write for Scalable Long-Term Storage

To complement federation, configure each cluster-local Prometheus to push metrics via remote_write:

  • Choose a scalable backend like Thanos Receive, Cortex, or a managed service (e.g., Grafana Cloud).
  • Use remote_write to send data asynchronously, reducing scraping load on the central server.
  • Apply relabeling to filter or annotate metrics before transmission.

Example remote_write config:

remote_write:
- url: https://thanos-receive.example.com/api/v1/receive
  bearer_token_file: /var/run/secrets/token
  queue_config:
    max_shards: 10
  write_relabel_configs:
  - source_labels: [__name__]
    regex: 'container_cpu_usage_seconds_total|container_memory_usage_bytes'
    action: keep

Step 4 Securing Inter-Cluster Communication

Security is paramount when scraping or pushing metrics across clusters:

  • Use TLS encryption for all Prometheus endpoints and remote write URLs.
  • Employ mutual TLS (mTLS) to authenticate Prometheus servers.
  • Leverage Kubernetes secrets or cloud KMS to manage certificates and tokens.
  • Restrict network policies and firewall rules to limit access.

Step 5 Querying and Visualization

With multi-cluster data centralized, leverage tools like Grafana for cross-cluster dashboards:

  • Configure datasources for the central Prometheus or Thanos Querier.
  • Use labels such as cluster or region to filter and compare metrics.
  • Create alerting rules that span clusters for correlated incident detection.

Example PromQL query comparing pod CPU usage across clusters:

sum(rate(container_cpu_usage_seconds_total{cluster=~"cluster1|cluster2"}[5m])) by (cluster)

Best Practices and Performance Considerations

  • Label consistency: Ensure uniform labeling across clusters for easier aggregation.
  • Metric cardinality: Avoid high-cardinality metrics that can overwhelm storage and querying.
  • Retention policies: Balance local short-term retention with centralized long-term storage.
  • Thanos/Cortex: Consider these projects for enhanced scalability, HA, and global querying.
  • Monitoring Prometheus: Monitor Prometheus instances themselves to detect scrape failures or overload.

Conclusion

Configuring Prometheus for multi-cluster monitoring in cloud environments requires a thoughtful architecture combining federation, remote write, and secure networking. By following the outlined steps, you can achieve scalable, reliable, and unified observability across your distributed Kubernetes clusters. This approach not only improves operational insight but also lays a foundation for advanced analytics and proactive incident response in complex cloud-native deployments.

Start implementing these strategies today to elevate your monitoring stack and gain comprehensive visibility across all your cloud clusters.