As enterprises increasingly adopt hybrid cloud architectures, monitoring systems need to evolve to provide unified visibility across both on-premises infrastructure and cloud resources. Prometheus has become the de facto standard for monitoring containerized workloads and Kubernetes clusters, while AWS CloudWatch offers native monitoring for AWS services. Integrating Prometheus with CloudWatch enables organizations to leverage the strengths of both systems for comprehensive hybrid cloud observability.

In this post, we’ll explore the technical nuances of integrating Prometheus with AWS CloudWatch, focusing on metrics ingestion, architecture considerations, and best practices for scalable hybrid cloud monitoring.

Why Integrate Prometheus with AWS CloudWatch

Prometheus excels at scraping time-series metrics from Kubernetes and custom exporters, providing powerful querying and alerting capabilities. However, Prometheus lacks native insight into AWS-managed services such as RDS, ELB, or Lambda functions. Conversely, CloudWatch specializes in collecting AWS service metrics but offers limited scope outside the AWS ecosystem.

By integrating Prometheus with CloudWatch, you can:

  • Consolidate metrics from Kubernetes, on-prem, and AWS services in a single Prometheus instance.
  • Extend alerting and visualization capabilities using Prometheus-native tools like Alertmanager and Grafana.
  • Achieve consistent monitoring workflows across hybrid environments.

Architecture Overview for Prometheus and CloudWatch Integration

A common integration pattern involves the use of a CloudWatch Exporter — an open-source Prometheus exporter that scrapes AWS CloudWatch metrics and exposes them in Prometheus format. Here’s a high-level flow:

  1. CloudWatch Exporter periodically queries AWS CloudWatch API for configured namespaces and metrics.
  2. The exporter converts CloudWatch metrics into Prometheus-compatible metrics.
  3. Prometheus scrapes the CloudWatch Exporter alongside other exporters and Kubernetes targets.
  4. Metrics are stored in Prometheus TSDB and used for alerting or visualization.

This architecture ensures minimal disruption to existing Prometheus deployments while enabling AWS metrics ingestion.

Setting Up CloudWatch Exporter

To deploy the CloudWatch Exporter:

  1. Obtain the exporter from the official prometheus/cloudwatch_exporter repository.
  2. Create an IAM Role with cloudwatch:GetMetricData and cloudwatch:ListMetrics permissions, attached to the EC2 or Kubernetes node running the exporter.
  3. Configure the config.yml with AWS namespaces and metrics to scrape. Example configuration snippet:
region: us-east-1
metrics:
  - aws_namespace: AWS/EC2
    aws_metric_name: CPUUtilization
    aws_dimensions: [InstanceId]
    aws_statistics: [Average]
  1. Deploy the exporter as a standalone service or sidecar container.
  2. Add the exporter endpoint to your Prometheus scrape_configs for metric collection:
scrape_configs:
  - job_name: 'cloudwatch'
    static_configs:
      - targets: ['cloudwatch-exporter:9106']

Handling Large Scale and Cost Considerations

CloudWatch API has rate limits and cost implications based on GetMetricData calls. To optimize:

  • Aggregate metrics where possible using CloudWatch statistics (Sum, Average).
  • Limit the number of metrics and dimensions scraped to reduce API calls.
  • Use metric filtering and caching within the CloudWatch Exporter.
  • Schedule scraping intervals thoughtfully (e.g., 1m or 5m) balancing freshness and cost.
  • Monitor AWS billing for API usage spikes.

Enhancing Observability with Prometheus and CloudWatch Metrics

Once integrated, you can:

  • Create unified Grafana dashboards combining Kubernetes pod metrics with AWS service health.
  • Use PromQL queries to correlate application metrics with infrastructure metrics.
  • Implement cross-environment alerting that triggers on anomalies detected in either environment.
  • Leverage Prometheus federation to aggregate metrics across multiple clusters or regions, including AWS.

Best Practices for Secure and Scalable Integration

  • Use IAM roles with least privilege to restrict CloudWatch Exporter permissions.
  • Secure exporter endpoints via network policies or authentication proxies.
  • Regularly update the exporter to handle API changes and improvements.
  • Monitor exporter logs for scraping errors or AWS API throttling.
  • Consider using Thanos or Cortex to scale Prometheus storage and enable global querying for large hybrid setups.

Conclusion

Integrating Prometheus with AWS CloudWatch is a powerful approach to achieve unified hybrid cloud monitoring. By leveraging the CloudWatch Exporter, organizations can bridge metrics from AWS-managed services into Prometheus, enhancing observability and operational insights. Following best practices around configuration, security, and cost optimization ensures a scalable and maintainable monitoring solution across diverse infrastructure landscapes.

Unlock the full potential of your hybrid cloud environment with this integration, enabling proactive monitoring and faster incident response through consolidated metric analysis.