PTP Solves: Automating Cloud Monitoring with Terraform
In today’s cloud-native environments, monitoring is not optional—it’s essential. Whether running production workloads on AWS, Azure, or a hybrid setup, implementing a reliable monitoring system is critical for uptime, performance, and cost control.
However, manually configuring monitoring tools across cloud services can be time-consuming and error-prone. That’s where Terraform steps in.
In this blog, we’ll walk you through creating Terraform code to implement monitoring, using examples for AWS CloudWatch, Datadog, and Prometheus. By managing observability as infrastructure as code, you can maintain consistency, scalability, and complete version control.
Why Use Terraform for Monitoring?
Terraform, a popular open-source infrastructure as code (IaC) tool by HashiCorp, allows you to provision and manage infrastructure across multiple cloud platforms using declarative configuration files.
By using Terraform for monitoring, you get:
- Repeatable, version-controlled setup
- Environment consistency across dev/staging/prod
- Faster provisioning with automation
- Reduced risk of misconfiguration
Example 1: Set Up AWS CloudWatch with Terraform
Here’s a quick example of Terraform code to set up a CloudWatch metric alarm:
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provider “aws” {
region = “us-east-1”
}
resource “aws_cloudwatch_metric_alarm” “high_cpu_alarm” {
alarm_name = “HighCPUUtilization”
comparison_operator = “GreaterThanThreshold”
evaluation_periods = 2
metric_name = “CPUUtilization”
namespace = “AWS/EC2”
period = 120
statistic = “Average”
threshold = 80
alarm_description = “This metric monitors high CPU usage”
alarm_actions = [“arn:aws:sns:us-east-1:123456789012:NotifyMe”]
dimensions = {
InstanceId = “i-0123456789abcdef0”
}
}
This configuration sets an alarm to notify you when CPU usage exceeds 80% for over two 2-minute periods.
Example 2: Integrate Datadog Monitoring with Terraform
Datadog provides a Terraform provider for full-stack observability. Here’s how to configure a basic monitor:
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provider “datadog” {
api_key = var.datadog_api_key
app_key = var.datadog_app_key
}
resource “datadog_monitor” “cpu_high” {
name = “High CPU Alert”
type = “metric alert”
query = “avg(last_5m):avg:system.cpu.user{host:your-host} > 80”
message = “CPU usage is too high on {{host.name}}”
escalation_message = “Please investigate immediately!”
tags = [“env:production”]
thresholds {
critical = 80
}
}
With just a few lines of code, critical infrastructure metrics can be monitored automatically.
Example 3: Deploy Prometheus + Grafana via Terraform
For self-hosted environments, Prometheus + Grafana is a popular combo. You can deploy both on Kubernetes using Terraform in conjunction with Helm:
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provider “helm” {
kubernetes {
config_path = “~/.kube/config”
}
}
resource “helm_release” “prometheus” {
name = “prometheus”
repository = “https://prometheus-community.github.io/helm-charts”
chart = “prometheus”
version = “25.0.0”
namespace = “monitoring”
create_namespace = true
}
resource “helm_release” “grafana” {
name = “grafana”
repository = “https://grafana.github.io/helm-charts”
chart = “grafana”
version = “7.3.0”
namespace = “monitoring”
}
This code installs Prometheus and Grafana in a Kubernetes cluster, ready to collect and visualize metrics.
Best Practices for Terraform-Based Monitoring
- Use variables for sensitive values like API keys.
- Store Terraform state securely (e.g., S3 with locking via DynamoDB).
- Use Terraform modules to reuse monitoring setups across environments.
- Implement alerts as code to version-control your observability.
Terraform + Monitoring = Scalable Observability
At PTP, we believe infrastructure should be automated, observable, and resilient. By combining Terraform with modern monitoring tools like CloudWatch, Datadog, or Prometheus, you can create a robust observability layer for your cloud workloads, defined entirely in code.
Need help integrating Terraform and monitoring for your cloud stack?
Simplify your monitoring with Terraform and PTP’s DevOps expertise—build a scalable, secure observability stack today.