Key DevOps Metrics You Should Be Tracking

Organizations with comprehensive DevOps metrics programs are twice as likely to meet their business objectives compared to those without them. The harder question is which metrics actually deserve your attention.

What Are the Four DORA Metrics?

The DevOps Research and Assessment (DORA) metrics remain the gold standard for measuring software delivery performance:

1. Deployment Frequency - How often you successfully release to production
2. Lead Time for Changes - Time from code commit to production deployment
3. Change Failure Rate - Percentage of deployments causing failures
4. Mean Time to Restore (MTTR) - Time to recover from incidents

Together they tell you about both speed and stability, not one at the expense of the other. Elite performers deploy multiple times daily with less than one day lead time, while keeping failure rates below 15% and recovery times under one hour.

Which Metrics Matter Beyond DORA?

DORA covers delivery speed and stability, but it leaves gaps. A fuller picture needs a few more categories:

Reliability Metrics

Service Level Indicators (SLIs), Service Level Objectives (SLOs), and the Four Golden Signals (latency, traffic, errors, saturation) tell you more about how healthy your system actually is.

Security Metrics

Time to detect vulnerabilities, time to remediate, and vulnerability density help teams build security into their pipelines rather than bolting it on afterward.

Team Culture Metrics

Developer satisfaction, cognitive load, and cross-team collaboration metrics tell you whether the pace is something your team can keep up.

Cost Efficiency Metrics

Cloud waste, unit economics, and resource utilization metrics connect technical decisions to business outcomes.

How Do You Collect These Metrics With the Right Tooling?

Collecting these metrics is much easier when your platform exposes the data through an API. With Scalr's Terraform automation platform, you can pull infrastructure deployment metrics directly:

import requests
import json
 
# Connect to Scalr API to retrieve deployment metrics
def get_deployment_metrics(workspace_id, time_period='30d'):
    base_url = "https://example.scalr.io/api/iacp/v3"
    headers = {
        'Authorization': f'Bearer {API_TOKEN}',
        'Content-Type': 'application/vnd.api+json'
    }
    
    # Get deployment frequency
    response = requests.get(
        f"{base_url}/workspaces/{workspace_id}/runs?filter[status]=applied&filter[created-at][gt]={time_period}",
        headers=headers
    )
    data = response.json()
    
    # Calculate deployment metrics
    total_deployments = len(data['data'])
    successful_deployments = sum(1 for run in data['data'] if run['attributes']['status'] == 'applied')
    failed_deployments = total_deployments - successful_deployments
    
    return {
        'deployment_frequency': total_deployments,
        'success_rate': successful_deployments / total_deployments if total_deployments > 0 else 0,
        'change_failure_rate': failed_deployments / total_deployments if total_deployments > 0 else 0
    }
 
# Example usage
metrics = get_deployment_metrics('ws-1234567890')
print(json.dumps(metrics, indent=2))

For infrastructure-as-code environments, you can also track drift detection through Scalr's state management:

# Terraform code to enable Scalr's remote backend and drift detection
terraform {
  backend "remote" {
    hostname     = "example.scalr.io"
    organization = "acc-xxxxxxxxxxxxx"
    workspaces {
      name = "production-infrastructure"
    }
  }
}
 
# Enable scheduled drift detection on a Scalr environment.
# Drift detection is configured at the environment level; the scheduler
# supports Daily or Weekly runs (no arbitrary cron). Notifications are
# delivered to Slack or Microsoft Teams via integration channels.
resource "scalr_environment" "production" {
  name       = "production"
  account_id = "acc-xxxxxxxxxxxxx"
 
  policy_groups = [scalr_policy_group.drift.id]
}
 
# Notify a Slack channel when drift is detected
resource "scalr_integration_slack" "drift_alerts" {
  name         = "drift-alerts"
  account_id   = "acc-xxxxxxxxxxxxx"
  channel      = "#infra-drift"
  environments = [scalr_environment.production.id]
  events       = ["drift_detected"]
}
 
# Attach an OPA policy group to enforce guardrails on every run.
# Enforcement (advisory / soft-mandatory / hard-mandatory) is declared
# inside the policy group's scalr-policy.hcl file, not as a Terraform
# attribute on the resource itself.
resource "scalr_policy_group" "drift" {
  name        = "drift-guardrails"
  account_id  = "acc-xxxxxxxxxxxxx"
  vcs_provider_id = "vcs-xxxxxxxxxxxxx"
  vcs_repo {
    identifier = "my-org/opa-policies"
    branch     = "main"
    path       = "policies/drift"
  }
}

How Do Elite, High, Medium, and Low Performers Compare?

Here's how organizations typically stack up across key metrics:

What Are the Best Practices for Rolling Out DevOps Metrics?

Begin with the four DORA metrics before you expand into anything else. Automate collection, because manual tracking adds overhead and introduces errors. Tie each metric back to a business outcome so it drives value rather than activity. Build dashboards that fit the stakeholder reading them, whether that's an engineer or an exec. And when the numbers point at a problem, act on it: the goal is improvement, not measurement for its own sake.

What Common Pitfalls Should You Avoid?

1. Metrics overload - Too many metrics create noise rather than insight
2. Using metrics punitively - Creates a culture of fear rather than improvement
3. Vanity metrics - Focus on actionable metrics that drive decisions
4. Manual collection - Leads to inconsistent data and wasted effort
5. Missing infrastructure metrics - IaC quality and drift significantly impact reliability

How Do You Keep Metrics Useful as You Grow?

Measurement has to keep up as your delivery process changes. When the platform that automates your infrastructure also collects the metrics (as Scalr does for Terraform), you get visibility across the whole pipeline instead of stitching numbers together from separate tools. That makes it easier to spot where deploys slow down, where failures cluster, and where cost or reliability is slipping. If you're still designing that pipeline, start with our guide to building Terraform and OpenTofu CI/CD pipelines.

The teams that get the most out of this treat metrics as a way to find the next thing to fix. The numbers point you somewhere; the work is deciding what to do about them.

Want to learn how Scalr can help you implement effective DevOps metrics for your Terraform infrastructure? Contact us for a personalized demo.