Apache Airflow vs Grafana: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare Apache Airflow and Grafana across features, pricing, integrations, and community metrics. Apache Airflow / Grafana.

Feature

Apache Airflow

Workflow

Grafana

Monitoring

Side-by-side comparison of developer tools
Platform to programmatically author and schedule workflows
The open and composable observability and data visualization platform
GitHub Stars
⭐ 45,276
⭐ 73,569
Contributors
👥 4,323
👥 3,006
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Python
TypeScript
Features
  • • Airflow
  • • Apache
  • • Apache Airflow
  • • Automation
  • • Dag
  • • Alerting
  • • Analytics
  • • Business Intelligence
  • • Dashboard
  • • Data Visualization
Integrations
No integrations listed
  • • prometheus
  • • grafana
  • • elasticsearch
Momentum Score
94/100 (stable)
94/100 (stable)
Community Health
91/100 (excellent)
95/100 (excellent)
Maturity Index
88/100 (mature)
90/100 (mature)
Innovation Score
88/100 (pioneering)
95/100 (pioneering)
Risk Score (higher is safer)
94/100 (minimal)
94/100 (minimal)
Developer Experience
80/100 (good)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Apache Airflow Strengths

  • ✓ Larger community (4,323 contributors)

Grafana Strengths

  • ✓ More popular (73,569 stars)

When to Use Apache Airflow vs Grafana

Use Apache Airflow when its strengths align better with your stack and team needs, and choose Grafana when its ecosystem, integrations, or cost profile is a better fit.

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Data source: GitHub API

Last updated: 5/4/2026