Apache Airflow vs Dagu
| Tagline | Programmatically author, schedule, and monitor workflows as Python DAGs | DAG-based workflow orchestrator with a web UI — cron replacement with real dependencies |
| Category | Automation & iPaaS | Automation & iPaaS |
| Replaces | Workato | Zapier, Make, Tray.io |
| GitHub stars | 46k | 3.5k |
| Language | Python | Go |
| License | Apache-2.0 | GPL-3.0 |
| Self-host difficulty | 4/5 Involved | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes Manual | Docker Manual |
| Managed hosting | ||
| Last updated | today | today |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Apache Airflow
- Fully code-first (Python DAGs); there is no no-code builder for non-developers.
- Heavyweight to operate: scheduler, webserver, metadata DB, and executor/workers must be configured and maintained.
- Not built around consumer SaaS app triggers; it targets data orchestration rather than iPaaS connectors.
- Real-time/event triggering is weaker than purpose-built automation tools, which favor scheduling.
Dagu
- No distributed worker pool — all steps run on the same host, limiting horizontal scale
- No built-in secrets vault; credentials must be managed via environment variables or external tools
- UI is functional but lacks advanced features like parameterized run forms or dynamic DAG generation
- Community is smaller than Airflow or Prefect; fewer integrations and plugins
Bottom line
Choose Dagu if you want the lower-effort setup; choose Apache Airflow for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.
Apache Airflow
Programmatically author, schedule, and monitor workflows as Python DAGs
Dagu
DAG-based workflow orchestrator with a web UI — cron replacement with real dependencies