Apache Airflow vs Conductor (Netflix)
| Tagline | Programmatically author, schedule, and monitor workflows as Python DAGs | Microservice workflow orchestration engine open-sourced by Netflix |
| Category | Automation & iPaaS | Automation & iPaaS |
| Replaces | Workato | Zapier, Workato |
| GitHub stars | 46k | 9.5k |
| Language | Python | Java |
| License | Apache-2.0 | Apache-2.0 |
| Self-host difficulty | 4/5 Involved | 4/5 Involved |
| Deploy options | Docker Compose Kubernetes Manual | Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 5 days ago | 1 month ago |
| 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.
Conductor (Netflix)
- Workflow logic defined in JSON/YAML; no drag-and-drop canvas for non-technical users
- Requires Elasticsearch and a relational DB for production — non-trivial infrastructure
- Community edition lacks built-in RBAC available in the commercial Orkes Cloud offering
Bottom line
Both are a similar lift to self-host; choose Apache Airflow for the larger community and ecosystem. Apache Airflow has seen more recent development. Open each guide below for deploy steps and the full feature gap.
Apache Airflow
Programmatically author, schedule, and monitor workflows as Python DAGs
Conductor (Netflix)
Microservice workflow orchestration engine open-sourced by Netflix