Apache Airflow vs n8n
| Tagline | Programmatically author, schedule, and monitor workflows as Python DAGs | Fair-code workflow automation with 400+ integrations and native AI nodes |
| Category | Automation & iPaaS | Automation & iPaaS |
| Replaces | Workato | Zapier, Make, Workato |
| GitHub stars | 46k | 198k |
| Language | Python | TypeScript |
| License | Apache-2.0 | Sustainable Use License |
| Self-host difficulty | 4/5 Involved | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes Manual | One-Click Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 2 days ago | 2 days 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.
n8n
- Source-available (Sustainable Use License), not true OSI open source; some enterprise features (SSO, log streaming, external secrets) are gated behind paid tiers.
- Self-hosted instances require you to manage your own queue/Redis and Postgres for scaling and reliability.
- Far fewer pre-built app connectors than Zapier's 6,000+ catalog.
- Concurrency and execution throughput on the free self-hosted tier require manual queue-mode tuning.
Bottom line
Choose n8n if you want the lower-effort setup; choose n8n 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