Apache Airflow vs Temporal
| Tagline | Programmatically author, schedule, and monitor workflows as Python DAGs | Durable execution engine for resilient long-running business workflows |
| Category | Automation & iPaaS | Automation & iPaaS |
| Replaces | Workato | Zapier, Workato |
| GitHub stars | 46k | 12k |
| Language | Python | Go |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 4/5 Involved | 4/5 Involved |
| Deploy options | Docker Compose Kubernetes Manual | 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.
Temporal
- No visual no-code editor; all workflows must be written by developers in a supported SDK language
- Production deployment requires Cassandra or PostgreSQL plus Elasticsearch — significant infrastructure
- Self-hosted Web UI has limited analytics compared to Temporal Cloud
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