Apache Airflow vs Prefect
Apache Airflow
Programmatic workflow orchestration for data pipelines
Prefect
Modern Python-native workflow orchestration
| Feature | Apache Airflow | Prefect |
|---|---|---|
| Category | Automation | Automation |
| Sub-category | Workflow | Workflow |
| Maturity | stable | stable |
| Complexity | intermediate | intermediate |
| Performance tier | medium | medium |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 38.0K | 18.0K |
| Contributors | 0 | 300 |
| Commit frequency | weekly | weekly |
| Plugin ecosystem | none | none |
| Docs quality | good | good |
| Backing org | Apache Foundation | Prefect |
| Funding model | foundation | vc_backed |
| Min RAM | 2 GB | 1 GB |
| Min CPU cores | 2 | 1 |
| Scaling pattern | single_node | single_node |
| Self-hostable | Yes | Yes |
| K8s native | No | No |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Python | Python |
| API type | REST | REST |
| Protocols | HTTP | HTTP |
| Deployment | docker, pip | pip, docker |
| SDK languages | — | — |
| Team size fit | solo, small, medium, enterprise | small, medium |
| First release | 2020 | 2020 |
| Latest version | — | — |
When to use Apache Airflow
- ✓ Primary: data-pipeline-orchestration
- ✓ Primary: etl-scheduling
- ✓ Primary: ml-pipeline-management
When to use Prefect
- ✓ Python-native data pipeline orchestration
- ✓ ML training pipeline management
- ✓ Scheduled ETL with automatic retries
Apache Airflow anti-patterns
- ✕ Not for real-time streaming
- ✕ Complex setup and operations
- ✕ DAG parsing can be slow
- ✕ Not for event-driven workflows
Prefect anti-patterns
- ✕ Python-only
- ✕ Cloud UI is the best experience — self-hosted UI limited
- ✕ Smaller plugin ecosystem than Airflow