Apache Airflow vs Windmill

Apache Airflow

Programmatic workflow orchestration for data pipelines

Windmill

Developer-first internal tool and workflow engine

Feature Apache Airflow Windmill
Category Automation Automation
Sub-category Workflow Workflow
Maturity stable stable
Complexity intermediate intermediate
Performance tier medium medium
License Apache-2.0 AGPL-3.0
License type permissive copyleft
Pricing fully free fully free
GitHub stars 38.0K 12.0K
Contributors 0 0
Commit frequency weekly weekly
Plugin ecosystem none none
Docs quality good good
Backing org Apache Foundation Windmill
Funding model foundation vc_backed
Min RAM 2 GB 512 MB
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 Rust, TypeScript
API type REST REST
Protocols HTTP HTTP
Deployment docker, 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 Windmill

  • ✓ Build internal tools from scripts with auto-generated UI
  • ✓ Multi-language workflow orchestration
  • ✓ Scheduled scripts with approval flows

Apache Airflow anti-patterns

  • ✕ Not for real-time streaming
  • ✕ Complex setup and operations
  • ✕ DAG parsing can be slow
  • ✕ Not for event-driven workflows

Windmill anti-patterns

  • ✕ AGPL license
  • ✕ Newer project — less battle-tested
  • ✕ Smaller community than n8n/Airflow
Full Apache Airflow profile → Full Windmill profile → All comparisons