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