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Apache Airflow vs Mage

A side-by-side comparison of Apache Airflow and Mage across 26 workflow orchestration capabilities. Apache Airflow fully supports 13 of them and Mage fully supports 14; of the 12 mandatory features, Apache Airflow covers 11 and Mage covers 7.

Apache Airflow

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It allows you to define workflows as code, making them more maintainable, versionable, testable, and collaborative.

Features fully supported13 / 26
Mandatory features: 11 / 12View profile

Mage

Mage is an open-source data pipeline tool for transforming and integrating data, combining a notebook-style editor with modular, testable code blocks in Python, SQL and R.

Features fully supported14 / 26
Mandatory features: 7 / 12View profile

Feature comparison

11 of 26 capabilities differ between Apache Airflow and Mage.

Change tools
CapabilityApache AirflowMage
DAG-based workflow definition
Define dependencies and execution order
Mandatory		YesYes
Code-first workflows
Versionable, testable workflows
Mandatory		YesYes
Declarative workflow model
Predictability, reproducibility
Mandatory		YesPartial
Task dependency resolution
Correct execution order
Mandatory		YesYes
Persistent workflow state
Crash recovery, observability
Mandatory		YesYes
Task retries
Handle transient failures
Mandatory		YesYes
Retry policies (backoff, limits)
Avoid cascading failures
Recommended		PartialYes
Scheduling (cron / time-based)
Automated recurring workflows
Depends		YesYes
Event-driven execution
React to external systems
Depends		PartialYes
Backfills / replays
Reprocess historical data
Depends		YesYes
Idempotency support
Safe retries & replays
Mandatory		YesPartial
Exactly-once semantics
Correctness for business workflows
Optional		NoNo
Long-running workflows (days/months)
Business processes, approvals
Depends		NoNo
Human-in-the-loop steps
Approvals, manual actions
Optional		NoNo
Dynamic workflows at runtime
Conditional execution
Depends		PartialYes
Workflow versioning
Safe evolution over time
Recommended		NoPartial
Distributed execution
Scalability
Mandatory		YesPartial
High availability scheduler
Production reliability
Recommended		PartialPartial
Fault-tolerant execution
Crash safety
Mandatory		PartialPartial
Observability (logs, states, UI)
Debugging & ops
Mandatory		YesYes
Workflow-level visibility
Understand end-to-end runs
Mandatory		YesYes
Task isolation
Failure containment
Mandatory		YesPartial
Native Kubernetes support
Modern infra compatibility
Depends		PartialYes
Multi-language SDKs
Polyglot systems
Optional		NoPartial
Deterministic replay
Safe recovery & correctness
Optional		NoNo
Pluggable execution backends
Flexibility
Recommended		PartialYes

* To be considered a complete workflow orchestration tool, a tool must implement the features marked as mandatory according to our assessment.

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Learn more

Read the full profiles, including pros and cons, installation and alternatives.

Apache AirflowMage

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