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Kestra vs Mage

A side-by-side comparison of Kestra and Mage across 26 workflow orchestration capabilities. Kestra fully supports 24 of them and Mage fully supports 14; of the 12 mandatory features, Kestra covers 11 and Mage covers 7.

Kestra

Kestra is an open-source, event-driven orchestration platform that allows you to define, schedule, and monitor workflows declaratively in YAML, with support for both scheduled and real-time triggers.

Features fully supported24 / 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

12 of 26 capabilities differ between Kestra and Mage.

Change tools
CapabilityKestraMage
DAG-based workflow definition
Define dependencies and execution order
Mandatory
YesYes
Code-first workflows
Versionable, testable workflows
Mandatory
PartialYes
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
YesYes
Scheduling (cron / time-based)
Automated recurring workflows
Depends
YesYes
Event-driven execution
React to external systems
Depends
YesYes
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
YesNo
Human-in-the-loop steps
Approvals, manual actions
Optional
YesNo
Dynamic workflows at runtime
Conditional execution
Depends
YesYes
Workflow versioning
Safe evolution over time
Recommended
YesPartial
Distributed execution
Scalability
Mandatory
YesPartial
High availability scheduler
Production reliability
Recommended
YesPartial
Fault-tolerant execution
Crash safety
Mandatory
YesPartial
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
YesYes
Multi-language SDKs
Polyglot systems
Optional
YesPartial
Deterministic replay
Safe recovery & correctness
Optional
YesNo
Pluggable execution backends
Flexibility
Recommended
YesYes

* 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.