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Inngest vs Mage

A side-by-side comparison of Inngest and Mage across 26 workflow orchestration capabilities. Inngest fully supports 19 of them and Mage fully supports 14; of the 12 mandatory features, Inngest covers 10 and Mage covers 7.

Inngest

Inngest is an event-driven durable execution platform that lets developers write reliable, multi-step background functions and AI workflows in TypeScript, Python or Go, running on serverless, servers or the edge.

Features fully supported19 / 26
Mandatory features: 10 / 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

14 of 26 capabilities differ between Inngest and Mage.

Change tools
CapabilityInngestMage
DAG-based workflow definition
Define dependencies and execution order
Mandatory
PartialYes
Code-first workflows
Versionable, testable workflows
Mandatory
YesYes
Declarative workflow model
Predictability, reproducibility
Mandatory
NoPartial
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
PartialNo
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
PartialPartial
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
PartialYes
Multi-language SDKs
Polyglot systems
Optional
YesPartial
Deterministic replay
Safe recovery & correctness
Optional
PartialNo
Pluggable execution backends
Flexibility
Recommended
NoYes

* 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.