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Prefect vs ZenML

A side-by-side comparison of Prefect and ZenML across 26 workflow orchestration capabilities. Prefect fully supports 12 of them and ZenML fully supports 13; of the 12 mandatory features, Prefect covers 8 and ZenML covers 9.

Prefect

Prefect is a Python-native open-source workflow orchestration and automation tool, enabling you to author, schedule, and monitor data pipelines as code.

Features fully supported12 / 26
Mandatory features: 8 / 12View profile

ZenML

ZenML is an open-source MLOps framework that lets teams write ML and AI pipelines in Python once and run them on any orchestrator and infrastructure stack.

Features fully supported13 / 26
Mandatory features: 9 / 12View profile

Feature comparison

9 of 26 capabilities differ between Prefect and ZenML.

Change tools
CapabilityPrefectZenML
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
PartialPartial
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
YesPartial
Event-driven execution
React to external systems
Depends
YesPartial
Backfills / replays
Reprocess historical data
Depends
PartialNo
Idempotency support
Safe retries & replays
Mandatory
NoPartial
Exactly-once semantics
Correctness for business workflows
Optional
NoNo
Long-running workflows (days/months)
Business processes, approvals
Depends
PartialPartial
Human-in-the-loop steps
Approvals, manual actions
Optional
PartialPartial
Dynamic workflows at runtime
Conditional execution
Depends
YesPartial
Workflow versioning
Safe evolution over time
Recommended
PartialYes
Distributed execution
Scalability
Mandatory
YesYes
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
PartialYes
Task isolation
Failure containment
Mandatory
YesYes
Native Kubernetes support
Modern infra compatibility
Depends
PartialYes
Multi-language SDKs
Polyglot systems
Optional
NoNo
Deterministic replay
Safe recovery & correctness
Optional
NoNo
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.