Apache Airflow vs Prefect
A side-by-side comparison of Apache Airflow and Prefect across 26 workflow orchestration capabilities. Apache Airflow fully supports 13 of them and Prefect fully supports 12; of the 12 mandatory features, Apache Airflow covers 11 and Prefect covers 8.
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.
Prefect
Prefect is a Python-native open-source workflow orchestration and automation tool, enabling you to author, schedule, and monitor data pipelines as code.
Feature comparison
10 of 26 capabilities differ between Apache Airflow and Prefect.
| Capability | Importance* | ||
|---|---|---|---|
DAG-based workflow definition Define dependencies and execution order Mandatory | Mandatory | Yes | Yes |
Code-first workflows Versionable, testable workflows Mandatory | Mandatory | Yes | Yes |
Declarative workflow model Predictability, reproducibility Mandatory | Mandatory | Yes | Partial |
Task dependency resolution Correct execution order Mandatory | Mandatory | Yes | Yes |
Persistent workflow state Crash recovery, observability Mandatory | Mandatory | Yes | Yes |
Task retries Handle transient failures Mandatory | Mandatory | Yes | Yes |
Retry policies (backoff, limits) Avoid cascading failures Recommended | Recommended | Partial | Partial |
Scheduling (cron / time-based) Automated recurring workflows Depends | Depends | Yes | Yes |
Event-driven execution React to external systems Depends | Depends | Partial | Yes |
Backfills / replays Reprocess historical data Depends | Depends | Yes | Partial |
Idempotency support Safe retries & replays Mandatory | Mandatory | Yes | No |
Exactly-once semantics Correctness for business workflows Optional | Optional | No | No |
Long-running workflows (days/months) Business processes, approvals Depends | Depends | No | Partial |
Human-in-the-loop steps Approvals, manual actions Optional | Optional | No | Partial |
Dynamic workflows at runtime Conditional execution Depends | Depends | Partial | Yes |
Workflow versioning Safe evolution over time Recommended | Recommended | No | Partial |
Distributed execution Scalability Mandatory | Mandatory | Yes | Yes |
High availability scheduler Production reliability Recommended | Recommended | Partial | Partial |
Fault-tolerant execution Crash safety Mandatory | Mandatory | Partial | Partial |
Observability (logs, states, UI) Debugging & ops Mandatory | Mandatory | Yes | Yes |
Workflow-level visibility Understand end-to-end runs Mandatory | Mandatory | Yes | Partial |
Task isolation Failure containment Mandatory | Mandatory | Yes | Yes |
Native Kubernetes support Modern infra compatibility Depends | Depends | Partial | Partial |
Multi-language SDKs Polyglot systems Optional | Optional | No | No |
Deterministic replay Safe recovery & correctness Optional | Optional | No | No |
Pluggable execution backends Flexibility Recommended | Recommended | Partial | Yes |
* 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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