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Head-to-head · DataOps Platforms

Apache Airflow vs Prefect

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Apache Airflow #1 in DataOps Platforms 9.3/10 Free plan Free plan✓ 0 of 4 features Visit Apache Airflow
Prefect #7 in DataOps Platforms 8.6/10 Free plan Free plan✓ 0 of 4 features Visit Prefect

Apache Airflow leads on 0 checks, Prefect on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreApache Airflow · 9.3/10
  • Free planboth

Apache Airflow scores higher on our rubric for dataops platforms: 9.3 against 8.6 out of 10; our editors rank them #1 and #7.

Apache Airflow is the better fit for teams wanting proven Python workflow orchestration. Prefect is the better fit for python teams needing observable hybrid orchestration.

  • Apache Airflow fits best

    Teams wanting proven Python workflow orchestration

  • Prefect fits best

    Python teams needing observable hybrid orchestration

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Side by side

Feature Apache Airflow 9.3/10 Visit ↗ Prefect 8.6/10 Visit ↗
At a glance
Editor score 9.3 8.6
Ranking #1 in DataOps Platforms #7 in DataOps Platforms
Best for Teams wanting proven Python workflow orchestration Python teams needing observable hybrid orchestration
Pricing model Free Free plan + paid
Starting price Free plan $100/mo
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted Cloud, Self-hosted
Platforms Web Web
Support Community, Docs Community, Docs
Built for Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Features Apache Airflow 0/4 · Prefect 0/4
Workflow scheduling Not checked yet Not checked yet
Pipeline monitoring Not checked yet Not checked yet
Data quality checks Not checked yet Not checked yet
Version control Not checked yet Not checked yet
Specs
Deployment options Not checked yet Not checked yet
Supported integrations Not checked yet Not checked yet
Our review
Pros
  • Author DAGs in Python and define task dependencies.
  • Automatic retries and configurable retry policies help manage failures.
  • Monitor logs, task status, and execution through a web UI.
  • Track flow-run states, logs, schedules, and events in one platform.
  • Configure retries and delays, event automations, and webhook triggers.
  • Run workflows in Prefect Cloud, customer infrastructure, or hybrid setups.
Cons
  • Deployment is self-hosted, so teams manage the environment.
  • Workflows are authored in Python rather than a visual builder.
  • Support channels are community and documentation.
  • Workflow definitions are Python-based, not built with a visual workflow editor.
  • Hobby lacks the webhooks and bring-your-own-compute highlights of Starter.
  • Team pricing is $100 per user per month, rather than a flat monthly fee.
Our verdict

Apache Airflow is an open-source workflow orchestration platform for teams building data pipelines, machine-learning workflows, infrastructure automation, and other batch-oriented processes. Workflows are defined as Python-based directed…

Read the review →

Prefect is a Python-based orchestration platform for data, machine-learning, durable-execution, and agent workflows. Teams define flows and tasks in Python, then deploy to Prefect Cloud, their own infrastructure, or hybrid environments.…

Read the review →
  1. Apache AirflowDataOps Platforms 9.3Free plan
  2. PrefectDataOps Platforms 8.6Free plan

Strengths and trade-offs

  • Apache Airflow — where it wins

    • Author DAGs in Python and define task dependencies.
    • Automatic retries and configurable retry policies help manage failures.
    • Monitor logs, task status, and execution through a web UI.

    Where it doesn't

    • Deployment is self-hosted, so teams manage the environment.
    • Workflows are authored in Python rather than a visual builder.
    • Support channels are community and documentation.

  • Prefect — where it wins

    • Track flow-run states, logs, schedules, and events in one platform.
    • Configure retries and delays, event automations, and webhook triggers.
    • Run workflows in Prefect Cloud, customer infrastructure, or hybrid setups.

    Where it doesn't

    • Workflow definitions are Python-based, not built with a visual workflow editor.
    • Hobby lacks the webhooks and bring-your-own-compute highlights of Starter.
    • Team pricing is $100 per user per month, rather than a flat monthly fee.

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update