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Head-to-head · DataOps Platforms

Apache Airflow vs Kestra

  • 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
Kestra #6 in DataOps Platforms 8.7/10 Free plan Free plan✓ 0 of 4 features Visit Kestra

Apache Airflow leads on 0 checks, Kestra 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 8.7 out of 10; our editors rank them #1 and #6.

Apache Airflow is the better fit for teams wanting proven Python workflow orchestration. Kestra is the better fit for teams wanting visual workflows and approval gates.

  • Apache Airflow fits best

    Teams wanting proven Python workflow orchestration

  • Kestra fits best

    Teams wanting visual workflows and approval gates

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Side by side

Feature Apache Airflow 9.3/10 Visit ↗ Kestra 8.7/10 Visit ↗
At a glance
Editor score 9.3 8.7
Ranking #1 in DataOps Platforms #6 in DataOps Platforms
Best for Teams wanting proven Python workflow orchestration Teams wanting visual workflows and approval gates
Pricing model Free Free
Starting price Free plan Free
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted Cloud, Self-hosted
Platforms Web Web
Support Community, Docs Email, Community, Docs · 24/7
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Apache Airflow 0/4 · Kestra 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.
  • Choose a no-code editor or declarative YAML workflow definitions.
  • Use schedules, webhooks, polling, realtime, flow, and MCP triggers.
  • Track executions with logs, metrics, retries, replay, and approval gates.
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.
  • YAML definitions may be less approachable for teams avoiding configuration files.
  • Enterprise Edition and Kestra Cloud pricing details require a vendor conversation.
  • Deployment and governance options vary across the three offerings.
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 →

Kestra orchestrates workflows for data engineers, software engineers, and platform teams. Flows can be created in a no-code editor or defined declaratively in YAML, then run across languages and infrastructure. The Open Source plan…

Read the review →
  1. Apache AirflowDataOps Platforms 9.3Free plan
  2. KestraDataOps Platforms 8.7Free 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.

  • Kestra — where it wins

    • Choose a no-code editor or declarative YAML workflow definitions.
    • Use schedules, webhooks, polling, realtime, flow, and MCP triggers.
    • Track executions with logs, metrics, retries, replay, and approval gates.

    Where it doesn't

    • YAML definitions may be less approachable for teams avoiding configuration files.
    • Enterprise Edition and Kestra Cloud pricing details require a vendor conversation.
    • Deployment and governance options vary across the three offerings.

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update