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Head-to-head · DataOps Platforms

Apache Airflow vs Apache NiFi

  • 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
Apache NiFi #4 in DataOps Platforms 8.9/10 Open source ✓ 0 of 4 features Visit Apache NiFi

Apache Airflow leads on 0 checks, Apache NiFi 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 planonly Apache Airflow

Apache Airflow scores higher on our rubric for dataops platforms: 9.3 against 8.9 out of 10; our editors rank them #1 and #4.

Apache Airflow is the better fit for teams wanting proven Python workflow orchestration. Apache NiFi is the better fit for teams managing secure, observable dataflows.

  • Apache Airflow fits best

    Teams wanting proven Python workflow orchestration

  • Apache NiFi fits best

    Teams managing secure, observable dataflows

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Side by side

Feature Apache Airflow 9.3/10 Visit ↗ Apache NiFi 8.9/10 Visit ↗
At a glance
Editor score 9.3 8.9
Ranking #1 in DataOps Platforms #4 in DataOps Platforms
Best for Teams wanting proven Python workflow orchestration Teams managing secure, observable dataflows
Pricing model Free Free
Starting price Free plan Open source
Free plan ✓ (best) Not checked yet
Free trial — —
Deployment Self-hosted Self-hosted
Platforms Web Web, Windows, macOS, Linux
Support Community, Docs Community, Docs
Built for Small business, Mid-market, Enterprise Mid-market, Enterprise
Features Apache Airflow 0/4 · Apache NiFi 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.
  • Visual browser interface for designing, monitoring, and modifying dataflows
  • Fine-grained provenance and lineage tracking across dataflows
  • TLS, access controls, and back pressure support controlled delivery
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.
  • Requires installation and management in your own environment
  • No named integrations or pricing details are presented
  • Custom processor development can add engineering work
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 →

Apache NiFi is an open-source dataflow system for routing, transforming, mediating, and distributing data. Its browser-based interface lets teams design, control, monitor, and modify flows built from processors, connections, ports, and…

Read the review →
  1. Apache AirflowDataOps Platforms 9.3Free plan
  2. Apache NiFiDataOps Platforms 8.9Open source

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.

  • Apache NiFi — where it wins

    • Visual browser interface for designing, monitoring, and modifying dataflows
    • Fine-grained provenance and lineage tracking across dataflows
    • TLS, access controls, and back pressure support controlled delivery

    Where it doesn't

    • Requires installation and management in your own environment
    • No named integrations or pricing details are presented
    • Custom processor development can add engineering work

More comparisons

All DataOps platforms comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update