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Databases · head to head

Apache Airflow vs Rook

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Rook logo

Rook

Cloud

Kubernetes operator that deploys and manages Ceph storage clusters

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; Rook rook automates Ceph but does not abstract it, so an incident still demands Ceph expertise, and organisations without it end up hiring consultants at exactly the wrong moment.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Rook covers Ceph operator.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Apache Airflow and Rook actually diverge.

Attributes where Apache Airflow and Rook differ
AttributeApache AirflowRook
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, Kubernetes
CategoryDatabasesCloud

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Airflow

  • Pipelines as Python
  • Web UI
  • Cloud provider packages
  • Jinja templating
  • Retries and dependencies
  • Extensible operators

Only in Rook

  • Ceph operator
  • Block, file and object
  • Erasure coding
  • CSI driver
  • Automated upgrades
  • Multi-cluster mirroring

What people use each for

The jobs each tool is most often brought in to do.

Apache Airflow

  • Scheduling nightly ETL where step order and retries matternot Rook
  • Coordinating machine learning training and evaluation runsnot Rook
  • Orchestrating dbt runs alongside extraction and loadingnot Rook
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Rook

Rook

  • An on-premises Kubernetes platform needing block, shared filesystem and S3 storage without buying three productsnot Apache Airflow
  • A team that already runs Ceph and wants its lifecycle managed declaratively inside Kubernetesnot Apache Airflow
  • A large cluster where three-way replication overhead is unaffordable and erasure coding is requirednot Apache Airflow
  • An organisation building a private cloud that cannot use managed cloud storage services for residency reasonsnot Apache Airflow

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Airflow

  • Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
  • Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
  • Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind

Rook

  • Rook automates Ceph but does not abstract it, so an incident still demands Ceph expertise, and organisations without it end up hiring consultants at exactly the wrong moment.
  • There is no vendor and no SLA; the realistic commercial support routes are IBM Red Hat OpenShift Data Foundation or an independent Ceph consultancy, both of which change the cost picture entirely.
  • Ceph is resource hungry, needing substantial memory and dedicated disks per OSD, so the hardware cost of a properly sized cluster is often underestimated.
  • Recovery and rebalancing after a disk or node failure generates heavy background input and output that can degrade application performance for hours, which surprises teams sizing for steady state.
  • Minimum viable clusters require several nodes with several disks each, so it is impractical at small scale and the entry hardware cost exceeds simpler alternatives.

Pricing, plan by plan

Apache Airflow

Free
  • Apache AirflowFree
    • Full scheduler and web UI
    • All provider packages
    • No task or DAG limits

Rook

Free
  • RookFree
    • Apache 2.0 licensed, no licence fee
    • Graduated CNCF project
    • Community support via GitHub and Slack only

Which should you pick?

Choose Apache Airflow if

  • You need pipelines as python.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want web ui.
Apache Airflow review

Choose Rook if

  • You need ceph operator.
  • You want to start without paying.
  • You work on Linux, Kubernetes.
  • You also want block, file and object.
Rook review

Questions people ask

Is Apache Airflow or Rook better?
Neither clearly leads. Apache Airflow starts at Free and Rook at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Rook?
Apache Airflow starts at Free and Rook at Free.
Does Apache Airflow or Rook run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Rook runs on Linux, Kubernetes.
Can I use Apache Airflow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Airflow best used for?
Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what Rook is typically brought in for.
What can Apache Airflow do that Rook cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Rook covers Ceph operator, Block, file and object, Erasure coding, CSI driver.

Answered from the vendors’ own pages

Apache Airflow: Is Apache Airflow free?

Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.

Rook: Who supports it in production?

Nobody by default. IBM sells Red Hat OpenShift Data Foundation, which is supported Rook and Ceph, and independent consultancies sell Ceph support. Decide this before deployment.

Apache Airflow: What language are Airflow workflows written in?

Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.

Rook: Does it need Ceph knowledge?

Yes. Rook handles deployment and routine operations, but troubleshooting a degraded cluster is a Ceph skill and there is no way around it.

Apache Airflow: Is Airflow suitable for real-time pipelines?

Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.

Rook: Can it replace an object storage appliance?

Functionally yes, through the RADOS gateway, but you take on the operations that an appliance vendor would otherwise carry.

Apache Airflow: What are the main alternatives to Airflow?

Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.

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