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Databases · head to head

Apache Airflow vs Rowy

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Rowy logo

Rowy

Spreadsheets

Spreadsheet interface for Google Cloud Firestore with cloud functions written in the browser

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; Rowy every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Rowy covers Firestore backed grid.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Rowy differ
AttributeApache AirflowRowy
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb
CategoryDatabasesSpreadsheets

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 Rowy

  • Firestore backed grid
  • Browser authored cloud functions
  • Typed columns
  • Runs in your Google Cloud project
  • Role based access
  • Form view
  • Open source

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 Rowy
  • Coordinating machine learning training and evaluation runsnot Rowy
  • Orchestrating dbt runs alongside extraction and loadingnot Rowy
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Rowy

Rowy

  • A Firebase application that needs an internal admin interface without building onenot Apache Airflow
  • Operations staff correcting production records in Firestore without developer involvementnot Apache Airflow
  • Attaching a small transformation or notification function to a field change without a full deployment pipelinenot Apache Airflow
  • A content team populating documents that a mobile application reads directly from Firestorenot 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

Rowy

  • Every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
  • Firestore has no joins, so linking tables in the grid is a convenience layer rather than a relational feature and reports across collections still need a separate query.
  • It is useful only to teams already on Firebase, so choosing it effectively locks the operational tooling to one cloud vendor.
  • Query costs are billed by document read, and an unfiltered grid on a large collection can generate a bill that a spreadsheet user has no intuition for.
  • Development activity has slowed relative to the broader category, so evaluate current maintenance before making it load bearing for internal operations.

Pricing, plan by plan

Apache Airflow

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

Rowy

Free
  • Open source self hostedFree
    • No licence fee
    • Deploys into your own Google Cloud project
    • You pay Google Cloud for Firestore reads, writes and storage

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 Rowy if

  • You need firestore backed grid.
  • You want to start without paying.
  • You also want browser authored cloud functions.
Rowy review

Questions people ask

Is Apache Airflow or Rowy better?
Neither clearly leads. Apache Airflow starts at Free and Rowy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Rowy?
Apache Airflow starts at Free and Rowy at Free.
Does Apache Airflow or Rowy run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Rowy runs on Web.
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 Rowy is typically brought in for.
What can Apache Airflow do that Rowy cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Rowy covers Firestore backed grid, Browser authored cloud functions, Typed columns, Runs in your Google Cloud project.

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.

Rowy: Where is my data stored?

In your own Firestore instance inside your own Google Cloud project. Rowy does not hold a copy.

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.

Rowy: What does it cost to run?

No licence fee. You pay Google Cloud for Firestore reads, writes, storage and function invocations, and an open grid on a big collection reads a lot.

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.

Rowy: Can it do joins across collections?

Not really. Firestore does not support joins, and no interface layer can add them.

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.

Rowy: Is there a row limit?

Not from Rowy. The limits that bite are Firestore document size and per document write throughput.

Rowy: Is it suitable if I am not on Firebase?

No. It is specifically a Firestore interface.

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