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Databases · head to head

Apache Airflow vs Apache Superset

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Apache Superset logo

Apache Superset

Spreadsheets

Modern data exploration and visualization platform

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; Apache Superset no commercial pricing; open-source project
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Apache Superset covers 40+ Visualizations.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Airflow and Apache Superset differ
AttributeApache AirflowApache Superset
Pricing modelOpen source, no licence fee; managed services billed separatelyopen-source
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb, Self-hosted, Docker
CategoryDatabasesSpreadsheets
FoundedUnknown1999

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 Apache Superset

  • 40+ Visualizations
  • SQL IDE
  • Semantic Layer
  • Caching
  • Security
  • PostgreSQL
  • MySQL
  • Presto

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

Apache Superset

  • Self-service analyticsnot Apache Airflow
  • Data explorationnot Apache Airflow
  • Ad-hoc reportingnot Apache Airflow
  • Collaborative analysisnot Apache Airflow
  • Embedded analyticsnot 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

Apache Superset

  • No commercial pricing; open-source project
  • Commercial hosting available via Preset.io (separate vendor)

Pricing, plan by plan

Apache Airflow

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

Apache Superset

Free
  • Open SourceFree
    • Full Features
    • Self-hosted
    • Community Support

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 Apache Superset if

  • You need 40+ visualizations.
  • You want to start without paying.
  • You work on Web, Self-hosted, Docker.
  • You also want sql ide.
Apache Superset review

Questions people ask

Is Apache Airflow or Apache Superset better?
Neither clearly leads. Apache Airflow starts at Free and Apache Superset at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Apache Superset?
Apache Airflow starts at Free and Apache Superset at Free.
Does Apache Airflow or Apache Superset run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Apache Superset runs on Web, Self-hosted, Docker.
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 Apache Superset is typically brought in for.
What can Apache Airflow do that Apache Superset cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Apache Superset covers 40+ Visualizations, SQL IDE, Semantic Layer, Caching.

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.

Apache Superset: What does Apache Superset cost?

Apache Superset is open-source software available free under the Apache 2.0 license. There are no licensing costs, subscription fees, or per-seat charges for using Superset itself.

Source
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.

Apache Superset: Is there a paid version of Apache Superset?

Apache Superset itself is free and open-source. Preset.io offers commercial Superset hosting with premium support, but this is a separate vendor service, not an official paid tier of Superset.

Source
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.

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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