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Databases · head to head

Apache Airflow vs Dagster

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Dagster logo

Dagster

Automation Integration

Data orchestration platform with asset lineage and AI-native observability

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; Dagster learning curve steeper than simpler task schedulers
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Dagster covers Asset-centric orchestration.

Where they differ

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

Attributes where Apache Airflow and Dagster differ
AttributeApache AirflowDagster
Pricing modelOpen source, no licence fee; managed services billed separatelyFree open-source core plus paid cloud platform
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, Self-hosted
CategoryDatabasesAutomation Integration

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 Dagster

  • Asset-centric orchestration
  • Lineage tracking
  • Data quality monitoring
  • Multi-tool integration
  • Branch deployments
  • Dagster+ AI
  • Hybrid deployment

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

Dagster

  • Orchestrating multi-stage data transformation pipelinesnot Apache Airflow
  • Tracking data lineage across transformation toolsnot Apache Airflow
  • Monitoring data quality and asset healthnot Apache Airflow
  • Coordinating dbt and Snowflake workflowsnot 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

Dagster

  • Learning curve steeper than simpler task schedulers
  • Asset-centric model requires redesigning existing task-based pipelines
  • Pricing complex with per-credit costs on top of base fee
  • Open-source version requires self-hosting infrastructure
  • Limited to data workflows; not suitable for general automation

Pricing, plan by plan

Apache Airflow

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

Dagster

Free
  • Open SourceFree
    • Self-hosted deployment
    • Full orchestration engine
    • Community support
  • Solo$10/month
    • 1 user and 1 code location
    • Pay-as-you-go credits at $0.040 per credit
    • 1 deployment
  • Starter$100/month
    • Up to 3 users and 5 code locations
    • Better credit rate at $0.035 per credit
    • 1 deployment
  • Pro$null/custom
    • Unlimited code locations and deployments
    • Custom serverless compute pricing
    • Personalized onboarding

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 Dagster if

  • You need asset-centric orchestration.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want lineage tracking.
Dagster review

Questions people ask

Is Apache Airflow or Dagster better?
Neither clearly leads. Apache Airflow starts at Free and Dagster at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Dagster?
Apache Airflow starts at Free and Dagster at Free.
Does Apache Airflow or Dagster run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Dagster runs on Cloud, Self-hosted.
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 Dagster is typically brought in for.
What can Apache Airflow do that Dagster cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Dagster covers Asset-centric orchestration, Lineage tracking, Data quality monitoring, Multi-tool integration.

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.

Dagster: What does Solo plan at $10/month include?

Solo includes 1 user, 1 code location, 1 deployment, and serverless compute at $0.010/minute, with 30-day free trial.

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.

Dagster: How are credits calculated on Dagster+?

Each asset materialization and ops execution costs 1 credit. Solo plan charges $0.040 per credit; Starter reduces to $0.035 per credit.

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.

Dagster: Is Dagster open-source available for free?

Yes, Dagster open-source is free and includes the full orchestration engine for self-hosted deployment with community support.

Source
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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