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Automation Integration · head to head

Dagster vs Apache Airflow

Dagster logo

Dagster

Automation Integration

Data orchestration platform with asset lineage and AI-native observability

From
Free
Rated
-
Full reviewWebsite
Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Dagster learning curve steeper than simpler task schedulers; Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • They diverge on capability: Dagster covers Asset-centric orchestration, Apache Airflow covers Pipelines as Python.

Where they differ

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

Attributes where Dagster and Apache Airflow differ
AttributeDagsterApache Airflow
Pricing modelFree open-source core plus paid cloud platformOpen source, no licence fee; managed services billed separately
PlatformsCloud, Self-hostedLinux, Docker, Kubernetes, Self-hosted
CategoryAutomation IntegrationDatabases

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 Dagster

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

Only in Apache Airflow

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

What people use each for

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

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

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

Where each one falls short

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

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

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

Pricing, plan by plan

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

Apache Airflow

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

Which should you pick?

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

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

Questions people ask

Is Dagster or Apache Airflow better?
Neither clearly leads. Dagster starts at Free and Apache Airflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dagster or Apache Airflow?
Dagster starts at Free and Apache Airflow at Free.
Does Dagster or Apache Airflow run on more platforms?
Dagster runs on Cloud, Self-hosted. Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted.
Can I use Dagster for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dagster best used for?
Dagster is most often used for orchestrating multi-stage data transformation pipelines, tracking data lineage across transformation tools, monitoring data quality and asset health, coordinating dbt and snowflake workflows. Of those, orchestrating multi-stage data transformation pipelines and tracking data lineage across transformation tools are not what Apache Airflow is typically brought in for.
What can Dagster do that Apache Airflow cannot?
Dagster covers Asset-centric orchestration, Lineage tracking, Data quality monitoring, Multi-tool integration. Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating.

Answered from the vendors’ own pages

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: 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: 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: 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: 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: 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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