Apache Airflow vs Dagster (2026): which should you pick?

Pick Apache Airflow if

• Fits if you write Python and already run your own servers
• Works for teams that want workflows defined as code with no licence fee

Look elsewhere if

• Skip it if you want a drag-and-drop builder, as workflows are Python only
• Not for you if you have nobody to install, upgrade and secure it
• Tight if you need paid support, because the project lists community help only
• Watch out: managed hosting means a separate vendor and a separate bill

Pick Dagster if

• Fits if your team writes Python and thinks of pipelines as tables and files
• Works for data teams that want a hosted console but can self-host the core later

Look elsewhere if

• Skip it if you can't write Python, as pipelines are code
• Tight if you're not paying: no free hosted plan, only a 30-day trial
• Watch out: credits are charged per asset and per op on top of $10 or $100 a month
• Watch out: the site says Dagster is now part of Prefect, so plans and branding may change

Apache Airflow

• You install, upgrade, secure and scale it yourself unless you buy a managed service such as Astronomer's Astro.
• The project's site lists a community, meetups and a Slack for help; it offers no paid support plan.
• Defining workflows requires Python knowledge; there is no visual builder in the core project.

Dagster

• Usage is billed in credits: one per asset materialized and one per op executed, on top of the monthly fee.
• There is no free hosted plan; the trial lasts 30 days, though the core framework can be self-hosted.
• Cost tracking, role scoping by project and several alert types are limited to the Pro plan.
• The site says Dagster is now part of Prefect, so plans and branding may change.

Apache Airflow

Pipelines as Python code

Workflows are written in Python, so loops and date logic can generate tasks dynamically, with Jinja templating built in for parameters.

Web interface

A web application shows the status and logs of running and finished tasks, and lets you schedule and manage workflows.

Scalable architecture

A modular design with a message queue lets Airflow spread work over any number of workers.

Provider integrations

Ready-made operators cover Google Cloud, Amazon Web Services, Microsoft Azure and other third-party services, and you can write your own.

AI provider

The Common AI provider reached 1.0 in October 2026, covering agents, sandboxes, human approval and retries for LLM work in pipelines.

Dagster

Asset-based orchestration

Pipelines are modeled as assets with dependencies, alongside workflow-based orchestration for work that is not tied to a data asset.

Data catalog and lineage

Asset metadata, asset-level lineage and, on Starter and above, catalog search; column lineage is listed on the comparison table.

Data quality checks

Asset checks, freshness policies and dbt test integration surface the health of each asset.

Insights and alerts

Pipeline metrics with a 30-day lookback and alerts by email, Slack or Teams; cost tracking for BigQuery and Snowflake is a Pro feature.

Hybrid or Serverless compute

Run on Dagster Serverless, billed at $0.010 a minute, or on your own infrastructure in Hybrid mode with no compute charge from Dagster.

Open-source core

The core orchestration framework is open source on GitHub and can be self-hosted; Dagster+ adds branch deployments, RBAC and cost insights.

Apache Airflow

• Open source$0

  Apache-2.0 licensed software you install and run yourself; hosting and operations costs are yours.

Dagster

• Solo$10

  per month, plus $0.040 per credit

  1 user, 1 code location and 1 deployment for personal projects. Serverless compute is $0.010 a minute.

• Starter$100

  per month, plus $0.035 per credit

  Up to 3 users, 5 code locations, 1 deployment and catalog search. Serverless compute is $0.010 a minute.

• ProCustom

  Unlimited code locations and deployments, cost tracking and insights, uptime SLAs and a private Slack channel. Sold through sales.