Apache Airflow vs Dagster
No clear leader: Apache Airflow (73.0) and Dagster (70.4) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for data engineering tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Apache Airflow
A community-built orchestration system that enables users to define data pipelines and tasks in Python, execute them on distributed workers, and monitor progress through a web-based interface. It supports dynamic pipeline generation, extensive third-party integrations, and operates best for batch-oriented workflows.
Dagster
A unified orchestration and observability platform that manages data pipelines through assets, tracks dependencies and lineage, enforces data quality, and integrates with existing tools like dbt, Snowflake, and Spark. Supports both self-hosted open-source and managed cloud deployment.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Apache Airflow
Free, open-source project. Managed cloud versions available through third parties.
Dagster
From $120/month. 30-day free trial. Subscription with metered credits (asset materializations and ops executed); serverless compute charged separately.
- Solo$120/month with 7.5k credits/month included; overages at $0.040/credit
- 1 User
- 1 Code location
- 1 Deployment
- Core orchestration
- Asset-based orchestration
- +6 more
- Starter$1200/month with 30k credits/month included; overages at $0.035/credit
- Up to 3 Users
- 5 Code locations
- 1 Deployment
- Catalog search
- Column-level lineage
- +8 more
- Pro/EnterpriseContact sales
- Unlimited code locations
- Unlimited deployments
- Custom serverless compute pricing
- Cost tracking and insights
- Personalized onboarding support
- +8 more
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
- Databricks
- Snowflake
- Slack
- Tableau
- Datadog
- Airbyte
- PagerDuty
Apache Airflow
- Amazon Web Services
- Microsoft Azure
- Google Cloud Platform
- dbt Cloud
- Salesforce
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Kubernetes
- Docker
- Apache Spark
- Apache Kafka
- Apache Cassandra
- Apache Flink
- Apache Druid
- Elasticsearch
- Jenkins
- GitHub
- OpenAI
- Anthropic
- Cohere
- Pinecone
- Weaviate
- +19 more
Dagster
- dbt
- Spark
- Azure
- AWS
- Soda
- Microsoft Teams
- Python
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.