Dagster vs Apache Airflow

Side-by-side — pricing, strengths, tradeoffs, and fit.

Dagster

A data orchestrator for machine learning, analytics, and ETL.

Apache Airflow

A platform to programmatically author, schedule, and monitor workflows.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From N/A

Overview

One-liner

A data orchestrator for machine learning, analytics, and ETL.

One-liner

A platform to programmatically author, schedule, and monitor workflows.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Starts at

$0

Starts at

N/A

Model

Flat rate

Model

N/A

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams building Python-based data pipelines that require extensive monitoring and scheduling capabilities.
Organizations needing a self-hosted solution for data orchestration without cloud dependency.

✓ Best for

Data engineering teamsProvides robust tools for orchestrating complex ETL workflows.
Organizations with diverse data sourcesExtensible architecture supports integration with various systems.
Teams needing a visual workflow interfaceThe web-based UI offers clear visibility into task execution.

Not ideal for

✕ Not for

Projects requiring real-time data processing or streaming (Dagster is batch-oriented).
Developers looking for a fully managed service with no self-hosting requirements.

✕ Not for

Small teams with limited Python expertiseThe complexity of setup and DAG creation can be a barrier.
Real-time data processingAirflow's batch-oriented design is not suited for real-time tasks.

Strengths

↑ Strengths

Define data pipelines using Python code
Visualize and monitor pipeline execution in a web UI
Automate scheduling of pipelines with cron-like syntax
Extensive logging and error handling for troubleshooting

↑ Strengths

Highly extensible with plugins
Active community and development
Web-based UI for monitoring
Open-source and free to use

Weaknesses

↓ Weaknesses

Limited integrations with non-Python ecosystems
Complex setup and configuration

↓ Weaknesses

Complex setup and configuration
High resource consumption
Full Dagster profile →Full Apache Airflow profile →