Data orchestration platform for ML pipelines
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Reviews onG2
2 reviews trackedThe Bottom Line
Entry price
Paid plans only
Biggest pro
Modern data orchestration
Biggest con
Learning curve
TL;DR - Dagster
- Modern data pipeline tool
- Declarative YAML configurations
- Built-in data quality checks
Pricing: Paid only
Best for: Enterprises & pros
What is Dagster?
Dagster orchestrates data pipelines with a focus on developer experience. Define assets and their dependencies, let Dagster handle scheduling and execution-data engineering that fits software engineering practices.
The asset-based model aligns with how data teams think. Observability is built in. Testing and development workflows feel natural.
Data teams wanting modern tooling for pipeline orchestration choose Dagster for data engineering that applies software engineering discipline.
Available on: Web
Pros & Cons
Pros
- Modern data orchestration
- Good DX
- Asset-centric
- Type system
- Good documentation
Cons
- Learning curve
- Newer than Airflow
- Cloud expensive
- Ecosystem smaller
- Resource intensive
Ratings Across the Web
4.5(2 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Data orchestrationAssetsLineageObservabilityML pipelinesOpen source
Pricing Plans
30-day Free TrialPricing checked Jul 13, 2026
Open Source
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Usage-based pricing
- Full orchestration
- All features
- Community support
Solo
$10/month
Cloud
- 7,500 free credits
- $0.04/extra credit
- 1 user
Most Popular
Starter
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Teams
- 30,000 free credits
- $0.03/extra credit
- Multiple users
Pro
null
Enterprise
- Custom credits
- SLA
- Dedicated support
How Dagster's pricing compares
At $10/mo, Dagster is mid-range of its 2 direct competitors ($0.35 to $450/mo across the set).
Entry paid plan, monthly. Pricing checked Jul 13, 2026.
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Dagster FAQ
How does Dagster support machine learning pipelines?
Dagster orchestrates data pipelines specifically with a focus on developer experience, which is crucial for managing the complexities of machine learning workflows. It allows users to define data assets and their dependencies, enabling a structured approach to ML pipeline development and execution.
Which teams benefit most from using Dagster?
Data teams that prioritize modern tooling and want to apply software engineering discipline to their data engineering practices will find Dagster most beneficial. Its asset-based model and built-in observability are designed to align with how these teams think and operate.
How does Dagster compare to Apache Airflow?
Dagster offers a more modern approach to data orchestration compared to Apache Airflow, emphasizing an asset-centric model and developer experience. While Airflow is well-established, Dagster is newer and has a smaller ecosystem, though it provides a strong type system and good documentation.
What kind of trade-offs should users consider when adopting Dagster?
Users should be aware that Dagster has a learning curve and is newer than some established alternatives, leading to a smaller ecosystem. Additionally, running Dagster in the cloud can be expensive, and the platform itself can be resource-intensive.
How is Dagster priced?
Dagster is offered as a paid product. It does not include a permanently free tier for its services.
Can Dagster integrate with existing development workflows?
Yes, Dagster is designed to make testing and development workflows feel natural for data engineers. It aims to provide data engineering that fits seamlessly into existing software engineering practices.
Source: dagster.io