Dagstervs
Prefect
Prefect: Python-first workflow orchestration with simpler pricing
Dagster
Data orchestration platform with asset lineage and AI-native observability
Overview
What Dagster does
Dagster is an AI-native DataOps platform that orchestrates, observes, and activates data across entire technology stacks. The platform structures how data is built, delivered, and monitored by treating assets—not tasks—as first-class citizens, automatically tracking lineage, quality signals, and dependencies between them. Teams define asset-based pipelines that span multiple tools including dbt, Snowflake, Fivetran, and custom Python code, and Dagster executes and observes the entire graph. Built-in observability shows data health across the system, with branch deployments enabling safe pipeline changes before production rollout. Dagster+ AI provides diagnosis and decision-making capabilities, converting operational context into actionable intelligence for both humans and AI agents. The platform serves data teams, engineers, and organizations ranging from solo practitioners to enterprises across finance, retail, e-commerce, and life sciences.
What people use it for
- Orchestrating multi-stage data transformation pipelines
- Tracking data lineage across transformation tools
- Monitoring data quality and asset health
- Coordinating dbt and Snowflake workflows
Related searches
The honest half
Where it falls short
Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about 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
Cross-shopped
What people choose instead of Dagster
Each pairing was judged by two reviewers asking whether a buyer would genuinely weigh the two against each other. The ones that failed were deleted rather than published.
- DagstervsMage AI
Mage AI: Visual pipeline builder with built-in data transformation
- DagstervsApache Airflow
Apache Airflow: Established open-source task scheduler with large community
Pricing
What Dagster costs
Taken from the vendor's own pricing page. Prices move, so check before you buy.
Open Source
Free
- Self-hosted deployment
- Full orchestration engine
- Community support
Solo
$10 /mo
- 1 user and 1 code location
- Pay-as-you-go credits at $0.040 per credit
- 1 deployment
- 30-day free trial
Starter
$100 /mo
- Up to 3 users and 5 code locations
- Better credit rate at $0.035 per credit
- 1 deployment
- Catalog search
- 30-day free trial
Pro
On request
- Unlimited code locations and deployments
- Custom serverless compute pricing
- Personalized onboarding
- Private Slack channel
- Uptime SLAs
Capabilities
Features
Asset-centric orchestration
Define pipelines around data assets rather than tasks
Lineage tracking
Automatic visualization of data lineage across tools
Data quality monitoring
Built-in asset checks and quality tracking
Multi-tool integration
Native support for dbt, Snowflake, Fivetran, and custom code
Branch deployments
Test pipeline changes safely before production
Dagster+ AI
AI-powered diagnosis and decision-making for data teams
Hybrid deployment
Managed cloud or self-hosted infrastructure options
Answered, with sources
Questions people ask
Each answer names the page it came from, so you can check it rather than take our word for it.
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.
SourceHow 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.
SourceIs 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.
SourceKeep looking
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