Dagster
Your Platform for AI and Data Pipelines
Founded
2018
Starting Price
$10
Category
Automation & Integration
Last updated March 5, 2026
About Dagster
Dagster is an open-source data orchestration platform built for modern data and AI engineering teams. It takes an asset-centric approach to pipeline development, treating tables, files, ML models, and notebooks as first-class citizens with built-in observability, lineage tracking, and data quality validation.
Pros & Cons
Pros
- Asset-centric model makes pipelines more transparent and debuggable than task-based orchestrators
- Strong first-class integrations with dbt, Snowflake, and Databricks reduce glue code
- Built-in observability and data lineage catch quality issues proactively
- Highly testable and developer-friendly design enables local development and CI/CD workflows
- Open-source core with an active community and growing ecosystem
Key Features
Asset-Centric Orchestration
Models pipelines around actual data assets produced rather than just tasks, enabling clearer lineage and dependency management.
Declarative Automation
Event-driven, condition-based triggers that go beyond cron scheduling to reduce redundant computations.
Integrated Data Catalog
Unified, searchable catalog of all data assets, workflows, and metadata for cross-team discoverability.
Built-In Data Quality
Embeds validation, freshness checks, and automated testing directly into pipelines to catch issues early.
End-to-End Observability
Traces data lineage, captures operational metadata, monitors run health, and surfaces cost per operation.
Cost Transparency
Exposes per-operation compute costs and resource utilization for optimizing cloud spending.
Broad Ecosystem Integration
First-class integrations with dbt, Snowflake, Databricks, Spark, AWS, and Azure.
Pricing
Solo
$10/month
- 7,500 credits/month
- 1 user
- 1 code location
- 1 deployment
- 30-day free trial
Best For
ETL/ELT Pipeline Development
Build modular, testable extract-transform-load pipelines with full lineage tracking for warehouses like Snowflake or BigQuery.
Analytics Engineering with dbt
Orchestrate dbt model runs with observability, scheduling, and dependency management on top of dbt transformations.
MLOps Pipelines
Automate data ingestion, feature engineering, model training, and retraining triggers with asset-modeled ML artifacts.
Data Quality & Observability
Embed automated data validation, freshness checks, and anomaly detection into pipelines.
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