Workflow orchestration · Dagster Labs

Dagster

Asset-centric orchestrator that models pipelines around the data they produce, with built-in testing, typing, and observability.

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Dagster is an open-source orchestrator that models pipelines around 'software-defined assets' — the tables, files, or ML models a pipeline produces — rather than around tasks the way Airflow does. This asset-centric approach gives Dagster native data lineage, per-asset freshness and materialization history, and the ability to reason about a pipeline in terms of its outputs rather than its execution steps. It supports local development with fast iteration, typed inputs/outputs, built-in testing utilities, and partitioned and scheduled or sensor-based (event-driven) execution. Dagster integrates with dbt so dbt models appear as assets in the same lineage graph as ingestion and ML steps. The open-source core is self-hosted for free; Dagster+ is the company's managed cloud offering, priced on a credit-based consumption model with separate serverless compute charges.

At a glance

Vendor Dagster Labs
Pricing model Subscription
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Data teams who want lineage and observability built around the assets a pipeline produces, not just its tasks.

Pricing

The open-source core is free to self-host; Dagster+ cloud plans start at a flat monthly fee plus usage-based compute, with a 30-day free trial.

Plan Price Notes
Solo $120/month 7,500 credits/month included; 1 user, 1 code location, 1 deployment
Starter $1,200/month 30,000 credits/month included; up to 3 users, 5 code locations
Enterprise Contact sales Unlimited code locations/deployments, cost tracking, uptime SLAs

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Asset-centric orchestration with automatic lineage
  • Native dbt integration (dbt models as assets)
  • Typed inputs/outputs and built-in unit testing
  • Partitioned execution and backfills
  • Schedule- and sensor-based (event-driven) triggers
  • Per-asset freshness policies and observability
  • Branch deployments for isolated testing (Dagster+)

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Apache Airflow The de facto standard open-source workflow orchestrator, defining pipelines as Python DAGs of scheduled, dependent tasks. Prefect Python-native workflow orchestrator built around plain functions and dynamic flows, with a managed cloud control plane for scheduling and observability. Mage Notebook-style, open-source pipeline builder combining ingestion, transformation, and orchestration with a block-based visual editor.

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Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. dbt (data build tool) An open-source tool from dbt Labs that lets analytics teams write data transformations as version-controlled, tested SQL. Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a workflow orchestrator Pick a workflow orchestrator by pipeline shape, who owns it, and whether you already run Kubernetes — not by the demo DAG. How to decide build vs buy for analytics A framework for the build-vs-buy decision in analytics infrastructure — total cost, maintenance burden and lock-in, layer by layer.