Dagster Labs

Name: Dagster Labs
Brand: Dagster Labs
Rating: 5 (1 reviews)

Building out Dagster, the data orchestration platform built for productivity.

Est 2018|Valuation $133.4M|Raised $49.2M|35 Ref Customers

LinkedIn 18K|Web traffic 57K|FTEs 85|HQ San Francisco, California, US

MarketsWorkflow Orchestration

01Competitive Landscape

Narrative analysis

Dagster versus Airflow (and managed Airflow): - Dagster emphasizes asset-level lineage and data-aware orchestration abstractions. - Airflow's comparative advantages include ecosystem maturity, wide operator availability, and an established hiring pool. - The vendor provides migration tooling to support incremental migration from Airflow DAGs to Dagster assets, which can reduce migration risk relative to a full rewrite. Dagster versus Prefect: - Dagster provides explicit asset-level semantics intended to support lineage and data governance. - Prefect is positioned as a lower-friction option for teams seeking to run Python workflows with minimal refactoring; buyer preference for simplicity and predictable pricing is cited in competitor materials.

Market adoption signals

GitHub repository activity and stars indicate open-source traction; these signals reflect interest but are not substitutes for production adoption metrics.
Press coverage has referenced vendor funding and growth; vendor-provided metrics on active projects should be validated independently where procurement requires verified adoption data.

	Dagster (Dagster+)	Astronomer Astro (Managed Airflow)	Prefect Cloud
Starting Price	Entry-level and small-team tiers with usage-based compute and credit metering	Hourly/usage deployment pricing for developer and managed plans	Free tier available; paid tiers vary by features and are sales- or usage-based
Most suitable for	Data teams seeking asset-based orchestration and integrated lineage/observability	Teams standardizing on Airflow who need managed operations and an Airflow-compatible ecosystem	Teams prioritizing low-friction adoption for Python workflows and flexible workflow semantics
Key strength	Emphasis on asset-centric model, developer experience, and integrated observability/lineage capabilities	Airflow ecosystem maturity, broad operator availability, and an established hiring pool	Low-friction adoption for Python-based workflows and flexible orchestration model
Key weakness	Learning curve; asset paradigm may not align with all teams; limited public review volume in some review platforms	Airflow's task-centric model is less data-aware by design; operational overhead of Airflow	Less native asset-lineage semantics compared with Dagster; governance features may require additional tooling

01Competitive Landscape

Narrative analysis

Dagster versus Airflow (and managed Airflow):

Dagster emphasizes asset-level lineage and data-aware orchestration abstractions.
Airflow's comparative advantages include ecosystem maturity, wide operator availability, and an established hiring pool.
The vendor provides migration tooling to support incremental migration from Airflow DAGs to Dagster assets, which can reduce migration risk relative to a full rewrite.

Dagster versus Prefect:

Dagster provides explicit asset-level semantics intended to support lineage and data governance.
Prefect is positioned as a lower-friction option for teams seeking to run Python workflows with minimal refactoring; buyer preference for simplicity and predictable pricing is cited in competitor materials.

Market adoption signals

GitHub repository activity and stars indicate open-source traction; these signals reflect interest but are not substitutes for production adoption metrics.
Press coverage has referenced vendor funding and growth; vendor-provided metrics on active projects should be validated independently where procurement requires verified adoption data.

	Dagster (Dagster+)	Astronomer Astro (Managed Airflow)	Prefect Cloud
Starting Price	Entry-level and small-team tiers with usage-based compute and credit metering	Hourly/usage deployment pricing for developer and managed plans	Free tier available; paid tiers vary by features and are sales- or usage-based
Most suitable for	Data teams seeking asset-based orchestration and integrated lineage/observability	Teams standardizing on Airflow who need managed operations and an Airflow-compatible ecosystem	Teams prioritizing low-friction adoption for Python workflows and flexible workflow semantics
Key strength	Emphasis on asset-centric model, developer experience, and integrated observability/lineage capabilities	Airflow ecosystem maturity, broad operator availability, and an established hiring pool	Low-friction adoption for Python-based workflows and flexible orchestration model
Key weakness	Learning curve; asset paradigm may not align with all teams; limited public review volume in some review platforms	Airflow's task-centric model is less data-aware by design; operational overhead of Airflow	Less native asset-lineage semantics compared with Dagster; governance features may require additional tooling