Workflow orchestration · Astronomer, Inc.

Astronomer

Managed Apache Airflow platform (Astro) that runs, scales, and supports Airflow for teams who don't want to operate it themselves.

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Astronomer is the company behind Astro, a fully managed commercial distribution of Apache Airflow. It is not a different orchestrator — every Astro deployment runs the actual open-source Airflow scheduler and workers — but Astronomer operates the infrastructure, handles upgrades, autoscaling, and hibernating idle deployments, and layers on features open-source Airflow lacks out of the box: end-to-end observability, AI-assisted DAG authoring, deployment rollbacks, fine-grained RBAC, SCIM provisioning, and enterprise support SLAs. Astronomer is also a major contributor to Airflow itself, including much of the work in Airflow 3. Teams choose between self-hosting Airflow directly (free but requiring their own ops effort) and Astro (a paid, managed layer on top of the same engine), making the choice largely about operational burden rather than pipeline-authoring differences.

At a glance

Vendor Astronomer, Inc.
Pricing model Usage-based
Free tier No
Deployment Cloud, Self-hosted
Open source No
Best for Teams that want Airflow's ecosystem without operating and scaling the infrastructure themselves.

Pricing

Astro is billed pay-as-you-go per deployment-hour and worker-hour, with higher-tier plans requiring annual contracts and custom pricing.

Plan Price Notes
Developer From $0.35/hr Pay-as-you-go; scale-to-zero and hibernating deployments
Team From $0.42/hr Adds observability, dedicated clusters ($2.40/hr), 24x5 support
Business Contact for pricing Annual agreements; SSO enforcement, 24x7 support with 1-hour SLA
Enterprise Contact for pricing Annual agreements; remote execution agents, custom RBAC, SCIM

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

Features

  • Fully managed, autoscaling Apache Airflow deployments
  • AI-assisted DAG authoring
  • End-to-end pipeline observability
  • One-click deployment rollbacks
  • Scale-to-zero and hibernating deployments to cut idle cost
  • SSO, SCIM, and fine-grained RBAC (higher tiers)
  • Committer-led enterprise support

Integrations

Profile last reviewed September 21, 2026

Alternatives

Apache Airflow The de facto standard open-source workflow orchestrator, defining pipelines as Python DAGs of scheduled, dependent tasks. Dagster Asset-centric orchestrator that models pipelines around the data they produce, with built-in testing, typing, and observability. Prefect Python-native workflow orchestrator built around plain functions and dynamic flows, with a managed cloud control plane for scheduling and observability.

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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. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually. 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 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.