Apache Airflow alternatives

3 tools to consider instead of Apache Airflow, shown against it.

Apache Airflow Dagster Prefect Astronomer
Vendor Apache Software Foundation Dagster Labs Prefect Technologies, Inc. Astronomer, Inc.
Pricing model Open source + paid options Subscription Subscription Usage-based
Free tier Yes Yes Yes No
Deployment Self-hosted Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) Yes (Apache-2.0) No
Best for Teams needing a mature, widely supported orchestrator with the deepest ecosystem of integrations. Data teams who want lineage and observability built around the assets a pipeline produces, not just its tasks. Python teams wanting lightweight, code-first orchestration for dynamic or irregular pipeline structures. Teams that want Airflow's ecosystem without operating and scaling the infrastructure themselves.
Pricing

Airflow itself is free, open-source software; managed hosting (Astronomer, Cloud Composer, MWAA) is priced separately by those vendors.

Pricing has not been verified yet — see the vendor's site.

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.

Solo $120/month
Starter $1,200/month
Enterprise Contact sales

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

A free Hobby tier is available forever; paid Prefect Cloud plans are flat or per-user monthly fees, with custom pricing for Enterprise.

Hobby Free
Starter $100/month
Team $100/user/month
Enterprise Custom pricing

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

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

Developer From $0.35/hr
Team From $0.42/hr
Business Contact for pricing
Enterprise Contact for pricing

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

Features
  • Pipelines defined as Python DAGs of tasks
  • Large ecosystem of provider packages/operators
  • Time-based and event-driven (Airflow 3) scheduling
  • Web UI for monitoring runs, logs, and task state
  • Task retries, SLAs, and alerting
  • Kubernetes and CeleryExecutor for distributed execution
  • REST API for programmatic pipeline management
  • 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+)
  • Python-native flows via decorators (no separate DAG API)
  • Dynamic, runtime-determined pipeline structure
  • Run anywhere Python runs (local, containers, Kubernetes, serverless)
  • Automation rules triggered by run-state events
  • Built-in retries, caching, and result persistence
  • Prefect Cloud UI for scheduling, history, and alerting
  • Self-hostable open-source server as an alternative control plane
  • 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

In the index now