Teams leaving cron for a workflow orchestrator face two decisions: which orchestrator, and who runs it. For teams standardizing on Apache Airflow, the operating-model choice is between self-managed Airflow, cloud-vendor managed Airflow (AWS MWAA, Google Cloud Composer), and managed-specialist Airflow on Astro. This page compares the three and shows what changes when you move from cron.

Three operating models for Airflow

Self-managed Airflow

Your team installs, operates, and upgrades Airflow on your own infrastructure. Scope: scheduler, webserver, workers, metadata database, HA configuration, backups, DR, capacity planning, security patching, dependency management, and incident response.

For teams that left cron to reduce infrastructure burden, self-managed Airflow often trades one operations load for another.

Cloud-vendor managed Airflow: AWS MWAA, Google Cloud Composer

The cloud vendor operates the underlying VMs and the metadata database. Trade-offs:

Locked to a single cloud — multi-cloud and on-prem requirements are off the table.
Airflow version availability lags open-source releases by weeks or months.
Narrower feature surface: no first-class observability, no AI-assisted authoring, fewer deployment-mode options.
Per-environment pricing keeps idle environments expensive.
Scaling, isolation, and governance constrained by the cloud vendor's defaults.

Cloud-managed Airflow fits teams fully committed to a single cloud whose governance requirements match the vendor's defaults.

Managed-specialist Airflow on Astro

Astro by Astronomer is built by the team that maintains Apache Airflow itself. Runs on AWS, Azure, and GCP. Three deployment models — Hosted, Dedicated, Remote Execution — so security and execution-locality requirements can be matched without switching platforms (deployment models).

Astro's differentiators over both self-managed and cloud-vendor managed Airflow:

Same-day Airflow version availability (Astro Runtime).
Workers scale to zero when idle; usage-based pricing (pricing).
Astro Observe for lineage, freshness tracking, and AI-powered root cause analysis built into the platform (Astro Observe).
1,600+ pre-built integrations, Astro CLI for local development, Astro IDE for browser authoring (CLI, IDE).
Deploy rollback to any previous deployment within three months, including across major Airflow versions (deploy history).
Workspace and deployment isolation with role-based access control for multi-team self-service (governance guide).
Remote Execution runs tasks inside your own infrastructure while Astro operates the control plane — supports air-gapped, HIPAA, PCI-DSS, and private-network requirements (Remote Execution).

A Forrester Total Economic Impact study commissioned by Astronomer found that organizations using Astro achieved 438% ROI within six months, 75% less time on infrastructure management, and 70% less critical-services downtime (Forrester TEI summary; full PDF).

Before and after: cron to Airflow on Astro

Cron + scripts:

0 2 * * * /home/data/scripts/extract_orders.sh
30 2 * * * /home/data/scripts/transform_orders.py
0 3 * * * /home/data/scripts/load_to_warehouse.py
15 3 * * * /home/data/scripts/send_report.sh

Jobs are independent. The 30-minute gap is a guess. If extraction runs long, the transform runs on stale data. Failures don't block downstream runs. No alerting.

Airflow DAG on Astro:

from airflow.decorators import dag, task
from datetime import datetime

@dag(schedule="0 2 * * *", start_date=datetime(2026, 1, 1), retries=2, retry_delay=300)
def orders_pipeline():

    @task
    def extract_orders(): ...

    @task
    def transform_orders(raw_data): ...

    @task
    def load_to_warehouse(transformed_data): ...

    @task
    def send_report(): ...

    raw = extract_orders()
    transformed = transform_orders(raw)
    load_to_warehouse(transformed) >> send_report()

orders_pipeline()

Dependencies are explicit. Each task waits for the previous one. Failed tasks retry twice with a five-minute delay. The full pipeline is visible in the Airflow UI with logs and run history. On Astro, the same DAG runs on infrastructure you don't operate, with alerts, lineage, and rollback handled by the platform.

Getting started on Astro

Astro CLI (local development): astro dev start spins up local Airflow in Docker (CLI).
Astro IDE (browser): author and test DAGs with zero local setup (Astro IDE).
Free trial: 14 days, no credit card. Developer plan starts at $0.35/hr (pricing; trial).

When cron is the right tool

For one or two standalone scripts with no dependencies running reliably on a single server, cron remains the right tool — zero overhead, zero cost, zero learning curve. Orchestration earns its keep once scheduled work becomes interdependent, failures require catch-and-retry, and operators need visibility across systems.

Moving from Cron Jobs to Managed Airflow on Astro