Apache Airflow

Apache Airflow is a platform for creating, scheduling, and monitoring workflows written in Python. It is intended for batch workflows with a defined beginning and end that run on a schedule. Through its web application, users can schedule or manually trigger DAGs, inspect task status and logs, backfill runs, and rerun failed tasks. Its modular design uses a message queue to coordinate workers and can orchestrate an arbitrary number of them. Built-in operators connect workflows to Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other services. The community maintains more than 80 separately installable provider packages, which can be upgraded or downgraded independently of Airflow core. Airflow can read connections, variables, and configuration from secret backends, but those integrations do not support writes. It uses JWT authentication for its public REST API and internal Execution API. In Airflow 3 and later, sensitive credentials are masked in logs, the interface, and API outputs. Airflow is free to download and use under the Apache License. It officially supports Linux-based deployment; macOS is for local development, and Windows deployment is unsupported except for WSL2 development.

Airflow offers code-defined workflow scheduling and operational monitoring, with separately managed provider packages. Its intended fit is scheduled batch work, and deployment support is Linux-based.

Airflow may suit teams that define scheduled batch workflows in Python and want a web interface for scheduling and monitoring. Its official deployment support is Linux-based.

Apache Airflow is a self-hosted platform for authoring, scheduling, and monitoring workflows defined in Python. Its free, extensible model is compelling, but it is a poor fit for teams that need a hosted service or event-first workflows.

Overview

Airflow combines Python workflow definitions with a web application for scheduling and operational management. That makes it a strong choice for engineering teams that want workflows managed as code while giving operators a place to inspect task status and logs. The trade-off is self-management: the deployment model is self-hosted, and official deployment support is Linux-based.

Its intended workload is batch processing with a clear start and finish that runs on a schedule. A modular architecture uses a message queue to coordinate an arbitrary number of workers, which can accommodate growing execution demands, but does not remove the work of operating the platform. Airflow began at Airbnb in October 2014 and became an Apache Software Foundation top-level project in January 2019.

Key features

Python workflows and operational controls

Workflows are authored in Python, not a visual workflow builder. That suits teams comfortable defining and maintaining logic in code; teams seeking drag-and-drop authoring should look elsewhere. Operators can manually trigger DAGs, inspect logs and task status, backfill runs, and rerun failed tasks. Automatic retries and execution observability support routine operations, while human approvals are also supported.

Integrations and secrets

Plug-and-play operators cover Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other third-party services. More than 80 community-maintained provider packages can be installed separately and upgraded or downgraded independently from Airflow core. That separation gives teams flexibility to manage integrations on their own cadence, though it also means providers are distinct packages to manage.

Airflow can read connections, variables, and configuration from secret backends rather than its database, but those integrations do not support writes. JWT authentication is used for the public REST API and internal Execution API. In Airflow 3 and later, sensitive credentials are masked in logs, the interface, and API outputs.

Pricing

Apache Airflow costs nothing. The free plan provides the software for download and use at no cost under the Apache License source code; there is no paid tier described. The absence of a software fee makes it accessible to teams prepared to deploy and operate it themselves, but does not turn self-hosting into a managed service.

Platforms

Airflow supports API and web access and self-hosted deployment. Linux is the officially supported deployment environment. macOS is supported for local development, while Windows is unsupported for deployment apart from WSL2 development. Teams requiring Windows deployment or a hosted platform should choose a different approach.

Who it's for

Airflow is best for engineering and data teams with Python expertise that run scheduled batch workflows and need visibility into execution, retry controls, backfills, or worker scaling. Community learning resources, Slack, and mailing lists provide support. It is less suitable for users who need visual authoring, event-first automation, or a provider-managed deployment.

Pros and cons

• Pro: Python-defined workflows and a web interface combine programmable authoring with task status, logs, and operational controls.
• Pro: Independently managed provider packages cover major cloud platforms and let teams change integrations separately from core releases.
• Pro: The software is free to download and use, with a modular architecture designed to coordinate an arbitrary number of workers.
• Con: Self-hosting and Linux-based deployment support put infrastructure and operations responsibility on the adopting team.
• Con: The scheduled-batch focus and lack of a visual workflow builder make it a mismatch for event-first work or visual authoring needs.
• Con: Secret backends are read-only for connections, variables, and configuration, so they cannot serve as write paths.

Alternatives

Consider BMC Control-M if paid SaaS or cloud and hybrid application and data orchestration with SLA management is a better fit; its Starter Pack (SaaS) is $2,400 a month when billed annually. Choose Kestra if its separately offered open-source platform better suits your workflow needs. DataKitchen TestGen is another option, with a free open-source plan capped at 1 user, 1 connection, and 1 project.

Keboola may suit readers who prefer its free plan, which provides 120 compute minutes in the first month and 60 minutes per month thereafter. Prefect offers a free Hobby plan with 2 users, 1 workspace, 5 deployments, and 500 minutes per month of Prefect Serverless. For a free Apache-licensed platform requiring Java 21 for release 2.19.0, consider Apache Hop. Apache NiFi is another free option distributed under the Apache License, Version 2.0. Luigi is a free open-source Python package with no registration walls.

Browse DataOps Platforms, Workload Automation Software, and Workflow Orchestration Software for more options in related categories.

Verdict

Choose Apache Airflow if your team can operate a Linux-based deployment and needs Python-defined, scheduled batch orchestration with detailed run controls and independently managed integrations. Its strongest reason to choose is a capable, extensible workflow platform with no software fee; its main reason to look elsewhere is the combination of self-hosting and a narrow batch-workflow fit.