Apache Airflow
MLOps platforms
AI orchestration software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if Apache Airflow and its alternatives fit your requirements.
Completely free
Free Trial unavailable
Free version
Small
Medium
Large
- Agriculture, fishing, and forestry
- Transportation and logistics
- Information technology and software
What is Apache Airflow
Apache Airflow is an open-source workflow orchestration platform used to author, schedule, and monitor data and ML pipelines as code (Python DAGs). It is commonly used by data engineering and ML platform teams to coordinate batch jobs across databases, compute engines, and cloud services. Airflow focuses on dependency management, scheduling, retries, and observability rather than providing an end-to-end model development or deployment environment. It is typically deployed and operated by the user (self-managed) or consumed via managed offerings from third parties.
Mature workflow scheduling engine
Airflow provides robust scheduling, dependency management, retries, and backfills for complex batch workflows. It includes a web UI for monitoring DAG runs, task status, logs, and historical execution. These capabilities make it a common backbone for orchestrating multi-step ML and data pipelines where reliability and traceability matter.
Extensive integration ecosystem
Airflow supports many systems through providers and operators (for example, common cloud services, databases, and compute platforms). This breadth helps teams orchestrate heterogeneous toolchains without being locked into a single vendor stack. Compared with more vertically integrated platforms, Airflow often fits better when an organization already has established best-of-breed components.
Pipeline-as-code flexibility
Workflows are defined in Python, enabling code review, testing patterns, and version control alongside application code. Teams can implement custom operators, sensors, and hooks to match internal conventions and infrastructure. This approach supports standardized orchestration across multiple ML projects while keeping logic explicit and auditable.
Not a full MLOps suite
Airflow does not natively provide model registry, feature store, experiment tracking, or model serving. Teams typically integrate additional tools to cover the full ML lifecycle, which increases architectural complexity. In environments seeking a single integrated platform for development through deployment, Airflow usually serves only the orchestration layer.
Operational overhead at scale
Running Airflow reliably requires managing components such as the scheduler, webserver, metadata database, and workers/executors. Scaling and hardening (HA, upgrades, secrets, RBAC, and performance tuning) can require dedicated platform engineering effort. Managed services can reduce this burden, but that shifts responsibility and cost to a third-party provider.
Batch-oriented execution model
Airflow is primarily designed for scheduled and event-polled batch workflows rather than low-latency, real-time orchestration. While it can trigger external services and respond to events via sensors, it is not a stream processing engine. Use cases requiring continuous, near-real-time coordination may need complementary systems.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Open-source (Apache Airflow) | $0 (open-source) | Released under the Apache License 2.0; downloadable and self-hosted; no paid plans, tiers, or pricing published on the official Apache Airflow website. |
Seller details
Apache Software Foundation
Wakefield, Massachusetts, USA
1999
Non-profit
https://www.apache.org/
https://x.com/TheASF
https://www.linkedin.com/company/the-apache-software-foundation/
