Luigi vs Mage

Luigi is a free Python package for defining and coordinating batch-job pipelines, dependencies, workflow execution, and failures. Workflows and dependency graphs are specified in Python, and tasks can also trigger work outside Python. Its documentation describes task support for Hadoop, Hive, Pig, and Spark, plus HDFS and local file abstractions. Those abstractions make file operations atomic, helping avoid pipelines being left with partial data. The Luigi server includes a web interface for searching and filtering tasks and viewing dependency graphs. It also supports command-line integration and use from Python code. The documentation describes pipelines with thousands of tasks that may run for days or weeks, including Hadoop jobs, database transfers, and machine-learning algorithms. The package is self-hosted, with no registration wall. Install the latest stable version with `pip install luigi`; adding `[toml]` enables TOML-based configuration. Documentation lists Python 3.10, 3.11, 3.12, and 3.13 as tested versions. A mailing list is available for discussion and questions.

Mage is a web platform for building and operating data workflows. Its listed functions include workflow scheduling, pipeline monitoring, data quality checks, and version control. Integrations cover Stripe, Salesforce, BigQuery, HubSpot, databases, SaaS APIs, cloud storage, files, and streams. Hybrid deployment is available. Mage has a free plan, while Starter costs $29 a month, billed monthly, plus $0.50 per CPU-hour. Starter includes one developer seat, one development environment, and 50 AI credits per month. Team costs $100 a month, billed monthly, plus $0.50 per CPU-hour; it supports up to 10 developer seats, includes one environment, and provides 250 shared AI credits per month. Mage is headquartered in Santa Clara, California, United States.