MageMage streamlines ML workflows with an open-source, intuitive data pipeline builder for efficient model development and deployment.

Mage is an open-source data pipeline tool designed for machine learning teams, simplifying the process of building, training, and deploying ML models with intuitive and collaborative data preparation.

Mage is an open-source data pipeline tool for machine learning, helping data teams build, train, and deploy ML models efficiently. It focuses on making data preparation and model development intuitive and collaborative.

Not yet. This listing is compiled from Mage AI’s public documentation, pricing pages and changelogs — nothing on this page is presented as a hands-on test result.Mage sits in our testing queue; when we run it, this section will state what we tested, how long for, and what it actually produced. How we review AI tools.

• Automated Machine Learning Pipelines: Build end-to-end pipelines for data ingestion, preprocessing, feature engineering, model training, and evaluation.
• Collaborative Data Science Workflows: Enable data scientists and engineers to work together on ML projects with shared pipelines and version control.
• Model Deployment and CI/CD: Streamline the process of deploying trained models into production environments and integrate with CI/CD practices.
• Data Preparation for ML: Leverage Mage's visual tools to clean, transform, and prepare datasets for optimal machine learning model performance.

• Visual pipeline builder
• Data transformation and cleaning tools
• Model training integration
• Deployment automation
• Version control and collaboration
• Orchestration and scheduling
• Monitoring and logging

Prices as of July 25, 2026, taken from Mage AI’s public pricing page. Vendors change pricing without notice — check before you buy.

• Reliance on open-source community for support
• May require custom integrations for niche ML frameworks

• Apache Airflow
• Kubeflow Pipelines
• Prefect

1. Visit the official Mage.ai website to download and install the tool.
2. Explore the documentation to understand the core concepts and features.
3. Start building your first data pipeline using the visual editor or by writing Python code.

Use a SynaBot assistant to produce the thinking, then move the output into Mage for execution. Every SynaBot assistant is included with the platform membership.

• Content Creator (ZARA) — drafts the copy, captions and campaign angles you'll run through Mage.
• Business Planner (VIKRAM) — decides whether Mage belongs in your stack and what it should replace.
• Project Manager (PACE) — turns the rollout of Mage into owned, dated tasks.

Browse the full AI assistant roster, grab a starting point from the prompt library, or have us wire it together with our AI consultancy service.