Mage.ai review 2026: open-source data pipeline and orchestration tool. See Mage Pro pricing tiers, features, pros and cons, and top ETL alternatives.
Category
Automation
Pricing
Open Source with Freemium/Subscription (Mage Pro), from Free (open source, MIT license); Mage Pro Starter from about $100/month plus compute usage
Verified
Not yet
Last updated
July 18, 2026
Founded
2020
Headquarters
Santa Clara, California, United States
APIOpen SourceAIFreemium
Overview
Mage, or Mage.ai, is an open-source data pipeline and orchestration tool for building, running, and managing ETL/ELT workflows that integrate and transform data. It was founded in 2020 by Tommy Dang and Xiaoyou Wang and is headquartered in Santa Clara, California, having raised roughly $11.8 million in seed funding led by Google's Gradient Ventures.
The project's core premise is consolidating data ingestion, transformation, and orchestration, traditionally handled by separate tools like Airflow, dbt, and Fivetran, into a single open-source platform with a notebook-style development experience, reducing both custom glue code and deployment complexity for data teams.
Key Features
Mage lets users build real-time and batch data pipelines using Python, SQL, and R within an interactive, notebook-like interface, with built-in scheduling, orchestration, versioning, and pipeline observability rather than requiring separate tools bolted together.
The commercial Mage Pro product extends the open-source core with managed cloud hosting, hybrid and private-cloud deployment options, team collaboration and governance features, and regional deployment availability across the US, Canada, Europe, Asia, and Australia.
Pricing
Mage's open-source core is free and MIT-licensed, and can be self-hosted at no cost. Mage Pro, the managed commercial product, starts with a Starter plan at around $100/month plus $0.29 per compute hour, moving up through Team, Plus, and Business tiers.
At the top end, Mage Pro's Enterprise plan runs around $25,000/month, with every paid tier billed on top of a base compute-hour rate that resets monthly along with block runs and AI token usage.
Key Features
Notebook-style pipeline builder — Build real-time and batch data pipelines using Python, SQL, and R inside an interactive, notebook-like development environment.
Built-in orchestration and scheduling — Native scheduling and orchestration remove the need to bolt on a separate workflow orchestrator like Airflow.
Pipeline versioning and observability — Tracks pipeline changes and surfaces run history and monitoring without extra tooling.
Open-source, self-hosted core — The MIT-licensed open-source edition can be deployed on a user's own infrastructure at no cost.
Managed cloud (Mage Pro) — A fully managed cloud deployment removes infrastructure setup, maintenance, and update burden.
Hybrid and private-cloud deployment — Enterprise customers can keep data processing inside their own private cloud while Mage manages the control plane, or run the entire platform privately.
Multi-region availability — Regional deployment options span the US, Canada, Europe, Asia, and Australia.
Compute-based billing — Paid Mage Pro tiers combine a base subscription fee with per-compute-hour usage charges.
Pros & Cons
Pros
Free, MIT-licensed open-source core with no cost to self-host.
Unified support for Python, SQL, and R in a single pipeline, reducing tool sprawl.
Notebook-style interface lowers the learning curve compared to code-only orchestrators like Airflow.
Clear upgrade path from free open source to a managed commercial product, Mage Pro, without switching tools.
Flexible deployment options, including private-cloud and hybrid configurations for enterprises with data residency requirements.
Cons
Mage Pro pricing combines a base subscription with per-compute-hour charges, which can be harder to predict than flat-rate pricing.
Smaller ecosystem and community than long-established orchestrators like Apache Airflow.
Enterprise tier pricing, around $25,000/month, is a significant commitment for large organizations.
As a younger, venture-backed company, long-term roadmap and support depend on continued funding or revenue growth.
Notebook-style pipelines may be less familiar to teams standardized on pure code-based orchestration like Airflow DAGs.
Pricing
Open Source Free N/A
Mage Pro Starter About $100/month plus $0.29 per compute hour monthly
Mage Pro Team About $500/month plus compute usage monthly
Mage Pro Plus About $2000/month plus compute usage monthly
Mage Pro Business About $5500/month plus compute usage monthly
Mage Pro Enterprise About $25000/month plus compute usage monthly
Frequently Asked Questions
Is Mage.ai free?
Yes, Mage's open-source core is free and MIT-licensed and can be self-hosted at no cost; the paid Mage Pro product adds managed cloud hosting and enterprise features.
What languages does Mage support for pipelines?
Mage pipelines can be built using Python, SQL, and R within its notebook-style interface.
How much does Mage Pro cost?
Mage Pro starts at around $100/month for the Starter plan plus $0.29 per compute hour, with Team, Plus, Business, and Enterprise tiers scaling up to roughly $25,000/month.
Who founded Mage.ai?
Mage was founded in 2020 by Tommy Dang and Xiaoyou Wang and is headquartered in Santa Clara, California.
Can Mage be deployed in a private cloud?
Yes, Mage Pro supports hybrid deployment, where the control plane runs in Mage's cloud but data processing stays in the customer's private cloud, as well as fully private-cloud deployment.
What are Mage's main competitors?
Mage competes with Apache Airflow, Dagster, Prefect, and dbt in the data pipeline and orchestration space.