Open-Source Databases · mage-ai

mage-ai

Mage is an open-source, self-hosted data pipeline platform built in Python for creating, running, and managing ETL/ELT workflows locally. It provides a visual notebook-style interface with modular block execution, scheduling, and integrations to databases and cloud storage, with an optional enterprise platform (Mage Pro) for scaling.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/mage-ai/mage-ai

8.8k

GitHub stars

978

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	mage-ai/mage-ai
Owner	mage-ai
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	8.8k
Forks	978
Open issues	616
Latest release	0.9.79 (2026-01-21)
Last updated	2026-07-02
Source	https://github.com/mage-ai/mage-ai

What mage-ai is

Mage OSS is a Python-based orchestration and data integration framework offering modular pipeline composition, cron scheduling, dbt integration, and visual debugging with prebuilt connectors. The platform supports Python, SQL, and R transforms and runs locally via Docker, pip, or conda without requiring external cloud infrastructure.

Quickstart

Get the mage-ai source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/mage-ai/mage-ai.git2cd mage-ai3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Local ETL/ELT Development & Prototyping

Teams building and testing data pipelines locally benefit from Mage's notebook-style UI, visual debugging, and modular block execution. Ideal for rapid iteration before production deployment.

dbt Model Development & Orchestration

Data teams can author and run dbt models directly within Mage's visual interface, combining transformation logic with scheduling and monitoring in a single workspace.

Small-to-Medium Scale Scheduled Data Jobs

Simple daily or hourly ETL tasks connecting databases, APIs, and cloud storage (e.g., Google Sheets → Snowflake) are straightforward to implement with prebuilt connectors and cron scheduling.

Implementation considerations

Deploy via Docker (recommended), pip, or conda; ensure Python 3.8+ and compatible OS environment; plan for persistent storage of pipeline code and execution logs.
Configure database and API credentials securely (environment variables, secrets manager); vet prebuilt connectors for your specific data sources before committing.
Set up cron or external scheduler for production runs; Mage OSS runs jobs synchronously on a single machine by default—scale horizontally requires manual orchestration or Mage Pro.
Monitor logs and establish error handling per pipeline; no built-in alerting in OSS, so integrate with external monitoring (e.g., Datadog, PagerDuty) if needed.
Plan database schema and transformation logic upfront; modular block design helps, but large pipelines can become complex without clear naming and documentation discipline.

When to avoid it — and what to weigh

Requires Mission-Critical Enterprise SLAs — Mage OSS is self-hosted; no SLA, managed support, or guaranteed uptime. For production workloads requiring enterprise guarantees, Mage Pro or dedicated platforms are necessary.
Large-Scale Distributed Orchestration Needed — While Mage supports Spark jobs, it is not a replacement for Airflow, Prefect, or Dagster at massive scale. Use those platforms if you need sophisticated DAG orchestration across thousands of workflows.
Strict Vendor Lock-In Concerns — Mage OSS pricing model channels users toward Mage Pro. If avoiding vendor dependency is critical, consider neutral orchestrators like Airflow or Prefect.
Minimal Python/Engineering Resources — Mage assumes technical proficiency with Python, databases, and deployment. Teams without DevOps or engineering capacity may face steep onboarding and maintenance burden.

License & commercial use

Mage OSS is licensed under Apache-2.0 (Apache License 2.0), a permissive, OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions.

Apache-2.0 permits commercial use without royalties. No license restrictions on derivative works or proprietary deployment. However, review Mage's terms of service and support policies separately; the free OSS version includes no commercial support contract. Verify compliance with any internal IP or dependency policies before production use.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Mage OSS is self-hosted, shifting security responsibility to the operator. Sensitive considerations: credential management (env vars, secrets tools recommended), network isolation of the Mage instance, database connection encryption, and audit logging of pipeline executions. No formal security audit or penetration testing data provided. Review code for third-party dependencies before using in sensitive environments. Apply standard DevOps hardening practices (least privilege, TLS, firewall rules).

Alternatives to consider

Apache Airflow

Mature, battle-tested orchestrator with rich DAG support, massive community, and sophisticated scheduling. Better for complex, large-scale workflows but steeper learning curve and operational overhead.

Prefect

Modern, Python-native orchestration with strong error handling and dynamic workflows. Prefect Cloud offers managed platform; OSS version also available. More flexible DAG model than Mage but less integrated notebook-style UI.

dbt Cloud + dbt Core

Purpose-built for dbt workflows with managed orchestration, discovery, and lineage. Lighter than Mage if transformation-only; less versatile for heterogeneous ETL but excellent dbt focus.

Software development agency

Build on mage-ai with DEV.co software developers

Evaluate Mage OSS for your ETL/ELT workflows. Our team can help you assess deployment fit, design custom integrations, and plan production scaling strategies.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

magic-api DB-GPT rom

Related on DEV.co

Explore the category and the services that help you build with it.

Open-Source Databases AI Application Development DevOps & Infrastructure Custom Software Development Talk to DEV.co

mage-ai FAQ

Can I run Mage in production?

Yes, but with caveats. Mage OSS is self-hosted and single-machine by default; production deployments require custom scaling, monitoring, and error handling. Mage Pro offers managed production features. Evaluate your SLA and operational capacity carefully.

Is there commercial support for Mage OSS?

Not directly. Mage offers paid support and Mage Pro (managed platform). Community Slack and GitHub issues are the primary OSS support channels. Budget internal engineering time for troubleshooting and maintenance.

How does Mage handle job scheduling and parallelization?

Mage OSS supports cron-style scheduling and manual triggers. Jobs execute synchronously on a single machine; parallelization of multiple pipelines requires external orchestration (e.g., systemd, Kubernetes) or Mage Pro.

What's the difference between Mage OSS and Mage Pro?

OSS is self-hosted, local-first, and open-source. Mage Pro adds multi-environment orchestration, RBAC, AI-assisted workflows, managed hosting, and enterprise support. OSS is free; Pro is commercial.

Custom software development services

From first prototype to production, DEV.co delivers software development services around tools like mage-ai. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source databases and beyond.

Ready to streamline your data pipelines?

Evaluate Mage OSS for your ETL/ELT workflows. Our team can help you assess deployment fit, design custom integrations, and plan production scaling strategies.

Talk to DEV.co Schedule a Consultation