Mage OSS and Mage Pro
Mage AI is available as Mage OSS for self-managed data pipelines and Mage Pro for teams that need managed deployment, observability, support, and enterprise controls.
Mage AI started with a simple idea: data engineers should be able to build and run data pipelines without fighting fragile orchestration, scattered scripts, and slow handoffs between tools.
Mage OSS is the open source edition for teams that want a developer-first way to build pipelines in Python, SQL, and notebooks. Mage Pro is the commercial offering for teams that need those pipelines to run as a governed production system with managed infrastructure, deployment controls, monitoring, support, and AI-assisted workflow development.
When Mage OSS is enough
Use Mage OSS when you are experimenting, learning, prototyping, or running pipelines where your team is comfortable owning the operational work.
- Build data pipelines with Python, SQL, and notebooks.
- Develop locally and keep workflow logic visible in code.
- Orchestrate loaders, transformers, exporters, sensors, and schedules.
- Use a developer-friendly interface without giving up code-level control.
When teams move to Mage Pro
Teams usually move to Mage Pro when pipeline work becomes business-critical and the cost of owning everything around the open source project starts to exceed the value of doing it yourself.
- Managed cloud, hybrid, private-cloud, and on-premises deployment options.
- Production orchestration, scheduling, retries, backfills, observability, and alerting.
- Workspace, environment, user, role, secret, and deployment controls.
- Priority support for critical pipelines, migrations, integrations, and production incidents.
- AI assistance that works inside the workflow surface with pipeline context.
Mage OSS vs Mage Pro
This is the practical split. Mage OSS helps individuals and smaller teams build self-managed pipelines. Mage Pro helps companies operate those pipelines reliably across people, systems, and environments.
| Need | Mage OSS | Mage Pro |
|---|---|---|
| Build data pipelines | Yes | Yes |
| Python, SQL, and notebook workflow development | Yes | Yes |
| Managed infrastructure | Self-managed | Yes |
| Hybrid, private-cloud, and on-prem deployment | Self-managed | Available |
| Production observability and alerting | Self-managed | Built in |
| Enterprise access controls | Self-managed | Built in |
| Priority support | Community | Available |
| AI-assisted pipeline work | Limited | Built in |
Why this matters for data engineering teams
The hard part is rarely writing one pipeline. The hard part is keeping many pipelines correct, visible, recoverable, and useful as the company depends on them.
Mage AI gives teams a path from Mage OSS experimentation to production-grade data workflow operations without changing the basic mental model: build clear pipelines, orchestrate them, inspect what happened, and recover when something breaks.
Recommended path
- Start with Mage OSS if you are learning Mage AI, proving a pipeline pattern, or evaluating the developer experience.
- Use Mage Pro when pipelines support revenue, operations, customer workflows, AI systems, or executive reporting.
- Talk to Mage when you need help choosing between cloud, hybrid, private-cloud, or on-premises deployment.
Review deployment options, compare plans, or talk to sales about your production pipeline requirements.