Mage open source and Mage Pro
Start with open source data pipelines, then move to Mage Pro when your team needs managed deployments, orchestration, observability, support, and enterprise controls.
Mage 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.
The open source project is still valuable for teams that want a developer-first way to build pipelines in Python, SQL, and notebooks. Mage Pro is 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 open source is enough
Use Mage open source 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.
Open source vs Pro
This is the practical split. Open source helps individuals and smaller teams build pipelines. Mage Pro helps companies operate those pipelines reliably across people, systems, and environments.
| Need | Mage open source | 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 gives teams a path from open source 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 open source if you are learning Mage, 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.