Workflow orchestration · Mage Technologies, Inc.

Mage

Notebook-style, open-source pipeline builder combining ingestion, transformation, and orchestration with a block-based visual editor.

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Mage is an open-source data pipeline tool that blends orchestration with a notebook-like development experience: pipelines are built as a sequence of interactive 'blocks' (SQL, Python, or R) that can be run and previewed individually, then chained into a DAG for scheduled execution. This makes it closer to a hybrid of Airflow-style orchestration and a Jupyter-notebook workflow than a pure task scheduler. Mage includes built-in data integration connectors (an Airbyte-compatible source/destination framework), streaming pipeline support via Kafka, and a visual pipeline editor alongside a code-first API. The open-source core is self-hosted for free; Mage's managed cloud offering adds hosted compute billed per developer seat plus CPU-hour usage, along with AI features for pipeline generation. It suits smaller data teams that want ingestion, transformation, and orchestration without assembling separate tools.

At a glance

Vendor Mage Technologies, Inc.
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Smaller teams wanting ingestion, transformation, and orchestration together in one lightweight, notebook-style tool.

Pricing

The open-source tool is free to self-host; the managed cloud plans are a flat monthly seat fee plus per-CPU-hour compute usage.

Plan Price Notes
Starter $29/month + $0.50/CPU-hour 1 developer seat, 1 environment, 50 AI credits/month
Team $100/month + $0.50/CPU-hour Up to 10 developer seats, 250 shared AI credits/month
Enterprise Custom pricing Unlimited seats, private/hybrid deployment, dedicated support

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Block-based, notebook-style pipeline development
  • Built-in ingestion connectors (Airbyte-compatible)
  • Streaming pipeline support via Kafka
  • Visual pipeline editor plus code-first API
  • Scheduling, retries, and monitoring dashboard
  • AI-assisted pipeline and code generation
  • Self-hosted or managed cloud deployment

Integrations

Profile last reviewed September 21, 2026

Alternatives

Dagster Asset-centric orchestrator that models pipelines around the data they produce, with built-in testing, typing, and observability. Prefect Python-native workflow orchestrator built around plain functions and dynamic flows, with a managed cloud control plane for scheduling and observability. Kestra Open-source, event-driven orchestration platform defining workflows declaratively in YAML, with 800+ plugins for data, infra, and AI tasks.

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Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed.

Related guides

How to choose a workflow orchestrator Pick a workflow orchestrator by pipeline shape, who owns it, and whether you already run Kubernetes — not by the demo DAG.