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Build and run pipelines
that make data AI-ready

Build and run pipelines
that make data AI-ready

Ingest, transform, and stream production data using SQL, Python, R, and dbt with built-in orchestration, scheduling, and backfills.

Ingest, transform, and stream production data using SQL, Python, R, and dbt with built-in orchestration, scheduling, and backfills.

Power analytics, applications, and AI systems

Run pipelines that keep your data current and reliable in production.

Build pipelines

Across SQL, Python, R, and dbt, with full control over logic and execution

Connect data

From APIs, databases, and streams into a single, consistent system

Run continuously

On schedules or real-time triggers with managed execution

Fix and recover

Backfill data and resolve failures without rerunning entire pipelines

Reuse data

Share datasets and logic across teams without rebuilding

Power AI

Use production data to power AI systems and applications

How Mage fits into your stack

Mage sits between your data sources and the systems that use your data.

From raw data to production-ready outputs

From raw data to production-ready outputs

Step 1

Connect

Bring in data from your existing sources. Databases, warehouses, lakes, SaaS tools, and APIs.

Step 2

Execute

Run pipelines on your data. Use SQL, Python, R, and dbt with scheduling and managed execution.

Step 3

Deliver

Make data available for use. Publish datasets for dashboards, APIs, applications, and AI systems.

Workflows

  • SQL, Python, R, and dbt pipelines

  • Dependencies and execution order

  • Batch and streaming support

Ingestion

  • APIs, databases, warehouses

  • Scheduled and real-time syncs

  • Schema validation

Orchestration

  • Scheduled and event-based runs

  • Dependency management

  • Centralized run monitoring

Reliability

  • Backfills and partial reruns

  • Execution history and debugging

  • Testing and validation

Reuse

  • Reusable data and logic

  • Shared outputs across workflows

  • Version tracking over time

AI

  • Code generation and optimization

  • Natural language debugging

  • Run AI systems on production data

Use cases teams rely on

Running critical systems in production.

Build, run, fix, and reuse pipelines in one place

Simple to operate. Fast to recover. Built for production.

Unified execution

Run ingestion, transformation, and orchestration in one system instead of stitching tools together.

Modular runtime

Fix failures without rerunning entire pipelines. Backfill only what changed and recover quickly.

Build once, reuse everywhere

Reuse data and logic across analytics, automation, and AI systems without rebuilding.

Trusted every day by titans, unicorns, and dreamers

Faster execution, less overhead

When execution is reliable and reusable, teams move faster with less operational work.

Scale without scaling costs

Increase throughput as the business grows without adding tools or headcount.

  • Consolidate expensive point tools and DAG sprawl into a single execution surface

  • Lower total cost of ownership by reducing external orchestration and operational overhead

Iterate faster, safer

Ship changes without breaking production by making execution reproducible and recovery routine.

  • Promote updates through environments with controlled releases and preserved run history

  • Replay and partially re-run only what changed, so fixes and backfills are fast and low-risk

Watch

See how teams
move faster and safer

See how teams
move faster and safer

Deploy and run your way

Flexible deployment options designed to fit cleanly into your environment across scale, security, and performance needs.

Get Started

Get Started

Build your first data workflow in minutes and start delivering data you can depend on.