Put your data pipelines on autopilot

Mage is an AI data engineering platform that runs production pipelines, checks data quality, fixes failures, and optimizes performance.

Samsung
IBM
JPMorgan Chase
Nokia
Stability AI
Comcast
Ion Solar
Nursa
Avanza

Build data pipelines with AI, explore your data, create charts, and give agents reliable context

Describe what you need, and AI creates a scheduled data pipeline with code and tests.

Keep production data pipelines reliable with automatic validation, recovery, and performance optimization

Vendor feedRecovered automatically
Write to BigQuery
Source schema changedSafe fix applied

Affected steps replayed · Checks passed

Recover failed pipelines automatically
Self-healing pipelines diagnose failures, apply safe fixes, and recover automatically.

Only write accounts with valid domains
41records written
2invalid domains blocked

Output rules verified

Catch data issues early
Automated data validation catches quality issues before they reach your dashboards, applications, and business teams.

Pipeline optimizationCalculate order totals
Replaced row-by-row processing
Before
df["total"] = df.apply(
    lambda row: row["price"] * row["quantity"],
    axis=1,
)

Keep pipelines fast as your data grows
Mage identifies bottlenecks and automatically optimizes code and execution to keep your pipelines fast as your data grows.

Explore the platform

Use cases

Turn manual data work into reliable automation

Start with the work your team repeats, and let Mage keep it running as your data and business needs change.

Automate data integration across your systems

Process vendor feeds and keep databases and business applications in sync with automated data pipelines.

Explore automation

Modernize legacy SQL and spreadsheet workflows

Turn existing SQL and spreadsheet logic into automated data models that adapt as source schemas change.

Explore data modeling

Give AI agents accurate, up-to-date business data

Prepare and validate business data for AI agents, with current context tailored to the task each agent needs to do.

Explore AI-ready data

Connect your data sources and run SQL, Python, and dbt together

Use built-in or custom connectors to move data between your databases, warehouses, files, and business applications.

Step 1

Connect data sources
Connect your databases and external APIs with built-in or custom data connectors.

Step 2

Run SQL, Python, and dbt
Schedule your existing code and manage dependencies in one workflow.

Step 3

Deliver data to your tools
Send data to warehouses, business applications, dashboards, and AI agents.

Run enterprise data pipelines with flexible deployment and access controls

Choose cloud or self-hosted deployment with isolated workspaces, role-based permissions, and expert support.

Flexible deployment

Deploy pipelines where your data belongs

Run in the cloud, private cloud, or on premises to meet your infrastructure and security needs.

Enterprise controls

Control team access and production changes

Give teams isolated workspaces, set access by role, and review changes before production.

Expert support

Get expert guidance from migration onward

Plan your architecture, migrate data pipelines, and resolve issues with priority support.

Explore enterprise

What data work will you automate first?

Keep customer dashboards up to date.

Give our AI agent accurate business data.

Sync Stripe and Salesforce every hour.

Prioritize customer accounts that need attention.

Automatically recover failed data pipelines.

Turn vendor files into clean, validated datasets.