Mage unifies ingestion, transformation, orchestration, and execution in a single system. So you data stays reliable across every system it powers — dashboards, decisions, products, and AI.
Build with SQL, Python, R, and dbt.
Only recompute what changes.
The
Runtime
for
Data
Workflows
Connect data and build a workflow in minutes
Most data workflows execute and disappear
Pipelines run, produce outputs, and lose the execution context needed to recover, reuse, or build reliably on top of them. Your team compensates by stitching together orchestration tools, retries, backfills, monitoring systems, caches, and custom recovery logic.
The result is operational complexity that grows faster than the workflows themselves.
Mage preserves execution
as infrastructure
Instead of treating runs as disposable, Mage turns execution into reusable system state.
Recover failed workflows without rerunning everything. Reuse outputs across pipelines, APIs, analytics, and AI systems. Replay execution deterministically as logic and data evolve.
Run ingestion, transformation, orchestration, and recovery in one runtime.
Superpower
One runtime across
the data lifecycle
Ingest
Pull from APIs, databases, warehouses, SaaS platforms, files, and streams with scheduled or real-time execution.
Transform
Build workflows in SQL, Python, R, and dbt with dependency management, scheduling, and version-controlled logic.
Deliver
Publish reusable outputs for dashboards, applications, APIs, automations, and AI systems.
Operational Advantage
Built for workflows
that run continuously
Partial reruns instead of full pipeline rebuilds
Replay execution with preserved runtime state
Backfill only what changed
Centralized execution history and debugging
Batch and streaming in the same system
Shared reusable outputs across workflows
Deploy where your data runs
Fully managed — Run Mage without managing infrastructure.
Hybrid and self-hosted — Keep execution inside your environment with centralized management and governance
AI systems need
reproducible context
Most AI systems operate on fragmented, non-reproducible data pipelines.
Mage turns workflow outputs into reusable execution context that
AI systems can safely depend on in production.
Shared outputs instead of duplicated retrieval pipelines
Observable execution history behind generated results
Recoverable and reproducible data context over time
Build
Workflows
Survive
Production
Product
Solutions
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