Mage AI

Name: Mage AI
Brand: Mage AI

AI Native

Build, deploy, & run data pipelines through an intuitive interface in minutes. Run at any scale instantly with Mage Pro!

Est 2020|Valuation $30.5M|Raised $14.8M|2 Ref Customers

LinkedIn 21K|Web traffic 27K|FTEs 25|HQ Santa Clara, California, US

MarketsData Transformation Data Integration (ETL/ELT)

01Competitive Landscape

Narrative analysis

Mage vs Airflow - The vendor positions mage.ai as providing a more integrated development and debugging experience compared with Airflow, which is often used as a code-first orchestrator. Airflow remains widely used due to its extensive operator ecosystem and community support; many organizations accept higher operational cost in exchange for that maturity. Mage vs Dagster - Dagster is often chosen by organizations seeking a strong conceptual model for data assets, lineage, and long-term maintainability. Third-party comparisons indicate Dagster may provide stronger governance and lineage features, while mage.ai aims for interactive development and streamlined authoring. Mage vs Prefect - The vendor presents mage.ai as broader than orchestration alone (including connectors and authoring UI). Prefect is commonly selected for teams that prefer a lightweight orchestration layer and to keep transformation logic primarily in code repositories. Pricing comparison - Mage Pro publishes tiered pricing and highlights usage factors such as block runs and compute. Competitors use varying pricing models (monthly tiers, credits, or usage-based metrics). Buyers should confirm current pricing details and how usage maps to billing for each vendor. Market share and adoption trends - Public adoption signals in this dataset are primarily open-source activity indicators (GitHub). These signals demonstrate community interest but do not directly equate to enterprise penetration.

	Mage (Mage Pro)	Dagster Cloud	Prefect Cloud
Starting Price	Entry-level paid tier plus compute-based charges	Low-cost starter tiers available	Free tier available; paid tiers vary
Best For	Teams seeking notebook-style pipeline development and an integrated environment for integrations, transformations, and orchestration	Teams adopting an asset-based orchestration model with a focus on lineage and governance	Python-centric orchestration with a lightweight control plane and flexible execution options
Key Strength	Interactive pipeline building, multi-language blocks, and multiple deployment options (managed/hybrid/private/on-prem)	Strong asset model, lineage capabilities, and governance features	Flexible Python-first orchestration and a focus on execution flexibility
Key Weakness	Public third-party review footprint is harder to interpret due to name collisions; pricing complexity tied to block runs and usage limits	Less emphasis on notebook-style authoring; may require more engineering discipline to adopt	Historically Python-focused; users may need additional tooling for non-Python tasks

01Competitive Landscape

Narrative analysis

Mage vs Airflow

The vendor positions mage.ai as providing a more integrated development and debugging experience compared with Airflow, which is often used as a code-first orchestrator. Airflow remains widely used due to its extensive operator ecosystem and community support; many organizations accept higher operational cost in exchange for that maturity.

Mage vs Dagster

Dagster is often chosen by organizations seeking a strong conceptual model for data assets, lineage, and long-term maintainability. Third-party comparisons indicate Dagster may provide stronger governance and lineage features, while mage.ai aims for interactive development and streamlined authoring.

Mage vs Prefect

The vendor presents mage.ai as broader than orchestration alone (including connectors and authoring UI). Prefect is commonly selected for teams that prefer a lightweight orchestration layer and to keep transformation logic primarily in code repositories.

Pricing comparison

Mage Pro publishes tiered pricing and highlights usage factors such as block runs and compute. Competitors use varying pricing models (monthly tiers, credits, or usage-based metrics). Buyers should confirm current pricing details and how usage maps to billing for each vendor.

Market share and adoption trends

Public adoption signals in this dataset are primarily open-source activity indicators (GitHub). These signals demonstrate community interest but do not directly equate to enterprise penetration.

	Mage (Mage Pro)	Dagster Cloud	Prefect Cloud
Starting Price	Entry-level paid tier plus compute-based charges	Low-cost starter tiers available	Free tier available; paid tiers vary
Best For	Teams seeking notebook-style pipeline development and an integrated environment for integrations, transformations, and orchestration	Teams adopting an asset-based orchestration model with a focus on lineage and governance	Python-centric orchestration with a lightweight control plane and flexible execution options
Key Strength	Interactive pipeline building, multi-language blocks, and multiple deployment options (managed/hybrid/private/on-prem)	Strong asset model, lineage capabilities, and governance features	Flexible Python-first orchestration and a focus on execution flexibility
Key Weakness	Public third-party review footprint is harder to interpret due to name collisions; pricing complexity tied to block runs and usage limits	Less emphasis on notebook-style authoring; may require more engineering discipline to adopt	Historically Python-focused; users may need additional tooling for non-Python tasks