# Mage: how AI models rank it, November 2026

Gane Index, November 2026 Edition. Named in 6 judge labels across 3 categories by 4 of 14 models. Page: https://www.gane.ai/index/it/vendors/mage

## Standing by category

| Category | Share | Rank | Negative rate | Labels |
|---|---|---|---|---|
| [Workflow orchestration](https://www.gane.ai/index/it/it-data/workflow-orchestration/index.md) | 0% | 26 | 25% | 4 |
| [Data security posture management](https://www.gane.ai/index/it/security/data-security-posture-management/index.md) | 0% | 91 | 100% | 1 |
| [ML platforms](https://www.gane.ai/index/it/it-data/ml-platforms/index.md) | 0% | 120 | 100% | 1 |

## What the models said for it

- "Choose Mage if you want a visual, less code-heavy experience for data pipelines." (GLM 4.7 FlashX, Orchestration)
- "The Lightweight Choice: Mage" (Qwen 3.7 Flash, Orchestration)

## And against it

- "Vulnerabilities were broken down for projects including Weave, ZenML, Deep Lake, Vanna.AI, and Mage AI" (Muse Glimmer 30B, ML platforms)
- "Niche (e.g., Slack/Zendesk-focused or AI-only) and may not cover end-to-end needs." (Grok 4.20, DSPM)
- "great for quick prototyping but less robust for production at mid-scale" (Grok 4.20, Orchestration)

## By model

| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 1 | 2 | 3 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 1 | 0 | 0 | 1 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 1 | 0 | 0 | 1 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 1 | 1 |

## Sources returned with these answers

The sites and pages cited by the answers that named Mage, from 8 of the 9 answers that named it (only the models that return a source list).

### Domains cited

- dataworkers.io: 3
- getorchestra.io: 3
- kestra.io: 3
- ajelix.com: 2
- datachannel.co: 2
- g2.com: 2
- skyvia.com: 2
- ai21.com: 1
- alexmerced.blog: 1
- atlan.com: 1

### Pages cited

- https://dataworkers.io/resources/data-orchestration-tools-2026: 3
- https://ajelix.com/ai/workflow-orchestration-tools: 2
- https://datachannel.co/blogs/top-7-data-orchestration-tools-2024: 2
- https://kestra.io/resources/infrastructure/workflow-orchestration-tools: 2
- https://ai21.com/knowledge/ai-data-privacy: 1
- https://alexmerced.blog/blog/2026-09-02-orchestration-in-2026.html: 1
- https://atlan.com/know/data-orchestration-tools: 1
- https://bestdevops.com/top-10-data-pipeline-orchestration-tools-features-pros-cons-comparison: 1

## In its own words

What Mage's own pages state, read 2026-10-05. Stated by the vendor, not checked by the index.

- Positioning: AI data engineering platform that runs production pipelines, checks data quality, fixes failures, and optimizes performance. (https://mage.ai/)
- For: data teams (https://mage.ai/)
- Price stated: Starter: $29 / month + $0.50 per CPU-hour; Team: $100 / month + $0.50 per CPU-hour; Enterprise: Contact sales; Additional AI usage is $0.12 per credit. (https://www.mage.ai/pricing)
- Starting price: 29 (https://www.mage.ai/pricing)
- Free plan or trial: Start building free (https://mage.ai/)
- Certifications: SOC 2 Type II

## What it publishes

Every page mage.ai exposes, read 2026-09-29: 819 addresses. By kind, pages and the last 90 days:

- blog: 157 pages, 1 in the last 90 days, latest 2026-09-01
- case study: 1 pages, 0 in the last 90 days, latest 2025-05-22
- report or ebook: 100 pages, 0 in the last 90 days, latest undated
- comparison: 8 pages, 0 in the last 90 days, latest 2026-05-06
- glossary or explainer: 55 pages, 0 in the last 90 days, latest undated
- Recent: 2026-09-01 blog: Data pipeline monitoring reliability production guide https://www.mage.ai/blog/data-pipeline-monitoring-reliability-production-guide
- Recent: 2026-05-12 blog: Build data pipelines with your ai coding assistant meet mage agent https://www.mage.ai/blog/build-data-pipelines-with-your-ai-coding-assistant-meet-mage-agent
- Recent: 2026-05-06 comparison: Mage vs dbt fivetran choosing the right stack for ai ready data pipelines https://www.mage.ai/blog/mage-vs-dbt-fivetran-choosing-the-right-stack-for-ai-ready-data-pipelines
- Recent: 2026-04-08 blog: The real cost of tool sprawl why your modern data stack is slowing you down https://www.mage.ai/blog/the-real-cost-of-tool-sprawl-why-your-modern-data-stack-is-slowing-you-down
- Recent: 2026-03-25 blog: Why your ai is confidently wrong the silent data gap problem https://www.mage.ai/blog/why-your-ai-is-confidently-wrong-the-silent-data-gap-problem
- Recent: 2026-03-18 comparison: Mage vs airflow for ml and llm workflows https://www.mage.ai/blog/mage-vs-airflow-for-ml-and-llm-workflows

## Record

- Method: https://www.gane.ai/index/it/methodology
- License: CC BY 4.0. Cite as Gane Index, November 2026 Edition, www.gane.ai/index/it.