Your sovereign data pipelines
An open-source data and AI orchestration tool powered by Mage, installed and maintained by DINAO. Your data flows on our French servers — never in a third-party cloud.
What is Mage AI?
Mage AI is an open-source tool for building, running, and managing data pipelines for integration and transformation (ETL/ELT). Positioned as a self-hostable alternative to Apache Airflow, it offers a visual notebook-style interface to design workflows before scaling them.
Modular pipelines are built in Python, SQL, or R, with manual or scheduled (cron) execution, visual debugging (logs, live previews, step-by-step execution), and pre-built connectors to databases, APIs, and cloud storage. It also runs dbt models directly.
Built in Python (backend) and TypeScript (frontend), Mage deploys via Docker and exposes an API to manage pipelines. A data and AI tool, it can feed inference processes — at DINAO, connected to a self-hosted model, it keeps the entire data chain within the country.
Host Mage AI at DINAO
Resource tiers compatible with Mage AI prerequisites (minimum 2 cœurs / 2 Go / 5 Go). Hosted in France, fully managed.
- 2 dedicated vCPU
- 4 Go RAM
- 40 GB NVMe
- Daily backups
- Managed & monitored by DINAO
- 4 dedicated vCPU
- 8 Go RAM
- 80 GB NVMe
- Daily backups
- Managed & monitored by DINAO
- 8 dedicated vCPU
- 16 Go RAM
- 160 GB NVMe
- Daily backups
- Managed & monitored by DINAO
1 hidden tier(s) (insufficient resources for this app) : Découverte
This application uses AI
The container hosts the application, not the AI engine (which requires dedicated GPUs) : inference is handled externally, with your own provider key. Prioritize a sovereign engine — Mistral AI, NumSpot, Scaleway, or OVHcloud AI Endpoints (France, GDPR) ; an international provider (OpenAI, Anthropic…) only if a specific capability requires it. Inference subscriptions are not included in hosting.
Technical details
You might be wondering…
Does my data leave my infrastructure?
No. The open-source edition of Mage is fully self-hosted: your datasets and transformations remain on your DINAO instance in France.
What languages are used to write pipelines?
Python, SQL, or R, within a notebook-style interface that combines code, documentation, and live data preview.
Can processing be scheduled?
Yes. Mage supports scheduled executions (cron support) in addition to manual runs, with logs and step-by-step visual debugging.
Can Mage AI feed sovereign AI processing?
Yes. Mage orchestrates both data and AI pipelines; it can be connected to a self-hosted model via the DINAO inference funnel to remain 100 % in France.
Can I change plans or export my pipelines?
Yes. Your pipelines and data remain exportable — no vendor lock-in — and you can upgrade or downgrade at any time.
