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Coming soon
Coming soon

Yorizon Factory AI

Yorizon’s managed AI platform - Inference API, AI Workbench and model hosting on European GPU infrastructure.

Software developers, data teams, and business units that want to run open-source models and RAG applications in production—without their own GPU infrastructure, without model hosting complexity, and without data leakage to US cloud providers.

What Yorizon delivers

  • Inference endpoints for Llama 3.1, Mistral, DeepSeek

  • OpenAI-compatible REST API

  • AI Workbench with PyTorch, JAX, vLLM

  • Pay-per-use per million tokens

  • Hosting of your own open-source models by arrangement

  • Multi-tenant and white-label mode

Architecture & Technology

NVIDIA NIMs on B200 nodes under Direct Liquid Cooling. Multi-node inference via RoCEv2. OSISM (Sovereign Cloud Stack) with Ceph and Neutron.

Security & Sovereignty

  • Data never leave the Yorizon data center

  • EU-only data residency, no US Cloud Act

  • Addresses GDPR Art. 9, NIS2, EU AI Act

  • Tenant separation at the identity, network, and model levels

  • Audit logs for API calls

Service Level

  • 99.9% monthly availability

  • Token latency and throughput per model on request

  • Tiered service credits

Which models are available?

Phase 1: Llama 3.1 70B, Mistral NeMo, DeepSeek-R1. Phase 2: Llama 3.1 405B and Flux.1 (Image). Open-weights models with a hosting license, European models preferred.

Is the API OpenAI-compatible?

Yes, a fully OpenAI-compatible REST API for chat, completion, and embedding requests. Existing applications can be seamlessly integrated.

Where is my data?

Data never leave the Yorizon data center - no US Cloud Act. Addresses GDPR Article 9, NIS2 and the EU AI Act.

How much does it cost to use it?

Pay-per-use per million tokens or requests. Token latencies, throughput per model, and guaranteed quotas are agreed individually with the customer.

Can I host my own models?

Hosting finely tuned open-source models is optionally available (details on request). An AI workbench with PyTorch, JAX, and vLLM for fine-tuning is available.