AI Fundamentals

Foundation Model

A Foundation Model is a large AI model pre-trained on broad datasets that serves as a base for diverse downstream tasks. GPT-4, Claude, Llama, and Stable Diffusion are foundation models. They are trained once at great expense, then adapted to specific use cases via fine-tuning, prompt engineering, or RAG.

Why does this matter?

Foundation models fundamentally change AI economics: instead of training own models, companies build on existing foundation models. This lowers the entry barrier for AI projects by orders of magnitude. The strategic question is no longer "whether AI" but "which foundation model for which purpose."

How IJONIS uses this

We help you choose the right foundation model: GPT-4 or Claude for complex reasoning tasks, Llama or Mistral for privacy-sensitive on-premise scenarios, Stable Diffusion for image generation. Our AI consulting compares models based on your specific requirements — performance, cost, licensing, and GDPR compliance.

Frequently Asked Questions

What does it cost to use a foundation model?
Costs vary significantly: Cloud APIs (OpenAI, Anthropic) charge per token, costing EUR 50-500/month for typical business applications. Open-source models (Llama, Mistral) are license-free but require your own GPU servers. Total costs depend on your usage volume and hosting model.
Should my company use open-source or commercial foundation models?
Both have merit. Commercial models (GPT-4, Claude) offer top performance without infrastructure overhead. Open-source models (Llama, Mistral) offer maximum control and privacy. We often recommend a hybrid approach: cloud APIs for non-critical tasks, local models for sensitive data.

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Find out how we apply this technology for your business.