Do you need all nine layers of the GenAI stack to ship a product?
No. A minimal GenAI feature might need only three: cloud inference (or a hosted API), a foundation model, and a thin application layer calling it directly. The other layers — fine-tuning, vector databases, synthetic data, model supervision, model safety — are pulled in as specific needs appear: your own data grounding, domain specialization, monitoring at scale, or compliance requirements.
Answered in
The Generative AI Tech Stack, Layer by LayerFrom GPU infrastructure to model safety, a working map of the nine layers that turn a foundation model into a shippable GenAI product.
Read the full analysisOther questions this article answers
- What's the actual difference between the frameworks layer and the orchestration layer?
- Why does a GenAI stack need synthetic data as its own layer?
- What's the difference between model supervision and model safety in this stack?
- Why do vector databases specifically belong in the GenAI stack, and not just any database?
More how ai actually works questions
- Did the AI ultrasound study prove that it improves pregnancy outcomes?
- What does blind-sweep ultrasound mean in this study?
- Does Meta’s PrivacyLib watch every AI-agent action?
- What is the main design idea behind PrivacyLib?
- Is GPT-6.1 Terra available through the API?
- Do OpenAI's computer-use benchmarks guarantee reliable automation?
- What is semantic tokenization in a recommender system?
- Does Meta's reported conversion lift prove semantic tokens will improve every recommender?
Every answer on Crashtech is written by the editor of the article it comes from — never auto-summarised. Browse all answers or the How AI Actually Works beat.