Model Registries for Generative AI

A model registry is a structured inventory of the models a team uses or deploys. In generative AI, this includes not only weights and versions, but also prompts, adapters, safety notes, evaluation results, and rollout status.

What a Registry Tracks

• Model name and version
• Provider or training source
• Intended use cases
• Evaluation scores and known limitations
• Deployment environments and approval state

Why It Matters

Without a registry, teams lose track of which model is serving which feature, what changed between releases, and whether a model was ever approved for a given workflow.

Operational Value

A good registry improves governance and debugging at the same time. It gives product, engineering, and risk teams a shared source of truth for the AI stack.