# Model registry

**Source:** https://promtable.com/glossary/model-registry

> A model registry is the versioned store for trained model artifacts + metadata — the source of truth for which model version is in staging vs prod, what its eval scores were, and how to roll back. MLflow, W&B, Sagemaker, Vertex AI all ship one in 2026.

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A model registry is the versioned store for trained model artifacts + metadata — the source of truth for which model version is in staging vs prod, what its eval scores were, and how to roll back. MLflow, W&B, Sagemaker, Vertex AI all ship one in 2026.

Without a registry, 'which model is in prod?' becomes tribal knowledge. Registries solve this: every trained model gets versioned + tagged (stage: dev / staging / prod), linked to its training run, eval scores attached, and lifecycle hooks (promote, deprecate, roll back). For LLM apps the registry holds prompt versions + eval scores + linked datasets — same pattern, different artifact. Production patterns: every push to main triggers eval; promotion to prod requires passing baseline; rollback is one click. MLflow's registry is the open-source standard; cloud platforms (Sagemaker, Vertex AI, Azure ML) ship native equivalents. For LLM-only stacks, Langfuse / Braintrust / Opik provide prompt registries.

## When to use

- Production ML / LLM apps.
- Compliance / audit-heavy environments.

## Common mistakes

- Promoting without eval gates — broken model ships silently.
- No rollback drill — first incident discovers rollback is broken.

## Related terms

- [experiment-tracking](https://promtable.com/glossary/experiment-tracking)
- [shadow-deployment](https://promtable.com/glossary/shadow-deployment)
- [evals-driven-development](https://promtable.com/glossary/evals-driven-development)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/model-registry
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