As soon as AI moves beyond a pilot, teams face a basic problem: nobody knows which model version is running where, what policy constraints apply, and what evidence supports its release. A model registry turns that ambiguity into a managed lifecycle.
What a registry is (and is not)
A registry is not just a list of model names. It is a source of truth for:
- Version lineage. Model/provider, fine-tuning, prompt templates and data dependencies (see model cards and lineage).
- Policy constraints. Data residency, safety requirements and tool permissions (see policy layering).
- Release evidence. Benchmarks, red team results, and operational readiness (see enterprise benchmarking).
Minimum metadata that pays off
Keep the registry lightweight but complete. Capture:
- Model identifier, provider, region, and supported context window.
- Intended use cases and disallowed use cases.
- Prompt template versions and key policy prompts.
- Tooling scope and authorisation patterns (see tool authorisation).
- Evaluation suite and most recent scores.
- Operational SLOs and incident ownership (see SLO playbooks).
Deployment gates that match risk
Not every model needs the same controls. Use gates based on risk appetite:
- Sandbox gate. Basic safety checks and a clear data boundary.
- Production gate. Benchmark results, privacy review, and a rollback plan.
- Regulated gate. Evidence packs and traceability suitable for audits (see compliance audits).
Make routing and rollback registry-driven
In practice, the registry becomes most valuable when it drives runtime decisions. For example, a router can use registry metadata to enforce residency rules, choose fallback models, and explain why a route was chosen (see routing and failover).
Support investigations with good records
When something goes wrong, teams need to reconstruct what changed. Connect the registry to incident response by recording which model, prompt version and policy pack were active for each request (see incident response).
The outcome is simpler than it sounds: fewer unknowns, faster rollbacks, and clearer accountability when AI systems evolve.