Model Governance
Do not select the most capable model by default. Maintain an approved portfolio that matches models and deployments to use-case sensitivity, quality, cost, latency, throughput, availability, and processing-location requirements.
Model Register
For each approved model connection, record:
| Field | Why it matters |
|---|---|
| Provider and model family | Contract, capability, support, and risk ownership |
| Deployment name and type | Runtime configuration and processing boundary |
| Region or data zone | Residency and operational review |
| Approved use cases | Prevents accidental use in unsuitable scenarios |
| Preview or production status | Preview terms and behavior may differ |
| Input/output modalities | Defines data types the model can receive and produce |
| Evaluation result | Shows quality and safety evidence for the intended use |
| Quota, limits, and cost owner | Supports capacity and budget control |
| Retirement or review date | Prevents unmanaged model drift and end-of-life risk |
Portfolio Pattern
Use high-capability reasoning models for complex planning and analysis, smaller efficient models for high-volume extraction or classification, embedding models for retrieval, and specialized audio or image models only where required. Separate highly regulated or deterministic workloads from general assistants.
If a model router is used, approve the routing mode and allowed model subset. The router does not remove the need to review every possible underlying model and the deployment's processing location.
Change Control
Re-evaluate a production agent when the provider changes a model version, a preview becomes generally available, a model is retired, routing membership changes, or deployment type changes. Preserve the previous evaluation result and rollback decision.
See Azure AI Foundry and Model Deployment and Data Residency.