Azure AI Foundry
Per-org deployed LLMs, embeddings, images, and audio models
Vendor ID: azure-foundry · Maturity: preview · Categories: AI
Azure AI Foundry — LLMs, embeddings, images, and audio models deployed per-org. Foundry endpoints are tenant-specific; the integration accepts baseUrl + projectEndpoint config so each org points at its own deployment.
Auth
| Credential | Notes |
|---|---|
apiKey | Foundry-issued API key. Tenant endpoint configured separately. |
Capabilities
| Capability | Notes |
|---|---|
| LLM | Capability-blind discovery — Foundry deployment listings sometimes need a classifier cascade. The vendor implements discoverUnclassified() so admins can review unrecognized deployments. |
| Embedding | — |
| Image | — |
| Audio | STT + TTS where the deployment supports them |
There are no stand-alone tool products on this vendor — every capability resolves through the integration registry.
Model discovery
After you configure and enable Azure AI Foundry, open Settings → Platform → Integrations → Available, expand Azure AI Foundry, and select a capability badge such as LLM. Scrydon discovers deployments for the exact connection and active workspace environment, then shows the cached model inventory in the popover. Use Refresh models to query Azure again after adding, removing, or reclassifying a deployment.
The configured model allowlist is policy, not a discovery cache. An empty allowlist can block execution without hiding which deployments Azure returned from the administrator inventory.
If no models appear, verify that the integration is enabled, the API key is
current, and projectEndpoint/baseUrl identifies the Foundry resource that
contains your deployments. Then select Refresh models. Discovery remains
server-side: the browser never receives the API key or calls Azure directly.