Azure AI Foundry Video generation and editing

Capability boundary

The Azure AI Foundry Video generation and editing catalog describes the jobs this class of model is intended to solve. It does not mean that every listed model shares request fields, input limits, or output formats. Read each model card together with its lifecycle status, origin, and official source. A platform provider may host models from several original vendors, so both the displayed ID and origin matter.

Selection criteria

Video generation is commonly an asynchronous job and should not be integrated like a text response. Evaluate text-to-video, image-to-video, first- and last-frame control, and reference-driven generation independently. Measure duration, resolution, frame rate, camera motion, physical consistency, identity preservation, and continuity across shots. Also verify polling or callback behavior, bounded retries, generation latency, download retention, soundtrack support, and the effect of safety review on the complete workflow.

Integration and protocol boundary

Confirm the exact model ID, authentication method, endpoint, and request schema in the Azure AI Foundry official documentation, then look for the same exact ID and supported endpoint in this site's model marketplace. Changing only the base URL does not guarantee an identical response contract. Streaming events, asynchronous jobs, file uploads, tool calls, and error payloads may require separate adapters.

Production checklist

Build a repeatable evaluation from representative inputs and record the model ID, parameters, input version, and result. Measure success rate, tail latency, throttling, timeout behavior, cost per completed task, and safety outcomes. Prepare a replacement path for preview or legacy models. The official catalog explains product boundaries; callable availability, channel status, and current pricing remain the responsibility of the model marketplace.