SitePoint7 min readintermediate
Why Your AI Video Pipeline Shouldn’t Be Built Around One Model
Summary
AI video generation APIs share a stable async task lifecycle but constantly change request payload details like duration limits and reference handling. Abstract those specifics behind a small VideoProvider interface and centralize validation to avoid rewrites whenever a provider updates.
- Implement a provider‑agnostic task lifecycle function; only the payload varies per vendor.
- Create a VideoProvider interface with generate() and provider‑specific subclasses handling validation and request building.
- Centralize reference‑asset validation so limits can be updated in one place when providers change.
- Add a lineage field to job state now to support multi‑round generation without later schema migrations.
Integration engineers building production AI video pipelines should care, as it prevents frequent rewrites when providers evolve their APIs.
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