Once Sora becomes widely used, video content will explode—hundreds of thousands or millions of short AI-generated videos daily. To make that content usable (searchable, remixable, retrievable), systems will need video embeddings indexed in vector databases. Developers will expect to query “videos like this scene,” “frames matching this style,” or “previous video parts for remixing.” This raises demand for vector DBs to natively support video or frame embeddings, temporal embedding indexing, and cross-modal retrieval (text → video, image → video).
Moreover, real-time or near-real-time embedding insertions and queries will be essential in interactive applications. For example, during video generation or remixing, a system might query similar frames, styles, or transitions to guide coherence or reduce drift. The vector DB must handle high write throughput (new embeddings), low-latency queries, and updates or deletions (when videos are revoked or edited). Because video embeddings are higher-dimensional and possibly heavy, compression, quantization, partitioning, and approximate search algorithms need to scale well.
In effect, Sora’s adoption will shift vector DB systems from being primarily text / image retrieval engines to fully multimodal video indexing platforms. This conversion drives requirements for efficient embedding management, temporal / sequential retrieval, versioning, hybrid search, and deletion / update operations at scale.