Why a Multimodal Warehouse When Vector DBs Already Exist?
Summary
One app searching one modality is easy: embed, store, done. But every app needs every modality, so teams rebuild the same chunk-extract-embed-index pipeline until 5 apps x 4 modalities is 20 pipelines processing the same footage, with every copy drifting out of sync. The gap: a vector database stores vectors, while extraction (how raw video becomes vectors) is the expensive part everyone duplicates. A multimodal data warehouse extracts once, upstream of every app: scenes, transcripts, faces, OCR, and embeddings become queryable timestamped features, indexes build from them, and retrievers compose search, filter, and rerank on top, so a new app is a query, not a pipeline. Explore: mixpeek.com/comparisons/multimodal-data-warehouse-vs-vector-database
About this video
One app searching one modality is easy: embed, store, done. But every app needs every modality, so teams rebuild the same chunk-extract-embed-index pipeline until 5 apps x 4 modalities is 20 pipelines processing the same footage, with every copy drifting out of sync. The gap: a vector database stores vectors, while extraction (how raw video becomes vectors) is the expensive part everyone duplicates. A multimodal data warehouse extracts once, upstream of every app: scenes, transcripts, faces, OCR, and embeddings become queryable timestamped features, indexes build from them, and retrievers compose search, filter, and rerank on top, so a new app is a query, not a pipeline. Explore: mixpeek.com/comparisons/multimodal-data-warehouse-vs-vector-database