Implementing Video Intelligence in the Cloud: AI Video Analysis with TwelveLabs Part 5 – Comparing Vector DBs for Video Embeddings | Amazon Web Services

Series demonstrates step-by-step how to use TwelveLabs video intelligence in an AWS cloud environment.

The final installment addresses the questions to answer when applying the embedding strategies from part 4 to real services.

Embeddings are produced with twelvelabs.marengo-embed-3-0-v1:0 on Amazon Bedrock as visual vectors: each temporal segment yields a 512‑dim vector; short clips create 2–7 segments, long videos create hundreds–thousands.

Test dataset used ten videos (~1GB) and produced a total of 2,285 vectors.

Network tests ran from same‑region EC2 and used VPC Endpoints with Private DNS for OpenSearch Serverless and S3 Vectors.