Video Content Moderation: AWS Managed Services vs. Open-Source Models
I have built video content moderation pipelines both ways: one using AWS managed AI services orchestrated by Step Functions, another using open-source models running on SageMaker endpoints orchestrated by SageMaker Pipelines. Both architectures process uploaded video, detect unsafe visual content, transcribe audio for toxic language analysis, and route flagged material to human reviewers. They solve the same problem with fundamentally different trade-offs in cost, accuracy, operational overhead, customization depth, and data control. This article is the comparative analysis. I break down every dimension that matters when making this architectural decision, with real pricing data, accuracy benchmarks, and operational experience from running both approaches in production. For the full implementation details, see the companion articles: Video Content Moderation with Step Functions and AWS AI Services for the managed services approach and Video Content Moderation with SageMaker Pipelines and Open-Source Models for the open-source approach.