Unlocking video insights at scale with Amazon Bedrock multimodal models

Video content is now everywhere, from security surveillance and media production to social platforms and enterprise communications. However, extracting meaningful insights from large volumes of video remains a major challenge. Organizations need solutions that can understand not only what appears in a video, but also the context, narrative, and underlying meaning of the content.

In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each approach is designed for different use cases and cost-performance trade-offs. The complete solution is available as an open source AWS sample on GitHub.

The evolution of video analysis

Traditional video analysis approaches rely on manual review or basic computer vision techniques that detect predefined patterns. While functional, these methods face significant limitations:

• Scale constraints: Manual review is time-consuming and expensive
• Limited flexibility: Rule-based systems can’t adapt to new scenarios
• Context blindness ...