Using Amazon S3 Vectors (preview) to semantically search using a media lake on AWS
Media Blog
The article discusses AWS's new S3 Vectors feature and how media organizations can leverage semantic search for content discovery using a media lake architecture.
- S3 Vectors enables cost-effective vector storage and querying of media assets at massive scale
- Uses TwelveLabs Marengo multimodal embedding model on Amazon Bedrock for generating vector embeddings
- Provides a serverless architecture for semantic search across images, video, and audio content
- Enables natural language queries to find relevant media assets with sub-second performance
- Reduces content search time from hours to minutes while maintaining scalability
The solution allows media organizations to efficiently discover and utilize their digital libraries through AI-powered semantic search capabilities.
The AWS News Feed is currently looking for gold sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.
Related articles
Jul 21
2025
2025
Optimizing vector search using Amazon S3 Vectors and Amazon OpenSearch Service
Jul 10
2025
2025
Introducing: Guidance for a media lake on AWS
Jul 15
2025
2025
Announcing Amazon S3 Vectors (Preview)—First cloud object storage with native support for storing and querying vectors
Jul 15
2025
2025
Introducing Amazon S3 Vectors: First cloud storage with native vector support at scale (preview)
The AWS News Feed is currently looking for silver sponsors. If you want to support the AWS community and reach a large audience of AWS professionals, consider sponsoring the AWS News Feed.