AWS adds Marengo Embed 3.0 to Bedrock Knowledge Bases

AWS has made TwelveLabs’ Marengo Embed 3.0 generally available as an embedding option in Amazon Bedrock Knowledge Bases, expanding the managed service with semantic search across video, audio, images and text.

The integration is available in two AWS regions: US East (N. Virginia), or us-east-1, and US West (N. California), or us-west-1. AWS says the model maps supported media and text into the same 512-dimensional vector space. That allows a text, image or combined text-and-image query to retrieve related moments or assets across modalities. The release follows AWS making GPT-6 Astra generally available on Amazon Bedrock, another recent addition to the service.

In the workflow AWS describes, a customer connects an Amazon S3 data source and starts a sync. Bedrock Knowledge Bases then segments media, samples video frames, transcribes audio, creates an embedding for each segment and writes the vectors to an index. AWS says the process removes the need for customers to operate separate transcription, frame-extraction, embedding, vector-database and synchronization components. That operational benefit is an AWS claim, not an independently measured result.

According to AWS, the workflow supports MP4 and MOV video files, JPEG and PNG images, and audio tracks. Test queries can return ranked results with metadata that includes chunk start and end times, the source URI and embedding type. Applications can then use the Bedrock Retrieve API for downstream retrieval.

AWS documentation separates query and asset processing at the model API level. Synchronous InvokeModel handles search-query embeddings, while StartAsyncInvoke handles large-scale asset embeddings and is required for video and audio inputs. AWS says Knowledge Bases customers pay for storage and retrieval, while Marengo embedding generation is billed at the standard Bedrock model-invocation rate. The announcement does not provide numeric prices.

Marengo Embed 3.0 uses 512-dimensional embeddings, compared with 1,024 dimensions in Marengo 2.7, according to AWS documentation. TwelveLabs also describes the model as jointly representing video, audio, text, images and combinations of those inputs in a 512-dimensional space.