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What is Marengo?

Marengo is a cutting-edge multimodal model specifically engineered to transform various forms of media—such as video, audio, images, and text—into unified embeddings, thereby enabling flexible "any-to-any" functionalities for searching, retrieving, classifying, and analyzing vast collections of video and multimedia content. By integrating visual frames that encompass both spatial and temporal dimensions with audio elements like speech, background noise, and music, as well as textual components including subtitles and metadata, Marengo develops an all-encompassing, multidimensional representation of each media piece. Its advanced embedding architecture empowers Marengo to tackle a wide array of complex tasks, including different types of searches (like text-to-video and video-to-audio), semantic content exploration, anomaly detection, hybrid searching, clustering, and similarity-based recommendations. Recent updates have further refined the model by introducing multi-vector embeddings that effectively separate appearance, motion, and audio/text features, resulting in significant advancements in accuracy and contextual comprehension, especially for complex or prolonged content. This ongoing development not only enhances the overall user experience but also expands the model’s applicability across various multimedia sectors, paving the way for more innovative uses in the future. As a result, the versatility and effectiveness of Marengo position it as a valuable asset in the rapidly evolving landscape of multimedia technology.

What is EmbeddingGemma?

EmbeddingGemma is a flexible multilingual text embedding model boasting 308 million parameters, engineered to be both lightweight and highly effective, which enables it to function effortlessly on everyday devices such as smartphones, laptops, and tablets. Built on the Gemma 3 architecture, this model supports over 100 languages and accommodates up to 2,000 input tokens, leveraging Matryoshka Representation Learning (MRL) to offer customizable embedding sizes of 768, 512, 256, or 128 dimensions, thereby achieving a balance between speed, storage, and accuracy. Its capabilities are enhanced by GPU and EdgeTPU acceleration, allowing it to produce embeddings in just milliseconds—taking less than 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training keeps memory usage under 200 MB without compromising on quality. These features make it exceptionally well-suited for real-time, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. The model's versatility extends to personal file searches, mobile chatbot functionalities, and specialized applications, with a strong emphasis on user privacy and operational efficiency. Therefore, EmbeddingGemma is not only effective but also adapts well to various contexts, solidifying its position as a premier choice for diverse text processing tasks in real time.

Media

Media

Integrations Supported

Gemma 3
Gemma 4
TwelveLabs

Integrations Supported

Gemma 3
Gemma 4
TwelveLabs

API Availability

Has API

API Availability

Has API

Pricing Information

$0.042 per minute
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TwelveLabs

Date Founded

2021

Company Location

United States

Company Website

www.twelvelabs.io/product/models-overview#marengo

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

ai.google.dev/gemma/docs/embeddinggemma

Categories and Features

Categories and Features

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