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ease
features
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support

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Description

EmbeddingGemma is a versatile multilingual text embedding model with 308 million parameters, designed to be lightweight yet effective, allowing it to operate seamlessly on common devices like smartphones, laptops, and tablets. This model, based on the Gemma 3 architecture, is capable of supporting more than 100 languages and can handle up to 2,000 input tokens, utilizing Matryoshka Representation Learning (MRL) for customizable embedding sizes of 768, 512, 256, or 128 dimensions, which balances speed, storage, and accuracy. With its GPU and EdgeTPU-accelerated capabilities, it can generate embeddings in a matter of milliseconds—taking under 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training ensures that memory usage remains below 200 MB without sacrificing quality. Such characteristics make it especially suitable for immediate, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. Whether used for personal file searches, mobile chatbot functionality, or specialized applications, its design prioritizes user privacy and efficiency. Consequently, EmbeddingGemma stands out as an optimal solution for a variety of real-time text processing needs.

Description

The Voyage 4 model family from Voyage AI represents an advanced era of text embedding models, crafted to yield superior semantic vectors through an innovative shared embedding space that allows various models in the lineup to create compatible embeddings, thereby enabling developers to seamlessly combine models for both document and query embedding, ultimately enhancing accuracy while managing latency and cost considerations. This family features voyage-4-large, the flagship model that employs a mixture-of-experts architecture, achieving cutting-edge retrieval accuracy with approximately 40% reduced serving costs compared to similar dense models; voyage-4, which strikes a balance between quality and efficiency; voyage-4-lite, which delivers high-quality embeddings with fewer parameters and reduced compute expenses; and the open-weight voyage-4-nano, which is particularly suited for local development and prototyping, available under an Apache 2.0 license. The interoperability of these four models, all functioning within the same shared embedding space, facilitates the use of interchangeable embeddings, paving the way for innovative asymmetric retrieval strategies that can significantly enhance performance across various applications. By leveraging this cohesive design, developers gain access to a versatile toolkit that can be tailored to meet diverse project needs, making the Voyage 4 family a compelling choice in the evolving landscape of AI-driven solutions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cohere Embed
Gemini
Gemma 3
Gemma 4
Hugging Face
MongoDB Atlas
OpenAI
Voyage AI

Integrations

Cohere Embed
Gemini
Gemma 3
Gemma 4
Hugging Face
MongoDB Atlas
OpenAI
Voyage AI

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

ai.google.dev/gemma/docs/embeddinggemma

Vendor Details

Company Name

Voyage AI

Founded

2023

Country

United States

Website

blog.voyageai.com/2026/01/15/voyage-4/

Product Features

Product Features

Alternatives

Alternatives

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