EmbeddingGemma 2Google
|
Voyage AIMongoDB
|
|||||
Related Products
|
||||||
About
EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware.
|
About
Voyage AI provides best-in-class embedding models and rerankers designed to supercharge search and retrieval for unstructured data. Its technology powers high-quality Retrieval-Augmented Generation (RAG) by improving how relevant context is retrieved before responses are generated. Voyage AI offers general-purpose, domain-specific, and company-specific models to support a wide range of use cases. The models are optimized for accuracy, low latency, and reduced costs through shorter vector dimensions. With long-context support of up to 32K tokens, Voyage AI enables deeper understanding of complex documents. The platform is modular and integrates easily with any vector database or large language model. Voyage AI is trusted by industry leaders to deliver reliable, factual AI outputs at scale.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
Developers and AI teams wanting to build private, efficient, on-device multimodal search, retrieval, RAG, and semantic indexing systems
|
Audience
Voyage AI is ideal for AI engineers, machine learning teams, and enterprises building search, retrieval, and RAG systems that require high accuracy, scalability, and cost-efficient performance
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Supported
|
API
Offers API
Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Supported
|
|||||
Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/
|
Company InformationMongoDB
Founded: 2007
United States
www.voyageai.com
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
|
|
|
|||||
Categories |
Categories |
|||||
Integrations
LiteLLM
Not Supported
Pinecone Rerank v0
Not Supported
Snowflake
Not Supported
Snowflake Cortex AI
Not Supported
Weaviate
Not Supported
voyage-3-large
Not Supported
voyage-4-large
Not Supported
|
Integrations
LiteLLM
Supported
Pinecone Rerank v0
Supported
Snowflake
Supported
Snowflake Cortex AI
Supported
Weaviate
Supported
voyage-3-large
Supported
voyage-4-large
Supported
|
|||||
|
|
|