Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Vertex AI Reviews & Ratings
    732 Ratings
    Company Website
  • Google AI Studio Reviews & Ratings
    9 Ratings
    Company Website
  • Riddle Quiz Maker Reviews & Ratings
    98 Ratings
    Company Website
  • CLEAR Reviews & Ratings
    1 Rating
    Company Website
  • AthenaHQ Reviews & Ratings
    13 Ratings
    Company Website
  • Google Cloud BigQuery Reviews & Ratings
    1,871 Ratings
    Company Website
  • RaimaDB Reviews & Ratings
    5 Ratings
    Company Website
  • Gemini Credit Card Reviews & Ratings
    2 Ratings
    Company Website
  • DashboardFox Reviews & Ratings
    5 Ratings
    Company Website
  • Global App Testing Reviews & Ratings
    59 Ratings
    Company Website

What is Gemini Embedding?

The first text model of the Gemini Embedding, referred to as gemini-embedding-001, has officially launched and is accessible through both the Gemini API and Vertex AI, having consistently held its top spot on the Massive Text Embedding Benchmark Multilingual leaderboard since its initial trial in March, thanks to its exceptional performance in retrieval, classification, and multiple embedding tasks, outperforming both legacy Google models and those from other external developers. Notably, this versatile model supports over 100 languages and features a maximum input limit of 2,048 tokens, employing the cutting-edge Matryoshka Representation Learning (MRL) technique, which enables developers to choose from output dimensions of 3072, 1536, or 768 for optimal quality, efficiency, and performance. Users can easily access this model through the well-known embed_content endpoint in the Gemini API, and while older experimental versions are scheduled to be retired by 2025, there is no need for developers to re-embed previously stored assets when switching to the new model. This transition process is designed for a smooth user experience, minimizing any impact on existing workflows and ensuring continuity in operations. The launch of this model represents a significant step forward in the field of text embeddings, paving the way for even more advancements in multilingual applications.

What is Arctic Embed 2.0?

Snowflake's Arctic Embed 2.0 introduces advanced multilingual capabilities to its text embedding models, facilitating efficient data retrieval on a global scale while ensuring robust performance in English and extensibility. This iteration builds upon the well-established foundation of previous versions, providing support for a variety of languages and allowing developers to create stream-processing pipelines that leverage neural networks for complex tasks such as tracking, video encoding/decoding, and rendering, which enhances real-time data analytics across diverse formats. The model utilizes Matryoshka Representation Learning (MRL) to enhance embedding storage efficiency, achieving significant compression with minimal quality degradation. Consequently, organizations can adeptly handle demanding workloads such as training large models, fine-tuning, real-time inference, and executing high-performance computing tasks across various languages and regions. Moreover, this technological advancement presents new avenues for businesses eager to exploit the potential of multilingual data analytics within the fast-paced digital landscape, thereby fostering competitive advantages in numerous sectors. With its comprehensive features, Arctic Embed 2.0 is poised to redefine how organizations approach and utilize data in an increasingly interconnected world.

Media

Media

Integrations Supported

Gemini
Google AI Studio
OpenAI
Python
Snowflake
Vertex AI

Integrations Supported

Gemini
Google AI Studio
OpenAI
Python
Snowflake
Vertex AI

API Availability

Has API

API Availability

Has API

Pricing Information

$0.15 per 1M input tokens
Free Trial Offered?
Free Version

Pricing Information

$2 per credit
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

Google

Date Founded

1998

Company Location

United States

Company Website

developers.googleblog.com/en/gemini-embedding-available-gemini-api/

Company Facts

Organization Name

Snowflake

Date Founded

2012

Company Location

United States

Company Website

www.snowflake.com/en/engineering-blog/snowflake-arctic-embed-2-multilingual/

Categories and Features

Categories and Features

Popular Alternatives

Popular Alternatives

voyage-code-3 Reviews & Ratings

voyage-code-3

Voyage AI