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features
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Description

Start creating at no cost in just a few minutes. Vectorize provides a swift and economical solution for vector storage, enhancing your search capabilities and supporting AI Retrieval Augmented Generation (RAG) applications. By utilizing Vectorize, you can eliminate tool sprawl and decrease your total cost of ownership, as it effortlessly connects with Cloudflare’s AI developer platform and AI gateway, allowing for centralized oversight, monitoring, and management of AI applications worldwide. This globally distributed vector database empowers you to develop comprehensive, AI-driven applications using Cloudflare Workers AI. Vectorize simplifies and accelerates the querying of embeddings—representations of values or objects such as text, images, and audio that machine learning models and semantic search algorithms can utilize—making it both quicker and more affordable. It enables various functionalities, including search, similarity detection, recommendations, classification, and anomaly detection tailored to your data. Experience enhanced results and quicker searches, with support for string, number, and boolean data types, optimizing your AI application's performance. In addition, Vectorize’s user-friendly interface ensures that even those new to AI can harness the power of advanced data management effortlessly.

Description

EmbeddingGemma 2 is a versatile and lightweight multimodal embedding model that facilitates the mapping of text, code, images, video, and audio into a unified embedding space, which is useful for applications such as search, retrieval, classification, routing, and RAG. It is constructed on the Gemma 4 framework and distributed under the Apache 2.0 license, featuring an impressive 740 million parameters while being fine-tuned for efficient on-device inference. Its flexible architecture allows for the use of only 270 million parameters for text-centric tasks, and it includes additional vision and audio encoders for comprehensive multimodal capabilities. Furthermore, the innovative Matryoshka Representation Learning technique enables developers to compress output vectors from 768 dimensions down to 512, 256, or even 128 dimensions, effectively reducing the storage and memory demands for local vector databases. The model is equipped with an 8K-token context window, providing the capability to handle up to 5.5 minutes of audio, 29 images, 58 video frames, or various combinations of these inputs seamlessly on local hardware. This adaptability makes it particularly valuable for developers seeking to enhance their applications with rich multimedia integration.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Cloudflare Workers Yes 

Integrations

Cloudflare Workers No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Cloudflare

Founded

2009

Country

United States

Website

www.cloudflare.com/developer-platform/products/vectorize/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

Product Features

Product Features

Alternatives

Alternatives

No Alternatives