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What is Zilliz Cloud?

While working with structured data is relatively straightforward, a significant majority—over 80%—of data generated today is unstructured, necessitating a different methodology. Machine learning plays a crucial role by transforming unstructured data into high-dimensional numerical vectors, which facilitates the discovery of underlying patterns and relationships within that data. However, conventional databases are not designed to handle vectors or embeddings, falling short in addressing the scalability and performance demands posed by unstructured data. Zilliz Cloud is a cutting-edge, cloud-native vector database that efficiently stores, indexes, and searches through billions of embedding vectors, enabling sophisticated enterprise-level applications like similarity search, recommendation systems, and anomaly detection. Built upon the widely-used open-source vector database Milvus, Zilliz Cloud seamlessly integrates with vectorizers from notable providers such as OpenAI, Cohere, and HuggingFace, among others. This dedicated platform is specifically engineered to tackle the complexities of managing vast numbers of embeddings, simplifying the process of developing scalable applications that can meet the needs of modern data challenges. Moreover, Zilliz Cloud not only enhances performance but also empowers organizations to harness the full potential of their unstructured data like never before.

What is Undb?

Undb is thrilled to engage with everyone and invites you to dive into its features while contributing your thoughts and suggestions. As it is still in the early stages of open-source development, it is recommended to refrain from using Undb in production settings for the time being. Remember that whenever the demo site undergoes updates, all previous table data will be lost. This open-source spreadsheet solution features an intuitive drag-and-drop interface and customizable functionalities, allowing users to effectively build their business systems, handle various data sets, and improve their operational logic. Impressively, Undb can handle tens of thousands of data entries in just seconds, showcasing its efficiency. With a strong focus on privacy, users have the flexibility to run their personal instance of Undb, which guarantees that their data remains safe and local. By default, it operates with SQLite and local object storage, necessitating only a single file to keep your information secure. Moreover, there is an array of built-in field types and configuration options designed to improve usability. This adaptability not only enhances the user experience but also simplifies the management of data processes, making it an excellent tool for businesses looking to optimize their operations. As Undb continues to evolve, user feedback will play a crucial role in shaping its future development.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Azure Marketplace
ChatGPT
ChatGPT Plus
ChatGPT Pro
Cohere
Coral
Google Cloud Platform
HoneyHive
Hugging Face
IBM watsonx.data
Java
Milvus
OpenAI
PyTorch

Integrations Supported

Docker
Fly.io
Railway

API Availability

Has API

API Availability

Pricing Information

$0
Zilliz Cloud on AWS
Compute Unit (CU) - $0.259 / hour
Storage - $0.025 / GB per month

Zilliz Cloud on Google Cloud
Compute Unit (CU) - $0.215 / hour
Storage - $0.02 / GB per month
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Zilliz

Date Founded

2017

Company Location

United States

Company Website

zilliz.com

Company Facts

Organization Name

Undb

Company Website

www.undb.xyz/

Categories and Features

Anomaly Detection

Not specified

Context Engineering

Not specified

Database

Backup and Recovery
Data Migration
Data Search
Data Security
Queries

Vector Databases

Not specified

Categories and Features

Database

Not specified

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