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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 ZeusDB?

ZeusDB is an advanced data platform designed to address the intricate demands of modern analytics, machine learning, real-time data insights, and hybrid data management solutions. This state-of-the-art system effectively merges vector, structured, and time-series data within one cohesive engine, enabling functionalities such as recommendation engines, semantic search capabilities, retrieval-augmented generation, live dashboards, and the deployment of machine learning models from a single source. Featuring ultra-low latency querying and real-time analytics, ZeusDB eliminates the need for multiple databases or caching solutions, streamlining operations. Moreover, it offers developers and data engineers the opportunity to extend its capabilities using Rust or Python, with flexible deployment options in on-premises, hybrid, or cloud setups while maintaining compliance with GitOps/CI-CD practices and integrating built-in observability. Its powerful characteristics, including native vector indexing methods like HNSW, metadata filtering, and sophisticated query semantics, enhance similarity searching, hybrid retrieval strategies, and rapid application development cycles. As a result, ZeusDB is set to transform how organizations manage data and conduct analytics, making it an essential asset in today’s data-driven environment. By harnessing its innovative features, businesses can achieve greater efficiency and effectiveness in their data operations.

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

LangChain
Python
Rust

API Availability

Has API

API Availability

Has API

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
On-Prem

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

ZeusDB

Date Founded

2012

Company Location

United States

Company Website

www.zeusdb.com

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

Relational Database

Not specified

Vector Databases

Not specified

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