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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 Model Context Protocol (MCP)?

The Model Context Protocol (MCP) serves as a versatile and open-source framework designed to enhance the interaction between artificial intelligence models and various external data sources. By facilitating the creation of intricate workflows, it allows developers to connect large language models (LLMs) with databases, files, and web services, thereby providing a standardized methodology for AI application development. With its client-server architecture, MCP guarantees smooth integration, and its continually expanding array of integrations simplifies the process of linking to different LLM providers. This protocol is particularly advantageous for developers aiming to construct scalable AI agents while prioritizing robust data security measures. Additionally, MCP's flexibility caters to a wide range of use cases across different industries, making it a valuable tool in the evolving landscape of AI technologies.

Media

Media

Integrations Supported

ChatGPT
OpenAI
Activepieces
Agent2Agent
ChatGPT Pro
CrawlChat
Crush
DeerFlow
GPT-4o
Gemini CLI
GoMarble
GrimoAI
HoneyHive
Mastra
Milvus
Pipedream
Rube
Supernovas AI LLM
TestSprite
xpander.ai

Integrations Supported

ChatGPT
OpenAI
Activepieces
Agent2Agent
ChatGPT Pro
CrawlChat
Crush
DeerFlow
GPT-4o
Gemini CLI
GoMarble
GrimoAI
HoneyHive
Mastra
Milvus
Pipedream
Rube
Supernovas AI LLM
TestSprite
xpander.ai

API Availability

Has API

API Availability

Has API

Pricing Information

$0
Free Trial Offered?
Free Version

Pricing Information

Free
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

Zilliz

Date Founded

2017

Company Location

United States

Company Website

zilliz.com

Company Facts

Organization Name

Anthropic

Date Founded

2021

Company Location

United States

Company Website

modelcontextprotocol.io

Categories and Features

Database

Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization

Categories and Features

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