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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 Manticore Search?

Manticore Search, introduced in 2017, is an open-source database that evolved from the Sphinx Search engine, integrating its most effective features while significantly improving its functionality and addressing numerous issues through a near-total codebase rewrite, all while adhering to our open-source values. This comprehensive overhaul has transformed Manticore Search into a contemporary, efficient, lightweight, and adaptable database that shines in its full-text search capabilities. We believe that simplifying technology should be a primary focus for vendors in today's market. Our goal is to enable all users, from developers to DevOps professionals, to utilize Manticore products without needing extensive database or search engine expertise, making advanced qualifications unnecessary. Recognizing the value of your time, we strive to minimize the annoyance of lengthy trials that often accompany understanding the impact of various settings on functionalities. Consequently, Manticore Search is engineered to operate effectively with its default settings in most scenarios, ensuring users have a seamless experience right from the outset. By prioritizing user-friendliness, we aim to empower our community to harness the full potential of Manticore Search with ease.

Media

Media

Integrations Supported

Azure Marketplace
ChatGPT
ChatGPT Plus
ChatGPT Pro
Cohere
Google Cloud Platform
HoneyHive
Hugging Face
IBM watsonx.data
Java
OpenAI

Integrations Supported

AWS Marketplace
Apache Druid
C++
ClickHouse
Google Cloud BigQuery
JSON
MySQL
PostgreSQL
SQL Server

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

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

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
On-Site Training

Company Facts

Organization Name

Zilliz

Date Founded

2017

Company Location

United States

Company Website

zilliz.com

Company Facts

Organization Name

Manticore Search

Date Founded

2017

Company Location

United Kingdom

Company Website

manticoresearch.com/about/

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

Popular Alternatives

Popular Alternatives

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