DBMS > Galaxybase vs. Milvus vs. MonetDB vs. Qdrant

System Properties Comparison Galaxybase vs. Milvus vs. MonetDB vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

Scalable, ACID-compliant native distributed parallel graph platform

A DBMS designed for efficient storage of vector data and vector similarity searches

A relational database management system that stores data in columns

A high-performance vector database with neural network or semantic-based matching

Primary database model

Secondary database models

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Website

galaxybase.com

milvus.io

www.monetdb.org

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

milvus.io/docs/overview.md

www.monetdb.org/Documentation

qdrant.tech/documentation

Developer

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

MonetDB BV

Qdrant

Initial release

2017

2019

2004

2021

Current release

Nov 20, November 2021

2.4.4, May 2024

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C and Java

C++, Go

Rust

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

Data scheme

Strong typed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

Numbers, Strings, Geo, Boolean

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

Go
Java
Python

C++
Go
Java
JavaScript (Node.js)
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined procedures and functions

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role-based access control

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

Key-based authentication

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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