DBMS > HEAVY.AI vs. Milvus vs. Oracle vs. Weaviate

System Properties Comparison HEAVY.AI vs. Milvus vs. Oracle vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Widely used RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

milvus.io

www.oracle.com/database

github.com/weaviate/weaviate
weaviate.io

Technical documentation

docs.heavy.ai

milvus.io/docs/overview.md

docs.oracle.com/en/database

weaviate.io/developers/weaviate

Developer

HEAVY.AI, Inc.

Oracle

Weaviate B.V.

Initial release

2016

2019

1980

2019

Current release

5.10, January 2022

2.3.4, January 2024

23c, September 2023

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

commercial license available with Weaviate Enterprise

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 CUDA

C++, Go

C and C++

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

yes

Schemaless in JSON and XML columns

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

string, int, float, geo point, date, cross reference, fuzzy references

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.

yes

Secondary indexes

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

with proprietary extensions

GraphQL is used as query language

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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As a database supporting the development of generative AI and semantic search applications...
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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All companies that have data.
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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