DBMS > Microsoft Azure Cosmos DB vs. VictoriaMetrics vs. Weaviate vs. XTDB

System Properties Comparison Microsoft Azure Cosmos DB vs. VictoriaMetrics vs. Weaviate vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Vector DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

azure.microsoft.com/services/cosmos-db

victoriametrics.com

github.com/weaviate/weaviate
weaviate.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

weaviate.io/developers/weaviate

www.xtdb.com/docs

Developer

Microsoft

VictoriaMetrics

Weaviate B.V.

Juxt Ltd.

Initial release

2014

2018

2019

Current release

v1.91, May 2023

1.19, May 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial license available with Weaviate Enterprise

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Clojure

Server operating systems

hosted

FreeBSD
Linux
macOS
OpenBSD

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes, maps to GraphQL interface

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

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

yes, extensible-data-notation format

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

All properties auto-indexed by default

yes

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

yes

SQL

Support of SQL

SQL-like query language

GraphQL is used as query language

limited SQL, making use of Apache Calcite

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

GraphQL query language
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript / TypeScript
Python

Clojure
Java

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Synchronous replication

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Access rights can be defined down to the item level

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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