DBMS > Amazon Neptune vs. Blueflood vs. Milvus

System Properties Comparison Amazon Neptune vs. Blueflood vs. Milvus

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable TimeSeries DBMS based on Cassandra

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

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Vector DBMS

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

Score	2.13
Rank	#111	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.06
Rank	#353	Overall
	#36	Time Series DBMS

Score	2.92
Rank	#87	Overall
	#7	Vector DBMS

Website

aws.amazon.com/neptune

blueflood.io

milvus.io

Technical documentation

aws.amazon.com/neptune/developer-resources

github.com/rax-maas/blueflood/wiki

milvus.io/docs/overview.md

Developer

Amazon

Rackspace

Initial release

2017

2013

2019

Current release

2.4.4, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Java

C++, Go

Server operating systems

hosted

Linux
OS X

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP REST

RESTful HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Role based access control and fine grained access rights

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Zupee implements Amazon Neptune to detect Wallet transaction anomalies in real time
28 April 2025, Amazon Web Services (AWS)

Use Amazon Neptune Analytics to analyze relationships in your data faster, Part 1: Introducing Parquet and CSV import and export
21 January 2025, Amazon Web Services (AWS)

Using generative AI and Amazon Bedrock to generate SPARQL queries to discover protein functional information with UniProtKB and Amazon Neptune | Amazon Web Services
9 April 2025, Amazon Web Services (AWS)

Introducing the GraphRAG Toolkit
27 January 2025, Amazon Web Services (AWS)

How Coinbase provides trustworthy financial experiences through real-time user clustering with Amazon Neptune
18 November 2024, Amazon Web Services (AWS)

provided by Google News

Real-Time Performance and Health Monitoring Using Netdata
2 September 2019, CNX Software

provided by Google News

What Is Milvus? A Distributed Vector Database
3 March 2025, Oracle

Milvus 2.5 Creates the Best of Both Worlds With Hybrid Vector-Keyword Search
17 December 2024, GlobeNewswire

AI-Powered Search Engine With Milvus Vector Database on Vultr
7 November 2024, SitePoint

Hands-On Vector Similarity Search with Milvus
20 October 2024, Packt

Dance between dense and sparse embeddings: Enabling Hybrid Search in LangChain-Milvus
19 November 2024, Towards Data Science

provided by Google News

Share this page