58+ Graph Databases Ranked & Compared

Compare graph databases ranked by GitHub stars, query performance, and relationship modeling.

Last updated: May 20, 2026
52 databases
1SurrealDB
SurrealDB
32.1k+246 30d

Multi-model database combining documents, graphs, vectors, and time-series with built-in API layer and real-time capabilities

Multi-Model·2022·BSL-1.1·Rust
2Dgraph
Dgraph
21.7k+12 30d

Distributed graph database with native GraphQL support built for horizontal scale

Graph·2017·Apache-2.0·Go
3Neo4j
Neo4j
16.5k+191 30d

Native graph database with Cypher query language for connected data at scale

Graph·2007·GPL-3.0·Java, Scala
4ArangoDB
ArangoDB
14.2k+18 30d

Multi-model database unifying document, graph, and key-value in a single engine with AQL

Multi-Model·2012·BUSL-1.1·C++, JavaScript
5Gel
Gel
14.1k0 30d

Graph-relational database on Postgres with EdgeQL, built-in auth, and AI-ready embeddings

Relational·2019·Apache-2.0·Python, Rust
6NebulaGraph
NebulaGraph
12.2k+43 30d

Distributed graph database built for billion-scale graphs with millisecond latency

Graph·2019·Apache-2.0·C++
7JanusGraph
JanusGraph
5.8k+17 30d

Scalable open-source distributed graph database optimized for storing and querying billions of vertices and edges

Graph·2017·Apache-2.0·Java
8OrientDB
OrientDB
5.0k+5 30d

Multi-model database combining graph, document, key-value, and object models with SQL support and ACID transactions

Multi-Model·2010·Apache-2.0·Java
9HelixDB
HelixDB
4.5k+471 30d

Open-source graph-vector database built from scratch in Rust with compiled queries

Graph·2024·AGPL-3.0·Rust
10Apache AGE
Apache AGE
4.5k+96 30d

PostgreSQL extension that adds graph database functionality with openCypher query support

Graph·2020·Apache-2.0·C
11FalkorDB
FalkorDB
4.4k+444 30d

Ultra-fast in-memory graph database using GraphBLAS, optimized for GraphRAG and knowledge graphs

Graph·2023·SSPL-1.0·C
12TypeDB
TypeDB
4.3k+31 30d

Polymorphic database with a conceptual data model, strong type system, and symbolic reasoning engine

Graph·2016·MPL-2.0·Rust
13Memgraph
Memgraph
4.0k+120 30d

In-memory graph database tuned for dynamic analytics environments with real-time performance

Graph·2018·BSL-1.1·C++
14CozoDB
CozoDB
4.0k+29 30d

Transactional relational-graph-vector database using Datalog for query — the hippocampus for AI

Multi-Model·2022·MPL-2.0·Rust
15RavenDB
RavenDB
3.9k+5 30d

ACID document database with integrated full-text search, time series, and distributed counters

Document·2010·AGPL-3.0 / Commercial·C#
16Kuzu
Kuzu
3.9k+56 30d

Embedded property graph database built for speed with Cypher, vector search, and full-text search

Graph·2023·MIT·C++
17TerminusDB
TerminusDB
3.3k+34 30d

Git-like version control for structured data with graph and document models

Graph·2019·Apache-2.0·Prolog, JavaScript, Rust
18Apache HugeGraph
Apache HugeGraph
3.1k+37 30d

High-performance graph database supporting hundreds of billions of vertices and edges

Graph·2017·Apache-2.0·Java
19IndraDB
IndraDB
2.4k+10 30d

Lightweight graph database written in Rust with pluggable datastores

Graph·2017·MPL-2.0·Rust
20Graph Engine
Graph Engine
2.3k+1 30d

Distributed in-memory graph processing engine with strongly-typed key-value store, formerly Trinity

Graph·2015·MIT·C#, C++
21TinkerGraph
TinkerGraph
2.1k+10 30d

Lightweight in-memory reference graph database from Apache TinkerPop with Gremlin support

Graph·2009·Apache-2.0·Java
22AgensGraph
AgensGraph
1.5k+2 30d

Multi-model graph database built on PostgreSQL with SQL and Cypher support

Graph·2016·Apache-2.0·C
23Apache Jena TDB
Apache Jena TDB
1.3k+24 30d

Native high-performance RDF triple store with full SPARQL 1.1 support for semantic web applications

Graph·2000·Apache-2.0·Java
24Virtuoso
Virtuoso
959+6 30d

High-performance multi-model database combining SQL, RDF, SPARQL, XML, and knowledge graph capabilities

Multi-Model·1998·GPLv2 (Open Source Edition)·C, C++
25ArcadeDB
ArcadeDB
888+74 30d

Multi-model database supporting graphs, documents, key-value, vectors, time-series, and search in one engine

Multi-Model·2021·Apache-2.0·Java
26gStore
gStore
839+3 30d

Open-source graph-based RDF triple store with native SPARQL query engine using subgraph matching

Graph·2011·BSD-3-Clause·C++
27Eclipse RDF4J
Eclipse RDF4J
404+2 30d

Scalable Java framework for processing, storing, and querying RDF and Linked Data

Graph·2004·BSD-3-Clause·Java
28Fluree
Fluree
385+9 30d

Immutable, ledger-backed semantic graph database with native RDF and JSON-LD support

Graph·2016·EPL-2.0·Clojure
29AllegroGraph

Neuro-symbolic AI platform combining RDF knowledge graphs, vector store, and SPARQL in a transactional graph database

Graph·2004·Commercial (free edition available)·Common Lisp, C
30Amazon Neptune

Fully managed graph database service supporting Gremlin, openCypher, and SPARQL

Graph·2018·proprietary
31AnzoGraph DB

Massively parallel graph OLAP database for W3C standards-based analytics at scale

Graph·2018·proprietary·C++
32Azure Cosmos DB

Globally distributed, multi-model database service with turnkey multi-region replication and single-digit millisecond latency

Multi-Model·2017·proprietary·C++, C#
33DataFS

Object-oriented multi-model database with runtime-extensible classes and graph linking

Multi-Model·2018·Commercial
34DataStax Enterprise

Enterprise-grade distributed database built on Apache Cassandra with integrated analytics, search, and graph

Wide-Column·2010·Commercial·Java
35Galaxybase

High-performance native distributed graph database for HTAP workloads at trillion-edge scale

Graph·2018·proprietary·Java
36Google Cloud Spanner

Globally distributed, strongly consistent relational database with unlimited scale and 99.999% availability

Relational·2017·Proprietary·C++
37GraphBase

Enterprise graph database for knowledge graphs and Graph RAG with distributed cloud-native architecture

Graph·proprietary
38GraphDB

Enterprise RDF graph database with semantic inference, knowledge graph, and SPARQL query support

Graph·2000·GraphDB Commercial License (Free edition available)·Java
39InfiniteGraph

Distributed graph database for large-scale relationship analytics and deep link analysis

Graph·2010·Commercial·Java, C++
40Kinetica

GPU-accelerated real-time analytics database for spatial, temporal, graph, and AI workloads at scale

Analytics·2016·proprietary·C++
41MarkLogic

Enterprise multi-model database combining documents, graph, and search with government-grade security

Multi-Model·2001·proprietary·C++
42Ontotext GraphDB

Enterprise RDF triplestore with real-time semantic inferencing at billion-statement scale

Graph·2000·proprietary·Java
43Oracle

Enterprise-grade multi-model database with AI-native capabilities

Relational·1979·Oracle Commercial License·C, C++
44RDFox

High-performance in-memory RDF knowledge graph and semantic reasoning engine

Graph·2014·proprietary·C++
45SAP HANA

In-memory relational database for real-time analytics and transactional processing in enterprise environments

Relational·2010·proprietary·C++
46Sparksee

High-performance graph database with bitmap indexing, formerly known as DEX

Graph·2008·Commercial·C++
47SQL Server

Enterprise relational database with built-in AI and analytics

Relational·1989·Microsoft Commercial License·C, C++
48Stardog

Enterprise knowledge graph platform built on RDF standards for data unification and AI-powered insights

Graph·2012·proprietary·Java
49TigerGraph

High-performance native graph analytics platform for AI, fraud detection, and real-time insights on connected data

Graph·2017·proprietary·C++
50Transwarp Hippo

Enterprise cloud-native distributed vector database with GPU acceleration and multi-model support

Vector·proprietary
51Transwarp StellarDB

Enterprise distributed graph database with native graph storage and deep link analysis at PB scale

Graph·proprietary
52Ultipa

Ultra-high-performance 4th-generation graph database with deep traversal and GQL compliance

Graph·2019·proprietary·C++

What is a Graph Database?

A graph database stores data as nodes (entities) and edges (relationships), making it natural to model and query interconnected data. Unlike relational databases where relationships are expressed through foreign keys and joins, graph databases treat relationships as first-class citizens — traversing connections is the core operation, not an afterthought. This makes graph databases dramatically faster for queries like 'find all friends of friends who also like this product.' Neo4j is the most widely adopted graph database, using the Cypher query language. Other options include ArangoDB, Amazon Neptune, Dgraph, and JanusGraph.

When to Use a Graph Database

Graph databases shine when relationships between entities are as important as the entities themselves. Key use cases include: social networks (friend recommendations, connection paths), fraud detection (identifying suspicious transaction patterns), knowledge graphs (connecting concepts across domains), recommendation engines (collaborative filtering), identity and access management (role hierarchies), network infrastructure mapping, and supply chain analysis. Consider relational databases if your queries are primarily CRUD operations on individual records with simple relationships, or document databases if your data is naturally hierarchical rather than graph-shaped.

Frequently Asked Questions

What is the difference between a graph database and a relational database?
Relational databases store data in tables and express relationships through foreign keys — querying across relationships requires JOIN operations that become increasingly expensive as data grows. Graph databases store relationships directly in the data structure as edges between nodes — traversing relationships is a constant-time operation regardless of dataset size. For queries like 'find all paths between two entities within 5 hops,' a graph database can be orders of magnitude faster than a relational database.
Is Neo4j the only graph database?
No. Neo4j is the most popular, but there are many alternatives. ArangoDB is a multi-model database with strong graph support. Amazon Neptune is a fully managed graph service on AWS. Dgraph is a distributed graph database built in Go. JanusGraph is an open-source, horizontally scalable graph database. Memgraph offers an in-memory graph database compatible with Neo4j's Cypher. Some relational databases like PostgreSQL also support graph queries through extensions like Apache AGE.
When should I use a graph database vs a relational database?
Use a graph database when your core queries involve traversing relationships — social connections, recommendation paths, fraud patterns, dependency trees. If you frequently ask 'what is connected to X, and what is connected to those things,' a graph database is the right fit. Stick with a relational database when your queries are primarily about filtering, aggregating, and joining structured records — customer orders, financial transactions, inventory management. Many applications benefit from both: a relational database for transactional data and a graph database for relationship-heavy features.
What query language do graph databases use?
Neo4j uses Cypher, a declarative pattern-matching language designed specifically for graphs. Amazon Neptune supports both Apache TinkerPop (Gremlin) and W3C SPARQL for RDF data. ArangoDB uses AQL (ArangoDB Query Language) which supports graph traversals alongside document and key-value queries. Dgraph uses DQL (based on GraphQL). GQL (Graph Query Language) is an emerging ISO standard aiming to unify graph querying. Most graph databases also offer client libraries for programmatic access.
Can graph databases handle large-scale data?
Yes, with caveats. Neo4j supports clusters with billions of nodes and relationships. Dgraph and JanusGraph are built for horizontal scaling across multiple machines. Amazon Neptune handles terabytes of graph data as a managed service. However, graph databases generally don't scale writes as easily as wide-column stores (Cassandra) or document databases (MongoDB). For very large graphs, distributed graph databases like Dgraph or JanusGraph are better suited than single-node deployments of Neo4j.

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