List of the Top 25 Free Graph Databases in 2025

Apache Cassandra serves as an exemplary database solution for scenarios demanding exceptional scalability and availability, all while ensuring peak performance. Its capacity for linear scalability, combined with robust fault-tolerance features, makes it a prime candidate for effective data management, whether implemented on traditional hardware or in cloud settings. Furthermore, Cassandra stands out for its capability to replicate data across multiple datacenters, which minimizes latency for users and provides an added layer of security against regional outages. This distinctive blend of functionalities not only enhances operational resilience but also fosters efficiency, making Cassandra an attractive choice for enterprises aiming to optimize their data handling processes. Such attributes underscore its significance in an increasingly data-driven world.

Redis Labs serves as the official home of Redis, showcasing its leading product, Redis Enterprise, which is recognized as the most advanced version of Redis. Offering much more than mere caching capabilities, Redis Enterprise is accessible for free in the cloud, delivering NoSQL solutions and utilizing the fastest in-memory database available. The platform is designed for scalability and enterprise-level resilience, enabling massive scaling along with user-friendly administration and operational efficiency. Notably, Redis in the Cloud has gained popularity among DevOps professionals due to its capabilities. Developers benefit from advanced data structures and a broad range of modules, empowering them to foster innovation and achieve quicker time-to-market. Chief Information Officers appreciate the robust security and reliable expert support that Redis provides, ensuring an impressive uptime of 99.999%. For scenarios involving active-active configurations, geodistribution, and conflict resolution with read/write operations across multiple regions on the same dataset, relational databases are recommended. Furthermore, Redis Enterprise facilitates various flexible deployment options, making it adaptable to different environments. The ecosystem also includes Redis JSON, Redis Java, and Python Redis, along with best practices for Redis on Kubernetes and GUI management, solidifying its versatility in modern application development.

With immediate access to a highly adaptable semantic layer, explainable AI, and reusable data modeling, data engineers and scientists can enhance their performance by as much as 95%. This capability allows them to develop and refine semantic models, grasp the connections within data, and execute federated queries, thereby accelerating the journey to actionable insights. Stardog stands out with its graph data virtualization and top-tier graph database, which are offered at a cost that can be as much as 57 times lower than those of its rivals. This solution facilitates seamless integration of any data source, data warehouse, or enterprise data lakehouse without the need for data duplication or relocation. Moreover, it enables the scaling of user engagement and use cases while significantly reducing infrastructure expenses. In addition, Stardog’s intelligent inference engine dynamically leverages expert knowledge during query execution to reveal hidden patterns and unexpected relationships, ultimately leading to enhanced data-driven business decisions and outcomes. By harnessing such advanced technologies, organizations can stay ahead of the competitive curve in a rapidly evolving data landscape.

Fauna serves as a data API designed to empower rich client applications utilizing serverless backends. It features a web-native interface that is compatible with GraphQL, allows for the implementation of custom business logic, and facilitates seamless integration within the serverless ecosystem, all while providing a reliable multi-cloud architecture that you can depend on and expand as needed. This versatility makes Fauna an attractive choice for developers looking to build scalable applications.

Graphlytic is an online business intelligence platform that facilitates the visualization and analysis of knowledge graphs. Users can interactively navigate the graph, searching for patterns with the help of the Cypher query language or utilizing query templates designed for those without a technical background. Additionally, filters can be applied to effectively address any inquiries related to the graph. This visualization tool offers profound insights across various fields, including scientific research and anti-fraud efforts. Even individuals unfamiliar with graph theory can readily delve into the data. The graph rendering is powered by Cytoscape.js, which can handle tens of thousands of nodes and an extensive number of relationships. Graphlytic is accessible in three different formats: Desktop, Cloud, or Server, catering to diverse user needs. The Graphlytic Desktop version is a Neo4j Desktop application that can be installed effortlessly in just a few clicks. For small teams seeking a swift setup without the hassle of installation, Cloud instances provide an ideal solution, ensuring they can start working quickly and efficiently. This flexibility greatly enhances the platform's usability across different organizational contexts.

InfiniteGraph is a highly scalable graph database engineered to handle rapid ingestion of extensive data volumes, processing billions of nodes and edges each hour while facilitating intricate queries. It is adept at efficiently distributing interconnected graph data across a worldwide organization. As a schema-driven graph database, InfiniteGraph accommodates very sophisticated data models and boasts an exceptional schema evolution feature that permits alterations and enhancements to an existing database structure. With its Placement Management Capability, InfiniteGraph optimizes the arrangement of data items, resulting in significant enhancements in both query execution and data ingestion speeds. Moreover, the database incorporates client-side caching that stores frequently accessed nodes and edges, allowing InfiniteGraph to operate similarly to an in-memory graph database, thus improving performance further. Additionally, InfiniteGraph’s specialized DO query language empowers users to execute complex queries that extend beyond typical graph capabilities, a feature that sets it apart from other graph databases in the market. This flexibility makes it a powerful tool for organizations that need to analyze and manage large-scale interconnected data efficiently.

data.world is a cloud-based platform meticulously crafted for modern data ecosystems, facilitating effortless management of updates, migrations, and ongoing maintenance. The straightforward setup process is enhanced by a growing array of pre-built integrations compatible with all leading cloud data warehouses. When quick results are paramount, teams should focus on tackling real business issues instead of wrestling with complicated data management tools. data.world streamlines the experience for all users, not just data specialists, equipping them to obtain clear, accurate, and timely responses to a wide range of business questions. Our platform boasts a cloud-native data catalog that links disparate and distributed data to familiar business concepts, creating an accessible, cohesive knowledge base for everyone. Additionally, in addition to our enterprise offerings, data.world nurtures the largest collaborative open data community worldwide, where participants work together on various projects, including social bot detection and prestigious data journalism endeavors, fostering innovation and collective learning. This vibrant environment not only promotes knowledge sharing but also empowers users to harness data in inventive and meaningful ways, ultimately driving impactful solutions across different sectors.

Apache TinkerPop™ is a dynamic graph computing framework that caters to both online transaction processing (OLTP) in graph databases and online analytical processing (OLAP) within graph analytic systems. At the heart of this framework lies Gremlin, a robust graph traversal language that empowers users to craft complex queries and traversals on their application's property graph with finesse. Each traversal in Gremlin comprises a sequence of steps that can be nested, offering significant flexibility in how data is explored and analyzed. Fundamentally, a graph is formed by interconnected vertices and edges, each capable of containing various key/value pairs referred to as properties. Vertices represent unique entities such as people, places, or events, while edges denote the relationships that link these vertices together. For instance, a vertex could signify an individual who knows another person, attended a specific event, or visited a certain place recently. This framework proves especially advantageous when tackling intricate domains filled with diverse objects (vertices) that can be linked through various types of relationships (edges). By grasping this structural design, users can maximize the potential of their data and extract meaningful insights from their interconnected networks. Ultimately, the ability to navigate and analyze such complex relationships enhances decision-making processes and drives innovation across various fields.

Easily manage complex models with ArcadeDB without compromising on performance. There's no need to rely on Polyglot Persistence; you can store various data types without using multiple databases. In one ArcadeDB Multi-Model database, you can efficiently integrate graphs, documents, key-value pairs, and time series data seamlessly. Since each model is built directly into the database engine, worries about translation delays are a thing of the past. Designed with cutting-edge technology, ArcadeDB can handle millions of records per second effortlessly. A remarkable feature of ArcadeDB is its consistent traversal speed, which remains stable regardless of the database's size, whether it contains a handful of entries or billions. Additionally, ArcadeDB can function as an embedded database on a single server but is also capable of scaling across multiple servers with Kubernetes. Its adaptable nature allows it to run on any platform while using minimal resources. The security of your data is critical; our robust, fully transactional engine ensures durability for essential production databases. To maintain consistency across different servers, ArcadeDB utilizes a Raft Consensus Algorithm, guaranteeing that your data stays reliable and synchronized even in distributed settings. With ArcadeDB, you can effectively manage all your data requirements without the complications of handling multiple systems, ensuring a streamlined and efficient experience. Whether for small projects or large-scale applications, ArcadeDB provides the flexibility and performance needed to meet diverse data challenges.

PuppyGraph enables users to seamlessly query one or more data sources through an integrated graph model. Unlike traditional graph databases, which can be expensive, require significant setup time, and demand a specialized team for upkeep, PuppyGraph streamlines the process. Many conventional systems can take hours to run multi-hop queries and struggle with managing datasets exceeding 100GB. Utilizing a separate graph database can complicate your architecture due to fragile ETL processes, which can ultimately raise the total cost of ownership (TCO). PuppyGraph, however, allows you to connect to any data source, irrespective of its location, facilitating cross-cloud and cross-region graph analytics without the need for cumbersome ETLs or data duplication. By directly integrating with your data warehouses and lakes, PuppyGraph empowers you to query your data as a graph while eliminating the hassle of building and maintaining extensive ETL pipelines commonly associated with traditional graph configurations. You can say goodbye to the delays in data access and the unreliability of ETL operations. Furthermore, PuppyGraph addresses scalability issues linked to graphs by separating computation from storage, which enhances efficient data management. Overall, this innovative solution not only boosts performance but also simplifies your overall data strategy, making it a valuable asset for any organization.

GraphDB facilitates the development of extensive knowledge graphs by connecting various data sources and optimizing them for semantic search capabilities. It stands out as a powerful graph database, proficient in handling RDF and SPARQL queries efficiently. Moreover, GraphDB features a user-friendly replication cluster, which has proven effective in numerous enterprise scenarios that demand data resilience during loading processes and query execution. For a concise overview and to access the latest versions, you can check out the GraphDB product page. Utilizing RDF4J for data storage and querying, GraphDB also accommodates a diverse array of query languages, including SPARQL and SeRQL, while supporting multiple RDF syntaxes like RDF/XML and Turtle. This versatility makes GraphDB an ideal choice for organizations seeking to leverage their data more effectively.

AllegroGraph stands out as a groundbreaking solution that facilitates limitless data integration, employing a proprietary method to consolidate fragmented data and information into an Entity Event Knowledge Graph framework designed for extensive big data analysis. By leveraging its distinctive federated sharding features, AllegroGraph delivers comprehensive insights and supports intricate reasoning over a distributed Knowledge Graph. Additionally, users of AllegroGraph can access an integrated version of Gruff, an intuitive browser-based tool for graph visualization that aids in uncovering and understanding relationships within enterprise Knowledge Graphs. Moreover, Franz's Knowledge Graph Solution not only encompasses advanced technology but also offers services aimed at constructing robust Entity Event Knowledge Graphs, drawing upon top-tier products, tools, expertise, and experience in the field. This comprehensive approach ensures that organizations can effectively harness their data for strategic decision-making and innovation.

Memgraph presents a robust and agile graph platform that includes the Memgraph Graph Database, the MAGE Library, and the Memgraph Lab Visualization tool. This innovative graph database is designed to enable rapid and efficient analysis of data, relationships, and dependencies. It features an extensive array of pre-existing deep path traversal algorithms, alongside a collection of traditional, dynamic, and machine learning algorithms specifically created for in-depth graph analysis, making Memgraph a prime option for crucial decision-making processes like risk evaluation (including fraud detection, cybersecurity threat assessment, and criminal risk analysis), comprehensive data and network exploration (such as Identity and Access Management, Master Data Management, and Bill of Materials), and the optimization of logistics and networks. Additionally, Memgraph boasts a thriving open-source community that unites over 150,000 developers from more than 100 countries, fostering collaboration and innovation in developing the next generation of in-memory data-driven applications, particularly in the realms of GenAI, LLMs, and real-time analytics with streaming data. Such a vibrant community not only enhances the platform's capabilities but also ensures that it remains at the forefront of technology advancements in the graph database ecosystem.

GUN is an innovative graph database engine that is decentralized, operates in real-time, and prioritizes an offline-first approach, enabling effortless data handling, sharing, and storage within applications without the complexities of managing servers or dealing with network requests, as well as addressing offline modifications or concurrency issues. This nimble and effective system for data synchronization functions wherever JavaScript is available, allowing developers to focus on their application's data requirements instead of technical obstacles. By leveraging GUN, you can quickly develop cutting-edge applications that capitalize on robust internet features like decentralization and true privacy, which ultimately aligns with the vision of a more open and liberated web. GUN is designed to be a flexible database solution that functions seamlessly across various platforms, including browsers, mobile devices, and servers, giving you the freedom to design your preferred data management framework. Additionally, GUN facilitates the creation of applications that not only enhance user experience but also prioritize privacy while minimizing reliance on conventional infrastructures, making it a valuable tool for modern development.

Apache Giraph is a robust framework that enables scalable iterative processing of graphs, making it ideal for managing extensive datasets. A prime example of its application is Facebook, where it is employed to analyze the complex social graph that emerges from user interactions and relationships. Originally created as an open-source counterpart to Google's Pregel, which was introduced in a 2010 paper, Giraph embodies the principles laid out in Leslie Valiant's Bulk Synchronous Parallel model for distributed computing. Besides the core functionalities inherited from Pregel, Giraph boasts several improvements, including master computation, sharded aggregators, edge-centric input methods, and support for out-of-core processing. Thanks to its ongoing development, driven by an active global community, Giraph stands out as an exceptional choice for harnessing the capabilities of structured datasets on a large scale. Furthermore, its seamless integration into the Apache Hadoop ecosystem enhances its attractiveness for developers and data scientists, making it a versatile tool for various data processing tasks. This adaptability ensures that Giraph remains at the forefront of graph processing technology.

Fluree is a Clojure-based RDF graph database that is immutable and compliant with W3C standards, featuring support for both JSON and JSON-LD while integrating multiple RDF ontologies. It utilizes an immutable ledger that ensures the security of transactions through cryptographic means, alongside providing a versatile RDF graph database that can handle a wide range of queries. SmartFunctions are employed within the system to enforce essential data management protocols, which cover aspects such as identity and access management, along with maintaining data quality. Furthermore, Fluree is designed with a scalable, cloud-native architecture that leverages a lightweight Java runtime, allowing for the independent scalability of its ledger and graph database components. This innovative approach reflects a "Data-Centric" philosophy, positioning data as a reusable asset that exists apart from specific applications, which ultimately enhances its versatility and utility across various use cases. With these features, Fluree successfully addresses modern data management challenges while promoting robust security and accessibility.

The cornerstone of developing intelligent systems is the database, with Grakn exemplifying a highly advanced knowledge graph database. It boasts a remarkably intuitive and expressive data schema, enabling the creation of hierarchies, hyper-entities, hyper-relations, and rules for constructing comprehensive knowledge models. Grakn employs an intelligent language that performs logical inferences on various data types, relationships, attributes, and complex patterns in real-time across both distributed and stored data. Additionally, it includes integrated distributed analytics algorithms, such as Pregel and MapReduce, which users can easily access through simple queries within the language. The system abstracts low-level patterns effectively, making it easier to articulate intricate constructs while ensuring that query execution is optimized automatically. By utilizing Grakn KGMS and Workbase, businesses can efficiently expand their knowledge graphs. Moreover, this distributed database is designed to operate optimally across a network of computers by employing strategies like partitioning and replication, which guarantees consistent scalability and performance. Ultimately, Grakn not only streamlines the management of complex data but also enhances the overall capability of intelligent systems.

HyperGraphDB is an adaptable open-source data storage solution built on an advanced knowledge management framework utilizing directed hypergraphs. Initially designed for persistent memory applications within fields like knowledge management, artificial intelligence, and semantic web projects, it also serves as an embedded object-oriented database for Java applications of various sizes, functioning as both a graph database and a non-SQL relational database. The architecture is underpinned by generalized hypergraphs, where tuples act as the core storage elements; these tuples may include zero or more other tuples and are known as atoms. The data model enables a relational perspective, which supports higher-order, n-ary relationships, or a graph-based view, where edges can connect a diverse array of nodes and other edges. Each atom possesses a strongly-typed value that is highly customizable, with the type system deeply integrated into the hypergraph structure. This adaptability empowers developers to modify the database to meet specific project needs, establishing it as a powerful option for a variety of applications. Additionally, the system's design encourages innovative uses, making it a valuable resource for both seasoned developers and newcomers exploring advanced data management solutions.

RecallGraph is a graph data storage solution that keeps a detailed history of all modifications made to its data, including vertices and edges, allowing it to reflect its current status. It enables users to perform time-specific graph traversals, meaning they can access and query any previous version of the graph, in addition to the current one. This system is particularly useful for scenarios where information is best depicted through interconnected edges and vertices (i.e., as a graph). 1. Each edge and vertex can hold various attributes in the format of key/value pairs, similar to JSON objects. 2. The documents representing edges and vertices can evolve over time, altering their individual attributes/values and their interconnections. 3. Preserving the historical context of documents is crucial, as past versions are as valuable as the present, necessitating a reliable way to maintain and access their change history. In addition, this blog post provides a comprehensive introduction - https://blog.recallgraph.tech/never-lose-your-old-data-again, which further elaborates on the features and advantages of using RecallGraph.

HugeGraph is a highly efficient and scalable graph database designed to handle billions of vertices and edges with impressive performance, thanks to its strong OLTP functionality. This database facilitates effortless storage and querying, making it ideal for managing intricate data relationships. Built on the Apache TinkerPop 3 framework, it enables users to perform advanced graph queries using Gremlin, a powerful graph traversal language. A standout feature is its Schema Metadata Management, which includes VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, granting users extensive control over graph configurations. Additionally, it offers Multi-type Indexes that support precise queries, range queries, and complex conditional queries, further enhancing its querying capabilities. The platform is equipped with a Plug-in Backend Store Driver Framework, currently compatible with various databases such as RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also providing the flexibility to integrate further backend drivers as needed. Furthermore, HugeGraph seamlessly connects with Hadoop and Spark, augmenting its data processing prowess. By leveraging Titan's storage architecture and DataStax's schema definitions, HugeGraph establishes a robust framework for effective graph database management. This rich array of features solidifies HugeGraph’s position as a dynamic and effective solution for tackling complex graph data challenges, making it a go-to choice for developers and data architects alike.

RushDB stands out as a groundbreaking, open-source graph database that eliminates the need for complex configurations and swiftly transforms JSON and CSV files into a fully normalized and queryable Neo4j graph, effortlessly bypassing the typical hurdles linked to schema design, migrations, and manual indexing. Designed with modern applications and AI-driven workflows in mind, RushDB provides developers with a seamless experience that blends the flexibility of NoSQL with the structured functionality of relational databases. With features like automatic data normalization, ACID compliance, and a powerful API, RushDB simplifies the often daunting tasks of data ingestion, relationship management, and query optimization, empowering developers to focus on application development instead of database administration. Noteworthy attributes of RushDB include: 1. Immediate data ingestion without requiring any configuration 2. Graph technology that enhances both storage and querying functions 3. ACID transaction support along with effortless schema evolution 4. An API tailored for developers, making querying as easy as utilizing an SDK 5. Exceptional performance for both search and analytical purposes 6. The option for self-hosting or compatibility with cloud solutions. This unique set of features not only elevates RushDB as an essential tool in data management but also signifies its potential to redefine how developers interact with data structures.

Neo4j's graph platform is crafted to enable users to maximize the potential of data and its interconnections. By utilizing Neo4j, developers can build smart applications capable of navigating the vast, interconnected datasets of today in real-time. This platform features a native graph storage engine coupled with a processing engine that enhances its capabilities. Furthermore, it offers distinctive, actionable insights through a user-friendly, adaptable, and secure database environment, making it an essential tool for modern data analysis.

Azure Cosmos DB is a fully managed NoSQL database solution tailored for modern application development, delivering guaranteed response times in just a few milliseconds and boasting an impressive availability rate of 99.999%, as outlined in its service level agreements (SLAs). It offers automatic scaling and is compatible with popular open-source APIs such as MongoDB and Cassandra, allowing developers to utilize familiar tools with ease. With its turnkey multi-master global distribution, users benefit from swift read and write operations from virtually anywhere across the globe. Additionally, it empowers organizations to reduce the time needed for insights by enabling near-real-time analytics and artificial intelligence on the operational data stored within Azure Cosmos DB. The integration with Azure Synapse Link also streamlines the connection to Azure Synapse Analytics, facilitating efficient data analysis without requiring data movement or affecting the operational data store's performance. This robust set of features positions Azure Cosmos DB as an exceptional choice for developers seeking both high performance and reliability in their applications, making it an invaluable resource in the realm of cloud databases. Ultimately, organizations leveraging this technology can enhance their operational efficiency and drive innovation more effectively.

Cayley is an open-source database specifically designed for Linked Data, inspired by the graph database that underpins Google's Knowledge Graph, which was formerly known as Freebase. This database prioritizes user-friendly design while effectively managing complex data structures; it includes a built-in query editor, visualizer, and Read-Eval-Print Loop (REPL) functionality. It supports a variety of query languages, such as Gizmo, which is modeled after Gremlin, a query language similar to GraphQL, along with MQL, a simplified version aimed at Freebase users. With its modular architecture, Cayley seamlessly integrates with preferred programming languages and backend storage solutions, making it production-ready and well-tested, with numerous companies relying on it for their operational needs. Furthermore, its optimization for application use showcases remarkable performance; for example, it has been demonstrated to handle 134 million quads in LevelDB on standard 2014 hardware, with multi-hop intersection queries—like identifying films featuring both X and Y—executing in around 150 milliseconds. By default, Cayley operates in-memory, reflecting the capabilities of its backend memstore, which significantly boosts its speed and efficiency during data retrieval and manipulation. Overall, Cayley stands as a robust solution for developers aiming to harness linked data in their applications, while its continuous improvements and community support ensure it keeps pace with evolving technological demands.

Blazegraph™ DB is a remarkably efficient graph database that supports both Blueprints and RDF/SPARQL APIs, capable of managing as many as 50 billion edges within a single system. This advanced database is currently employed by numerous Fortune 500 companies, including prominent names like EMC and Autodesk. Its significance extends to critical applications in Precision Medicine and it is widely used in the life sciences sector. Moreover, Blazegraph plays a vital role in cyber analytics across various commercial and governmental organizations. The database also underpins the Wikidata Query Service of the Wikimedia Foundation, showcasing its adaptability and dependability. Users have the flexibility to choose from different distribution formats, such as an executable jar, war file, or tar.gz, making it suitable for a range of deployment options. Designed with an emphasis on user convenience, Blazegraph is easy to install; however, it lacks SSL and authentication by default, which is an important factor for new users to consider. For production deployments, it is highly recommended to enable SSL, implement user authentication, and correctly configure the network to enhance security. Additionally, a wealth of resources and links are provided below to guide you through these necessary configurations and ensure optimal performance. This commitment to security and usability makes Blazegraph a compelling choice for organizations seeking robust graph database solutions.