# Best Graph Database Solutions - Page 2 *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)* Graph databases use topographical data models to store data. These databases connect specific data points (nodes) and create relationships (edges) in the form of graphs that can then be pulled by the user with queries. Nodes can represent customers, companies, or any data a company chooses to record. Edges are formed by the database so that relationships between nodes are easily understood by the user. Businesses can utilize graph databases when they are pulling data and do not want to spend time organizing it into distinct relationships. Large enterprises may use complex queries to pull precise and in-depth information regarding their customer and user information or product tracking data, among other uses. Database administrators can scale high data values and still create usable models. Some businesses may choose to run an RDF database, a type of graph database that focuses on retrieving triples, or information organized in a subject-predicate-object relationship. Similar types of databases include [document database](https://www.g2.com/categories/document-databases) tools, [key-value store](https://www.g2.com/categories/key-value-stores) tools, [object-orientated database](https://www.g2.com/categories/object-oriented-databases) tools and more. Developers who are looking for an affordable solution can look to [free database software](https://learn.g2.com/free-database-software). To qualify for inclusion in the Graph Database category, a product must: - Provide data storage - Record and represent data in a topographical schema - Allow users to retrieve the data using query language ## How Many Graph Databases Products Does G2 Track? **Total Products under this Category:** 68 ### Category Stats (May 2026) - **Average Rating**: 4.48/5 - **New Reviews This Quarter**: 1 - **Buyer Segments**: Enterprise 67% │ Small-Business 33% - **Top Trending Product**: Elastic Stack (+0.018) *Last updated: May 18, 2026* ## How Does G2 Rank Graph Databases Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,000+ Authentic Reviews - 68+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Graph Databases Is Best for Your Use Case? - **Leader:** [Arango](https://www.g2.com/products/arango/reviews) - **Highest Performer:** [GraphJSON](https://www.g2.com/products/graphjson/reviews) - **Easiest to Use:** [Amazon Neptune](https://www.g2.com/products/amazon-neptune/reviews) - **Top Trending:** [Stardog](https://www.g2.com/products/stardog/reviews) - **Best Free Software:** [Neo4j Graph Database](https://www.g2.com/products/neo4j-graph-database/reviews) --- **Sponsored** ### Couchbase Couchbase’s operational data platform for AI is a scalable foundation for enterprise operational, analytical, mobile and AI workloads that replaces legacy infrastructure and data services. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=304&secure%5Bdisplayable_resource_id%5D=306&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=306&secure%5Bplacement_resource_ids%5D%5B%5D=307&secure%5Bplacement_resource_ids%5D%5B%5D=318&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1263&secure%5Bresource_id%5D=304&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fgraph-databases%3Flocale%3Des%26page%3D2&secure%5Btoken%5D=0b354a631c526bc06db1087052b38330335ebd80dad1311a2bc3210223085b7d&secure%5Burl%5D=https%3A%2F%2Fwww.couchbase.com%2F%3Futm_source%3Dg2%26utm_medium%3Ddisplay&secure%5Burl_type%5D=custom_url) --- ## What Are the Top-Rated Graph Databases Products in 2026? ### 1. [AllegroGraph](https://www.g2.com/products/allegrograph/reviews) AllegroGraph® is a modern, high-performance, persistent graph database. AllegroGraph uses efficient memory utilization in combination with disk-based storage. AllegroGraph supports SPARQL, RDFS++, and Prolog reasoning from numerous client applications. **Average Rating:** 5.0/5.0 **Total Reviews:** 3 **How Do G2 Users Rate AllegroGraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind AllegroGraph?** - **Seller:** [Franz](https://www.g2.com/sellers/franz) - **Year Founded:** 1984 - **HQ Location:** Lafayette, US - **Twitter:** @Franzinc (2,144 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/franz-inc (44 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 75% Small-Business, 25% Mid-Market ### 2. [Bitsy](https://www.g2.com/products/bitsy/reviews) Bitsy is a small, fast, embeddable, durable in-memory graph database that implements the Blueprints API. **Average Rating:** 4.8/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Bitsy?** - **Data Model:** 9.2/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 10.0/10 (Category avg: 8.4/10) **Who Is the Company Behind Bitsy?** - **Seller:** [Bitsy](https://www.g2.com/sellers/bitsy) - **HQ Location:** Cottonwood Heights, UT - **Twitter:** @Bitbucket (46,631 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 33% Enterprise, 33% Mid-Market ### 3. [Connected Papers](https://www.g2.com/products/connected-papers/reviews) https://www.connectedpapers.com is a unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work. It started as a weekend side project between friends. When we saw how much it improved our own research and development workflows - and got more and more requests from friends and colleagues to use it - we committed to release it to the public. You know... for science. **Average Rating:** 4.5/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Connected Papers?** - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 9.2/10 (Category avg: 8.4/10) **Who Is the Company Behind Connected Papers?** - **Seller:** [Connected Papers](https://www.g2.com/sellers/connected-papers) - **Year Founded:** 2019 - **HQ Location:** N/A - **LinkedIn® Page:** http://www.linkedin.com/company/connectedpapers (4 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Mid-Market ### 4. [HGraphDB](https://www.g2.com/products/hgraphdb/reviews) HGraphDB is a client layer for using HBase as a graph database. It is an implementation of the Apache TinkerPop 3 interfaces. **Average Rating:** 4.5/5.0 **Total Reviews:** 3 **How Do G2 Users Rate HGraphDB?** - **Has the product been a good partner in doing business?:** 6.7/10 (Category avg: 8.8/10) - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 8.3/10 (Category avg: 8.4/10) **Who Is the Company Behind HGraphDB?** - **Seller:** [Apache HBase](https://www.g2.com/sellers/apache-hbase) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Enterprise ### 5. [Timbr](https://www.g2.com/products/timbr/reviews) Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity. **Average Rating:** 4.4/5.0 **Total Reviews:** 7 **How Do G2 Users Rate Timbr?** - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 9.2/10 (Category avg: 8.8/10) - **Built - In Search:** 8.3/10 (Category avg: 8.4/10) **Who Is the Company Behind Timbr?** - **Seller:** [Timbr.ai](https://www.g2.com/sellers/timbr-ai) - **Year Founded:** 2018 - **HQ Location:** Raanana , IL - **LinkedIn® Page:** https://www.linkedin.com/company/timbr-ai (9 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 63% Small-Business, 38% Enterprise #### What Are Timbr's Pros and Cons? **Pros:** - Features (2 reviews) - SQL Support (2 reviews) - Automation (1 reviews) - Data Analysis (1 reviews) - Data Management (1 reviews) **Cons:** - Learning Curve (2 reviews) - Complex Usability (1 reviews) - Expensive (1 reviews) - Limited Customization (1 reviews) ### 6. [ArchiGraph platform](https://www.g2.com/products/archigraph-platform/reviews) ArchiGraph is an ontology-based data management and data virtualization platform. It includes a collaborative ontology editor, a SHACL constraints and rules construction tool, and a middleware layer providing API access to the ontology-aligned data from various storages, and a set of supplementary utilities. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate ArchiGraph platform?** - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 6.7/10 (Category avg: 8.4/10) **Who Is the Company Behind ArchiGraph platform?** - **Seller:** [TriniData](https://www.g2.com/sellers/trinidata) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Enterprise, 33% Small-Business ### 7. [BaseQL](https://www.g2.com/products/baseql/reviews) BaseQL provides a dynamic GraphQL API for Airtable bases and Google Sheets. BaseQL is built for speed of development without the hassle of a managed database or complicated REST endpoints. It enables engineers and low/no coders everywhere to quickly and simply build custom applications using user-friendly data tools. **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate BaseQL?** - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 6.7/10 (Category avg: 8.4/10) **Who Is the Company Behind BaseQL?** - **Seller:** [BaseQL](https://www.g2.com/sellers/baseql) - **HQ Location:** Miami, US - **LinkedIn® Page:** https://www.linkedin.com/company/baseql/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 8. [Graphwise GraphDB](https://www.g2.com/products/graphwise-graphdb/reviews) Graphwise GraphDB is an enterprise-ready Semantic Graph database and a key component of the Graphwise Graph AI suite. Designed for knowledge graphs, complex data integration, and AI applications, it supports W3C standards and provides secure, high-performance querying. It integrates into AI-agentic workflows to improve LLM accuracy, enabling information to be easily identified, disambiguated, and interconnected. Graphwise GraphDB addresses business problems involving highly interconnected data that challenges relational databases. It helps organizations uncover patterns using inference and fast full-text/faceted searches via synchronized services like Elasticsearch and OpenSearch. GraphDB guarantees high availability and no data loss through cluster and multi-cluster deployments. This architecture allows enterprises to move from fragmented data to a unified, AI-ready graph environment. **Average Rating:** 4.2/5.0 **Total Reviews:** 6 **How Do G2 Users Rate Graphwise GraphDB?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Graphwise GraphDB?** - **Seller:** [Graphwise](https://www.g2.com/sellers/graphwise) - **Year Founded:** 2024 - **HQ Location:** Sofia, BG - **LinkedIn® Page:** https://linkedin.com/company/graphwise (134 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 33% Mid-Market ### 9. [Gun](https://www.g2.com/products/gun/reviews) GUN is a realtime, distributed, offline-first, graph database engine. Lightweight and powerful, at just ~9KB gzipped. **Average Rating:** 3.8/5.0 **Total Reviews:** 2 **Who Is the Company Behind Gun?** - **Seller:** [Gun.io](https://www.g2.com/sellers/gun-io) - **Year Founded:** 2013 - **HQ Location:** Nashville, TN - **Twitter:** @GUNdotIO (8,854 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2499131/ (107 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 10. [HyperGraphDB](https://www.g2.com/products/hypergraphdb/reviews) HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database. Or a (non-SQL) relational database. **Average Rating:** 4.0/5.0 **Total Reviews:** 2 **Who Is the Company Behind HyperGraphDB?** - **Seller:** [HyperGraphDB](https://www.g2.com/sellers/hypergraphdb) - **HQ Location:** Montreal, Canada - **Twitter:** @hypergraphdb (22 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Small-Business ### 11. [Memgraph](https://www.g2.com/products/memgraph/reviews) Memgraph is a high-performance, in-memory graph database that powers real-time AI context and graph analytics at scale. Vector search finds what's similar. Graph reasoning finds what's connected — following relationships, dependencies, and hierarchies that similarity alone can't capture. Modern AI systems need both, and Memgraph is the graph layer - surfacing precise structural context with full audit trails in sub-millisecond time. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows — a single high-performance layer for any system that needs structured, connected context. The same in-memory architecture drives real-time graph analytics for fraud detection, network analysis, infrastructure monitoring, and other operational workloads where milliseconds matter. NASA uses Memgraph to connect people, skills, and projects across the agency into a queryable knowledge graph that powers real-time expert discovery and workforce planning. Cedars-Sinai uses it to link genes, drugs, and clinical pathways in an Alzheimer's knowledge graph spanning over 230,000 entities that drives drug repurposing research and multi-hop biomedical reasoning. Organizations across cybersecurity, finance, retail, and other knowledge-intensive domains rely on Memgraph for the same reason: sub-millisecond graph traversals for the structured context and real-time insight that modern systems demand. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **Who Is the Company Behind Memgraph?** - **Seller:** [Memgraph Ltd.](https://www.g2.com/sellers/memgraph-ltd) - **Year Founded:** 2016 - **HQ Location:** London, GB - **Twitter:** @memgraphdb (1,555 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/memgraph (31 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 12. [NebulaGraph](https://www.g2.com/products/nebulagraph/reviews) A truly distributed, linear scalable, lightning-fast graph database. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate NebulaGraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 10.0/10 (Category avg: 8.4/10) **Who Is the Company Behind NebulaGraph?** - **Seller:** [VeSOFT](https://www.g2.com/sellers/vesoft) - **Year Founded:** 2018 - **HQ Location:** Cupertino, US - **LinkedIn® Page:** https://www.linkedin.com/company/vesoft-nebula-graph/ (119 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market ### 13. [RecallGraph](https://www.g2.com/products/recallgraph/reviews) RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, letting the user query any past state of the graph just as easily as the present. It is a Foxx Microservice for ArangoDB that features VCS-like semantics in many parts of its interface, and is backed by a transactional event tracker. It is currently being developed and tested on ArangoDB v3.5 and v3.6, with support for v3.7 in the pipeline. **Average Rating:** 4.5/5.0 **Total Reviews:** 2 **How Do G2 Users Rate RecallGraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) - **Data Model:** 7.5/10 (Category avg: 8.8/10) - **Data Types:** 7.5/10 (Category avg: 8.8/10) - **Built - In Search:** 7.5/10 (Category avg: 8.4/10) **Who Is the Company Behind RecallGraph?** - **Seller:** [RecallGraph](https://www.g2.com/sellers/recallgraph) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market ### 14. [Relay](https://www.g2.com/products/relay-2021-05-24/reviews) Relay is a JavaScript framework designed for building data-driven React applications using GraphQL. It enables developers to declaratively specify data requirements for each component, allowing Relay to efficiently manage data fetching and ensure that components receive the necessary data without manual intervention. This approach promotes modularity, reusability, and scalability in application development. Key Features and Functionality: - Declarative Data Fetching: Components declare their data needs using GraphQL, and Relay handles the fetching process, ensuring that each component receives the required data. - Colocation of Queries and Components: GraphQL queries are defined alongside the components that use them, making the codebase more maintainable and easier to understand. - Efficient Data Management: Relay's compiler optimizes data requirements across the application, aggregating them into efficient network requests and maintaining a normalized data store for consistent state management. - Automatic UI Updates: Relay ensures that components are updated only when necessary, preventing unnecessary re-renders and maintaining UI consistency. - Support for Mutations and Subscriptions: Relay provides mechanisms for executing GraphQL mutations with optimistic updates and handling real-time data through subscriptions, facilitating dynamic and interactive user experiences. Primary Value and Problem Solved: Relay addresses the complexities of managing data in large-scale React applications by providing a structured and efficient approach to data fetching and state management. By declaratively defining data dependencies and leveraging GraphQL, Relay simplifies the process of ensuring that components have access to the data they need, reduces boilerplate code, and enhances application performance. This results in more maintainable codebases and a better developer experience, especially as applications grow in size and complexity. **Average Rating:** 4.8/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Relay?** - **Data Model:** 7.5/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 7.5/10 (Category avg: 8.4/10) **Who Is the Company Behind Relay?** - **Seller:** [Relay](https://www.g2.com/sellers/relay-7a3bf754-3753-49c6-89c8-9a5abbeaeb92) - **HQ Location:** Indianapolis, US - **LinkedIn® Page:** http://www.linkedin.com/company/relayhq (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market, 50% Small-Business ### 15. [Rocketgraph | Graph Analytics Platform Powered by GenAI](https://www.g2.com/products/rocketgraph-graph-analytics-platform-powered-by-genai/reviews) Rocketgraph xGT uncovers hard-to-find patterns faster and performs analyses that used to take days to run or would stop before producing results. With Rocketgraph and its AI-powered Mission Control user interface, you can do iterative analysis on the biggest, most complicated datasets on the planet and get answers hundreds of times faster than with other graph analytics tools. Rocketgraph can be used on-premise or in the cloud, with Aurora - our fully-managed cloud offering. **Average Rating:** 4.8/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Rocketgraph | Graph Analytics Platform Powered by GenAI?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10) - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 10.0/10 (Category avg: 8.4/10) **Who Is the Company Behind Rocketgraph | Graph Analytics Platform Powered by GenAI?** - **Seller:** [Rocketgraph](https://www.g2.com/sellers/rocketgraph) - **Year Founded:** 2014 - **HQ Location:** Seattle, US - **Twitter:** @Rocketgraph_ai (480 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/rocketgraph (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Enterprise, 33% Small-Business ### 16. [AnzoGraph](https://www.g2.com/products/anzograph/reviews) Discover one of the fastest, most scalable, distributed graph databases purpose-built for high-performance analytical insights across the enterprise. Solving your complex connected data problems at record speed is our business. Use cases include enabling buyer intent analysis, key opinion leader analysis, recommendation engines, machine learning and AI, and even data integration and creation of knowledge graphs. Gain key insights using native graph algorithms, data warehouse-style analytics, data virtualization across the enterprise, location analytics with geospatial, and many other data science and feature engineering capabilities. At its heart, AnzoGraph DB is an analytical graph database, based on RDF\* and SPARQL\*, the new upcoming W3C standard that supports both semantic graphs and properties. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate AnzoGraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind AnzoGraph?** - **Seller:** [Cambridge Semantics](https://www.g2.com/sellers/cambridge-semantics) - **Year Founded:** 2007 - **HQ Location:** Boston, Massachusetts - **LinkedIn® Page:** https://www.linkedin.com/company/202709 (24 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 17. [Apache AGE](https://www.g2.com/products/apache-age/reviews) Apache AGE® is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE® is to provide graph data processing and analytics capability to all relational databases. Through Apache AGE, PostgreSQL users will gain access to graph query modeling within the existing relational database. Users can read and write graph data in nodes and edges. They can also use various algorithms such as variable length and edge traversal when analyzing data. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Apache AGE?** - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 6.7/10 (Category avg: 8.4/10) **Who Is the Company Behind Apache AGE?** - **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation-27b1adce-9be4-4620-85d0-2612155f63e5) - **HQ Location:** N/A - **Twitter:** @apache_age (754 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 18. [ApertureDB](https://www.g2.com/products/aperturedb/reviews) ApertureDB is a vector + graph database purpose-built to streamline the development and scaling of multimodal AI and analytics applications. Designed for modern AI and analytics workflows, it combines multimodal data management, vector search capabilities and knowledge graph into a single integrated solution. With ApertureDB developers and organizations get 2-10X faster vector search performance than the competition, save 6 to 9 months on average in infrastructure setup time and improve machine learning teams productivity by 10X. It powers use cases like semantic search, RAG chatbots, Generative AI applications and AI-driven agents. ApertureDB seamlessly integrates across your AI stack including popular large scale Language Models (LLMS), AI and machine learning frameworks and workflows. Its robust multi-tenant architecture, designed to handle complex multimodal data text, images, videos, embeddings, metadata and easily scales for large-scale deployments while maintaining enterprise-grade performance and reliability. ApertureDB offers flexible deployment options and optimized pricing performance. Available in the cloud, on-premises or hybrid, ApertureDB meets the needs of diverse organizations, from startups to large enterprises. Our optimized pricing empowers teams to choose a deployment model that aligns with their budget and can scale effortlessly without breaking the bank. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate ApertureDB?** - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 10.0/10 (Category avg: 8.4/10) **Who Is the Company Behind ApertureDB?** - **Seller:** [ApertureData](https://www.g2.com/sellers/aperturedata) - **Year Founded:** 2018 - **HQ Location:** Mountain View, US - **LinkedIn® Page:** https://www.linkedin.com/company/aperturedata (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 19. [Blazegraph](https://www.g2.com/products/blazegraph/reviews) Blazegraph is a scalable, high-performance graph database with support for the Blueprints and RDF/SPARQL APIs. Blazegraph is available in a range of versions that provide solutions to the challenge of scaling graphs. Blazegraph solutions range from millions to trillions of edges in the graph. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Blazegraph?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Blazegraph?** - **Seller:** [Blazegraph](https://www.g2.com/sellers/blazegraph) - **Year Founded:** 2006 - **HQ Location:** Greensboro, US - **Twitter:** @BlazeGraph (695 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/10445544 (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 20. [Graph Engine](https://www.g2.com/products/graph-engine/reviews) Graph Engine (GE) is a distributed, in-memory, large graph processing engine, underpinned by a strongly-typed RAM store and a general computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Graph Engine?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10) - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 6.7/10 (Category avg: 8.4/10) **Who Is the Company Behind Graph Engine?** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,100,852 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 21. [TerminusDB](https://www.g2.com/products/terminusdb/reviews) TerminusDB is an open source in-memory graph database designed for the web age. TerminusDB makes a radical departure from historical architectures. First, we implement a graph database with a strong schema so as to retain both simplicity and generality of design. Second, we implement this graph using succinct immutable data structures which enables more sparing use of main memory resources. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate TerminusDB?** - **Data Model:** 8.3/10 (Category avg: 8.8/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Built - In Search:** 8.3/10 (Category avg: 8.4/10) **Who Is the Company Behind TerminusDB?** - **Seller:** [TerminusDB](https://www.g2.com/sellers/terminusdb) - **Year Founded:** 2019 - **HQ Location:** Dublin, IE - **Twitter:** @TerminusDB (1,206 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/42874959 (4 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 22. [TIBCO Graph Database](https://www.g2.com/products/tibco-graph-database/reviews) TIBCO Graph Database allows you to discover, store, and convert complex dynamic data into meaningful insights. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **Who Is the Company Behind TIBCO Graph Database?** - **Seller:** [Cloud Software Group](https://www.g2.com/sellers/cloud-software-group) - **HQ Location:** Fort Lauderdale, FL - **Twitter:** @cloudsoftware (124 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/cloudsoftwaregroup/ (9,677 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 23. [TopQuadrant](https://www.g2.com/products/topquadrant/reviews) TopBraid EDG is a comprehensive data governance platform designed to help organizations manage, connect, and make sense of their data assets with unmatched flexibility and transparency. Powered by semantic web standards and knowledge graph technology, TopBraid EDG helps build an AI-ready data foundation for governing your data at scale. By seamlessly integrating taxonomies, ontologies, structured and unstructured data, and policies, TopBraid EDG enables dynamic, enterprise-wide semantic interoperability to connect disparate systems, drive smarter decision-making and compliance that inject policy into the data layer. With its scalable, extensible architecture, TopBraid EDG empowers organizations to unlock the full potential of their data while maintaining trust, accuracy, and agility. **Average Rating:** 4.7/5.0 **Total Reviews:** 3 **How Do G2 Users Rate TopQuadrant?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind TopQuadrant?** - **Seller:** [TopQuadrant](https://www.g2.com/sellers/topquadrant) - **Company Website:** https://topquadrant.com - **Year Founded:** 2001 - **HQ Location:** Raleigh, US - **LinkedIn® Page:** https://www.linkedin.com/company/topquadrant (35 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Mid-Market ### 24. [VelocityGraph](https://www.g2.com/products/velocitygraph/reviews) VelocityGraph extends the object-oriented database VelocityDB into a graph database. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate VelocityGraph?** - **Data Model:** 10.0/10 (Category avg: 8.8/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Built - In Search:** 6.7/10 (Category avg: 8.4/10) **Who Is the Company Behind VelocityGraph?** - **Seller:** [VelocityDB](https://www.g2.com/sellers/velocitydb) - **Year Founded:** 2011 - **HQ Location:** N/A - **Twitter:** @VelocityDB (62 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/velocitydb-inc-/ (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 200% Mid-Market ### 25. [Aerospike](https://www.g2.com/products/aerospike/reviews) The Aerospike Real-time Data Platform enables organizations to act instantly across billions of transactions while reducing server footprint by up to 80 percent. The Aerospike multi-cloud platform powers real-time applications with predictable sub-millisecond performance up to petabyte-scale with five-nines uptime with globally distributed, strongly consistent data. Applications built on the Aerospike Real-time Data Platform fight fraud, provide recommendations that dramatically increase shopping cart size, enable global digital payments, and deliver hyper-personalized user experiences to tens of millions of customers. Customers such as Airtel, Experian, Nielsen, PayPal, Snap, Wayfair, and Yahoo rely on Aerospike as their data foundation for the future. **Average Rating:** 4.4/5.0 **Total Reviews:** 80 **How Do G2 Users Rate Aerospike?** - **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.8/10) **Who Is the Company Behind Aerospike?** - **Seller:** [Aerospike](https://www.g2.com/sellers/aerospike) - **Year Founded:** 2009 - **HQ Location:** Mountain View, CA - **Twitter:** @aerospikedb (7,829 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2696852/ (306 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Software Engineer - **Top Industries:** Marketing and Advertising, Information Technology and Services - **Company Size:** 45% Mid-Market, 34% Enterprise ## What Is Graph Databases? [IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure) ## What Software Categories Are Similar to Graph Databases? - [Document Databases](https://www.g2.com/categories/document-databases) - [Key Value Databases](https://www.g2.com/categories/key-value-databases) - [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas) --- ## How Do You Choose the Right Graph Databases? ### What You Should Know About Graph Databases ### What are Graph Databases? Graph databases are designed for depicting relationships (_edges_) between data points (_nodes_). Less structurally rigid than relational databases, graph databases allow nodes to have a multitude of edges; that is, there’s no limit on the number of relationships a node can have. (An example of this is in the following section.) Additionally, each edge can have multiple characteristics which define it. There is no formal limit—nor standardization—on how many edges each node can have, nor how many characteristics an edge can have. Graph databases can also contain many different pieces of information that would not necessarily be normally related. Each node is defined by pieces of information called _properties_. Properties could be names, dates, identification numbers, basic descriptors, or other information—anything that would describe the node itself. Nodes are connected by edges, which can be _directed_ or _undirected_. Like in mathematical graph theory, an undirected edge is _bidirectional_; that is, a relationship can be carried from node A to node B, and from node B to node A. A directed edge, however, only carries meaning in one direction, say from node B to node A. Key Benefits of Graph Databases - Organize a variety of data without rigid structures - Offer flexible scaling and adjustment inherently - Describe numerous data relationship characteristics simultaneously ### Why Use Graph Databases? Graph databases are ideal for storing and retrieving information that is independent but related in multiple ways. For example, say a user wanted to map a group of friends. Each friend would be a node, with edges between each friend with a characteristic “friends." But, say two of those friends are coworkers; then, their edge would also have a characteristic “coworkers." Edges can get further definition by adding common interests, personal experiences, and so on. Because graph databases are, by design, most conducive to organizing broad sets of data through which there are not uniform relationships or kinds of data, they can be invaluable tools for social mapping, master data management, knowledge graphing/ontology, infrastructure mapping, recommendation engines, and more. A business could set each node to be one of their products, and let edges draw recommendation relationships based on what product a consumer might buy. It could also map relationships between contacts, departments, and more. Graph databases are flexible and scalable by design, so a business user would not need to know an exact or complete use case for a graph database before creating it. Expanding a graph database is a matter of adding new nodes and any potential edges which might be associated with them. ### Who Uses Graph Databases? Like other databases, graph databases are primarily maintained by a database administrator or team. That said, because of their wide range of coverage, graph databases are often accessed by several organizations within a company. Development, IT, billing, and more would all have valid reasons for needing access to graph databases, pending their assigned uses within the company. ### Graph Databases Features Graph database solutions will typically have the following features. Database creation and maintenance — Graph databases allow users to easily build and maintain a database(s). CRUD operations — An acronym for _create, read, update, and delete_, CRUD operations delineate basic operations of many databases. Graph databases should be able to perform these operations and usually can with similar capability to the most notable CRUD-oriented database type, relational. Scalability and flexibility — Graph databases can grow and expand with business requirements. Unlike some other database solutions, they can scale more quickly with less worry about strict data organization, relying instead on developing relationships between new and existing nodes. Simplified querying — Graph databases can skip some larger query complexities, bypassing things like foreign keys, nested queries, and join statements in favor of direct or transitive relationships. OS compatibility — Graph databases do not require any one specific operating system to run, making them a flexible choice for any operating system. ### Trends Related to Graph Databases Graph vs. relational — The graph database vs. relational database discussion is an ongoing point of conflict for database users and administrators alike. Graph databases generally lend themselves to more fluid data querying off simpler querying syntax, and are generally better at scaling without needing to prepare new or specific schema. But, relational databases’ schema rigidity and data normalization can be extremely beneficial in some use cases, and they are also generally more conducive to security and privacy policy implementation and enforcement. ### Potential Issues with Graph Databases Security and privacy — As alluded to above, graph databases can struggle with security and privacy situations. They require more strict implementations of security and access measures. Since graph databases are more oriented toward mapping relationships, that structure can also be utilized in ways that could raise privacy concerns, such as revealing a more laid-bare view of a client or customer—and every other potential client or customer to which they are related. Businesses implementing graph databases should take extra care to secure both how these databases are accessed, and the databases themselves. Data integrity implications — Graph databases simplify the ways in which information relates to other information. In doing so, by shortening or condensing the relationship (as compared to, say, traversing numerous tables in a relational database), it’s particularly vital that all data in a graph database is accurate. One improperly aligned relationship can directly lead to incorrect data, unlike in a relational database where improper data might hit a snag during a nested query, throw an error, and out the issue. So, in using graph databases, data integrity is of particularly high importance. ### Software and Services Related to Graph Databases Many conversations around graph databases are contextualized by one or both of the following alternatives. [RDF databases](https://www.g2.com/categories/rdf-databases) — A type of graph database, resource description framework (RDF) or _triplestore_ databases function around the concept of storing data as triples. Triples—in a “subject–predicate–object" format—are used specifically to describe the relationship between two things. [Relational databases](https://www.g2.com/categories/relational-databases) — Relational databases—the standard “rows and columns" data stores—had been the standard for databases virtually since inception. They carry with them significantly more rigid structure than graph databases, which can be extremely beneficial for tracking large volumes of like data but might make it more complicated to follow relationships between that data.