Best Graph Databases for MongoDB

Find and compare the best Graph Databases for MongoDB in 2026

Use the comparison tool below to compare the top Graph Databases for MongoDB on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    ArcadeDB Reviews
    ArcadeDB is a high-performance, open-source multi-model database that unifies graphs, documents, key-value, search engine, vectors, and time-series data in a single engine. Each model is native — no translation overhead, no external adapters. Built for developers who refuse to compromise: 10M+ records/second, constant graph traversal speed regardless of size, and 6 query languages out of the box — SQL, Cypher (native OpenCypher engine,TCK-compliant), Gremlin, GraphQL, MongoDB API, and Java. Runs embedded in your JVM, standalone, or distributed across an HA cluster using Raft Consensus. ACID-compliant, fully transactional, and extremely lightweight. Stop running five separate databases for five data models. One database. Every model. Apache 2.0 — open source forever.
  • 2
    GraphDB Reviews
    *GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.
  • 3
    AllegroGraph Reviews
    AllegroGraph represents a revolutionary advancement that facilitates limitless data integration through a proprietary methodology that merges all types of data and isolated knowledge into a cohesive Entity-Event Knowledge Graph, which is capable of handling extensive big data analytics. It employs distinctive federated sharding features that promote comprehensive insights and allow for intricate reasoning across a decentralized Knowledge Graph. Additionally, AllegroGraph offers an integrated version of Gruff, an innovative browser-based tool designed for visualizing graphs, helping users to explore and uncover relationships within their enterprise Knowledge Graphs. Furthermore, Franz's Knowledge Graph Solution encompasses both cutting-edge technology and expert services aimed at constructing robust Entity-Event Knowledge Graphs, leveraging top-tier tools, products, and extensive expertise to ensure optimal performance. This comprehensive approach not only enhances data utility but also empowers organizations to derive deeper insights and drive informed decision-making.
  • 4
    TerminusDB Reviews
    Simplifying data collaboration is our mission. Whether you're a developer eager to push the boundaries of innovation or a data specialist seeking effective version control, our platform caters to all needs. TerminusDB is an open-source knowledge graph database designed to facilitate secure, efficient, and private revision control and teamwork. By using TerminusDB, you can enhance your productivity when collaborating with teammates or developing data-heavy applications. It offers a comprehensive set of revision control functionalities. With TerminusHub, users can oversee database access and work together on shared resources seamlessly. Our platform boasts versatile capabilities for data storage, sharing, and versioning, making it suitable for both team-based collaboration and integration into applications. You can operate locally and sync changes when you're ready. Our tools also simplify querying, data cleaning, and visualization tasks. By incorporating robust version control and collaborative features, we empower both enterprise and individual users, enabling remote data teams to collaborate efficiently on projects. Together, we can drive innovation through effective data teamwork.
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