Guide
Best Graph Databases
Graph databases model data as nodes and relationships, making them ideal for connected data — knowledge graphs, fraud detection, recommendations, and network analysis — where relationship traversals would be slow and awkward in SQL. The category includes native graph engines with query languages like Cypher, distributed and GraphQL-native options, multi-model databases, and in-memory engines built for real-time analytics. The right pick depends on your query patterns, scale, and whether you need real-time streaming analytics or a general-purpose graph store. Consider the query language, clustering, and licensing. Below are widely used graph databases, compared on features, pricing, and the connected-data problems they solve best.
4 tools reviewed
Why this matters
When relationships are the core of your data, a graph database turns expensive multi-join queries into fast, natural traversals. Picking the right engine depends on query language, scale, and whether you need real-time analytics.
Featured tools
One database for graph, document and key-value data.
Distributed, GraphQL-native graph database.
In-memory graph database for real-time streaming analytics.
The world's leading graph database platform.
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Frequently asked questions
When should I use a graph database?+
When relationships between entities are central — recommendations, fraud rings, social networks, and knowledge graphs — and traversals dominate your queries.
What query language do graph databases use?+
Many use Cypher (or openCypher); others use Gremlin or GraphQL-native APIs. Query language compatibility can ease migration between engines.
Are graph databases open source?+
Several have free, open-source editions you can self-host, with paid enterprise features and managed cloud options.
Can a relational or multi-model database do graphs?+
Multi-model databases handle graph plus document/key-value data; pure relational stores can model graphs but struggle with deep traversals at scale.