Neo4j is the most widely‑adopted native graph database, known for its ACID‑compliant transactions, Cypher query language, and extensive ecosystem. If you’re looking for other graph‑oriented or multi‑model database platforms that can serve as back‑end storage for connected data, the list below provides twenty strong alternatives, each with its own strengths, licensing models, and cloud‑native options.
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Fully managed graph database service on AWS supporting both Gremlin and openCypher, with high‑availability and seamless integration with other AWS services.
Globally distributed, multi‑model database offering a Gremlin API for graph workloads, with turnkey scaling and low‑latency reads/writes.
Native multi‑model database that combines graph, document, and key‑value data models, with AQL query language and a flexible schema.
High‑performance, distributed native graph database built for real‑time analytics and deep link analysis, featuring GSQL query language.
Open‑source, scalable graph database that runs on top of storage back‑ends like Cassandra, HBase, or BerkeleyDB, and uses Gremlin for queries.
Multi‑model DBMS supporting graph, document, key‑value, and object models, with SQL‑like query language and ACID transactions.
Distributed, native graph database with GraphQL‑compatible query language, built for low‑latency queries at massive scale.
Enterprise‑grade RDF triplestore optimized for semantic graph queries, supporting SPARQL and reasoning capabilities.
High‑performance RDF graph database offering SPARQL 1.1 support, suitable for knowledge‑graph and linked‑data applications.
Graph module for Redis that adds Cypher‑compatible query capabilities on top of Redis' in‑memory speed.
Real‑time, in‑memory graph database with Cypher support, designed for high‑throughput analytics and streaming data.
Enterprise RDF graph database with advanced reasoning, geospatial, and temporal features, used for knowledge‑graph and AI workloads.
Open‑source graph database from Google, written in Go, supporting Gremlin, MQL, and GraphQL query interfaces.
Scalable, distributed native graph database with its own nGQL query language, optimized for large‑scale social and recommendation systems.
Enterprise knowledge graph platform offering RDF storage, SPARQL querying, and AI‑ready data integration.
High‑performance, in‑memory distributed graph processing engine for large‑scale analytics, used internally at Microsoft.
Enterprise‑grade, distributed graph database designed for complex relationship queries across massive data sets.
Hybrid relational‑graph database supporting SQL, SPARQL, and RDF, suitable for linked‑data and data‑virtualization scenarios.
Enterprise knowledge graph platform that combines RDF storage, reasoning, and a unified query engine (SPARQL + GraphQL).
Modern, high‑performance graph database written in C++, offering a Cypher‑compatible query language and built‑in analytics.