Apache HugeGraph vs GraphScope NeuG
Apache HugeGraph
hugegraph.apache.org
GraphScope NeuG
graphscope.ioSide by side
| Figure | Apache HugeGraph | GraphScope NeuG |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Graph Databases | 8.7 (#5) | 7.5 (#24) |
| Deployment | The Server can be deployed with Docker, a binary archive, a source build, or a legacy one-click deployment tool. | NeuG can run embedded in an application or expose the same engine as a service. |
| Integrations | The project documents integration with Spark and SeaTunnel and compatibility with Hadoop and Flink. | NeuG documents data exchange with Apache Arrow, Parquet, S3, and Alibaba Cloud OSS. |
| License | The HugeGraph-AI documentation identifies Apache License 2.0. | NeuG is released under the Apache License 2.0. |
| Product | Apache HugeGraph is a graph system covering graph database, graph computing, and graph AI. | NeuG is a graph-native transactional database that runs embedded in an application or behind a service. |
| Security | User authentication is disabled by default; the project advises enabling authentication and authorization, maintaining an IP allowlist, and avoiding direct public exposure of query endpoints in production. | The Web UI runs on localhost by default, and the documentation recommends firewall rules and a reverse proxy for public or production access. |
| Support | The project directs users to its Apache community, GitHub issues and discussions, developer mailing list, and Slack channel. | The installation guide directs users to GitHub issues and language-specific reference documentation for help. |
Plans
In their own words
Apache HugeGraph
Apache HugeGraph is an open-source graph system combining a graph database, graph computing, and graph AI. It supports Gremlin and Cypher/OpenCypher queries, a REST API, ACID transactions, graph algorithms, and vector search. HugeGraph-AI adds Python clients, graph machine-learning tools, and LLM tools for knowledge-graph construction and GraphRAG applications. Storage options include RocksDB for standalone use, HStore for distributed storage, and HBase. The project documents integrations with Spark and SeaTunnel and compatibility with Hadoop and Flink. Its toolchain provides graph loading, visualization, backup, restore, and migration; Hubble is a web interface for visualization and management. Documentation covers standalone deployments up to 2 TB and distributed deployments up to 1 PB. The project describes high-speed imports and millisecond-level real-time queries for hundreds of billions of graph data. It runs on Linux and macOS and can be self-hosted. The open-source plan costs nothing. Java 11 is recommended and required from version 1.5.0.
GraphScope NeuG
GraphScope NeuG has no write-up on Specifiction yet; its figures are above.