Apache HugeGraph vs LadybugDB
Apache HugeGraph
hugegraph.apache.orgLadybugDB
ladybugdb.comSide by side
| Figure | Apache HugeGraph | LadybugDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Graph Databases | 8.7 (#5) | 8.2 (#11) |
| Deployment | The Server can be deployed with Docker, a binary archive, a source build, or a legacy one-click deployment tool. | Ladybug runs embedded in an application process and does not require managing a separate database server. |
| Integrations | The project documents integration with Spark and SeaTunnel and compatibility with Hadoop and Flink. | The documentation describes integrations with Snowflake and PostgreSQL, and attaching to DuckDB, SQLite, Iceberg, Delta Lake, and Unity Catalog sources. |
| License | The HugeGraph-AI documentation identifies Apache License 2.0. | All source code and precompiled binaries are distributed under the MIT License. |
| Product | Apache HugeGraph is a graph system covering graph database, graph computing, and graph AI. | Ladybug is an embedded graph database optimized for complex analytical workloads on very large graphs. |
| Support | The project directs users to its Apache community, GitHub issues and discussions, developer mailing list, and Slack channel. | The site lists community support and says commercial enterprise support contracts are available. |
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.
LadybugDB
LadybugDB is an embedded columnar graph database for analytical workloads and agentic applications. It uses Cypher with a structured property graph model and can run on disk or in memory; in-memory data is lost when the process ends. Its query engine uses columnar disk storage, vectorized and factorized processing, multi-core parallelism, and join algorithms. Transactions are atomic, durable, and serializable. The project distributes source code and precompiled binaries under the MIT License, which permits commercial and proprietary applications. Imports support Parquet, CSV, JSON, NumPy, Pandas or Polars DataFrames, and PyArrow Tables. Official client APIs are listed for Python, Node.js, Java, Rust, Go, Swift, C, and C++, alongside a command-line interface. Ladybug Explorer provides a browser-based way to query and visualize a database, and the MCP Server exposes a database to LLMs and agents. Extensions cover sources and systems including Azure storage, Delta Lake, DuckDB, Iceberg, PostgreSQL, and vector similarity search. Concurrent access permits one read-write Database object or multiple read-only objects; multiple processes needing writes should use an API server pattern.