FalkorDB vs LadybugDB
FalkorDB
falkordb.comLadybugDB
ladybugdb.comSide by side
| Figure | FalkorDB | LadybugDB |
|---|---|---|
| Price from | $73/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 1 |
| Spec score · Graph Databases | 9.1 (#2) | 8.2 (#11) |
| Deployment | FalkorDB can be launched locally using Docker or used as a fully managed cloud instance. | Ladybug runs embedded in an application process and does not require managing a separate database server. |
| Integrations | Official materials list Graphiti, LangChain, LlamaIndex, Cognee, g.v(), OpenAI, Gemini, Google Cloud, Azure, Grafana, Docker, Snowflake and Model Context Protocol integrations or ecosystem connections. | The documentation describes integrations with Snowflake and PostgreSQL, and attaching to DuckDB, SQLite, Iceberg, Delta Lake, and Unity Catalog sources. |
| License | The documentation states FalkorDB is licensed under Server Side Public License v1 (SSPLv1). | All source code and precompiled binaries are distributed under the MIT License. |
| Product | FalkorDB is a high-performance graph database for managing complex relationships and supporting AI applications. | Ladybug is an embedded graph database optimized for complex analytical workloads on very large graphs. |
| Query language | FalkorDB supports OpenCypher and adds proprietary query extensions. | It uses a property graph data model and Cypher query language. |
| Support | The documentation directs users to Discord and a forum for questions, ideas, and reports. | The site lists community support and says commercial enterprise support contracts are available. |
Plans
In their own words
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.