LadybugDB vs RocksDB
LadybugDB
ladybugdb.comRocksDB
rocksdb.orgSide by side
| Figure | LadybugDB | RocksDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | Yes |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Embedded Databases | 8.4 (#7) | 8.0 (#11) |
| Integrations | The documentation describes integrations with Snowflake and PostgreSQL, and attaching to DuckDB, SQLite, Iceberg, Delta Lake, and Unity Catalog sources. | The project documents a MyRocks database storage engine as one example of using RocksDB. |
| Platforms | The CLI and C/C++ APIs have precompiled binaries for macOS, Linux, and Windows; Swift also supports iOS, and Java supports Android ARMv8-A. | Build instructions cover Linux, macOS, FreeBSD, OpenBSD, iOS, Windows, AIX, and Solaris Sparc. |
| Product | Ladybug is an embedded graph database optimized for complex analytical workloads on very large graphs. | RocksDB is an embeddable persistent key-value store for fast storage. |
| Support | The site lists community support and says commercial enterprise support contracts are available. | The project directs users to its Google Group or Facebook Group for general questions and reserves GitHub issues for bug reports. |
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.
RocksDB
RocksDB is an embeddable, persistent key-value store for applications that need fast storage. It stores keys and values as arbitrary byte arrays, with ordering determined by a comparator chosen by the user. Its log-structured merge design allows configuration of tradeoffs among write, read, and space amplification, and the project describes it as optimized for low-latency storage on flash drives and high-speed disks. The C++ library supports opening and closing databases, reads and writes, merging, and compaction filters. Described uses include database storage engines, application data caches, and embedded workloads. Multi-threaded compactions support databases holding multiple terabytes; the project describes an audience of application servers storing up to a few terabytes on local or remote systems. RocksDB also has a Java wrapper, RocksJava, with artifacts published through Maven. It is free and open source under either GPLv2 or Apache 2.0, at the user's choice. Building requires GCC 11 or later or Clang 10 or later for C++20 support; values must be smaller than 4 GB.