SurrealDB vs. Neo4j

SurrealDB 3.0 emerges as a versatile, multi-model database designed for continuously updated, large-scale workloads, distinguishing itself from Neo4j, which is optimized for static, read-heavy graph operations. SurrealDB supports a wide array of data types, including graph, document, vector, and temporal data, within a single engine, allowing for efficient storage and horizontal scalability across distributed nodes. This makes it particularly suitable for AI applications that demand real-time data updates and complex queries. While Neo4j relies on an in-memory page cache for performance, which can degrade under sustained write-heavy workloads, SurrealDB is optimized for concurrent updates and high write throughput. Furthermore, SurrealDB's open-source model offers cost advantages and flexibility, in contrast to Neo4j's proprietary enterprise offerings. The unified platform approach of SurrealDB reduces the need for multiple databases, thereby minimizing operational overhead and enhancing data consistency, making it ideal for live, large-scale production systems.