SurrealDB 3.x by the numbers

SurrealDB 3.x demonstrates significant improvements over its predecessors, showcasing enhanced performance in CRUD throughput, batch operations, and indexed and non-indexed full-table scans, with particular advancements seen in its query planner and storage engine. In benchmark comparisons against databases like PostgreSQL, MongoDB, and Neo4j, SurrealDB consistently outperforms in write operations and complex queries, while still maintaining competitive read speeds, illustrating its capabilities as a multi-model, transactional database. SurrealDB's design uniquely accommodates diverse data models such as relational, document, graph, vector, and full-text search within a single query language, SurrealQL, making it ideal for AI agents that require multi-model memory in a transactionally consistent store. Future developments aim to close the performance gaps still present in large batch operations and indexed predicate filtering, while planned benchmarks will further explore SurrealDB's performance in distributed settings and additional capabilities like vector search and full-text retrieval.