Neo4j, a native graph database, outperforms non-native graph databases in various benchmarks and scenarios. This is due to its efficient data structures and traversal algorithms, which are optimized for graph workloads. The database's storage engine and pointer chasing implementation also contribute to its performance advantages. Non-native graph databases, on the other hand, suffer from global indexes and expensive I/O operations, leading to slower traversals and higher costs. Recent advancements in non-volatile RAM and co-processors have opened up opportunities for Neo4j to further optimize its performance and scalability. Johan, Neo4j's CTO, has led benchmarking efforts that demonstrate the database's capabilities, including handling large graphs and high-performance queries. The author notes that Neo4j's native graph technology allows it to scale efficiently on modest hardware, making it an attractive choice for graph workloads. As hardware trends continue to evolve, Neo4j is well-positioned to take advantage of these advancements and ensure its efficiency and performance only improve over time.