What’s the Best Database for Knowledge Graphs? Pros & Cons

Knowledge graphs, essential for modern artificial intelligence applications, organize data by connecting related concepts, unlike traditional databases that store information in isolated rows and columns. This interconnected structure helps large language models (LLMs) improve accuracy and reduce misinformation by providing context and explainability. The text discusses the limitations of using standard relational databases for AI tasks and highlights the need for specialized graph databases. It evaluates various graph databases, emphasizing FalkorDB's low-latency performance due to its in-memory operations and Sparse Matrix Algebra, Neo4j's limitations due to Java-based architecture, ArangoDB's multi-model compromises, and Amazon Neptune's cost and latency challenges. The importance of choosing the right database for AI workflows is underscored, taking into account factors like scalability, real-time performance, ecosystem integration, and deployment flexibility.