This guide helps organizations choose the right graph database by comparing analytical (OLAP) and transactional (OLTP) use cases, such as fraud detection, identity resolution, and knowledge graphs. Key criteria include scalability, low-latency performance, cost, developer experience, and operational flexibility. Native graph databases offer speed for relationship-centric queries, while multi-model databases provide versatility. It compares leading vendors—Neo4j, Aerospike, TigerGraph, and Amazon Neptune—based on support, performance, and cost. As AI/ML adoption grows, graph databases must support large-scale, real-time workloads and seamless integration with modern data pipelines.

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