Compare the Top Graph Databases as of October 2026

What are Graph Databases?

Graph databases are specialized databases designed to store, manage, and query data that is represented as graphs. Unlike traditional relational databases that use tables to store data, graph databases use nodes, edges, and properties to represent and store data. Nodes represent entities (such as people, products, or locations), edges represent relationships between entities, and properties store information about nodes and edges. Graph databases are particularly well-suited for applications that involve complex relationships and interconnected data, such as social networks, recommendation engines, fraud detection, and network analysis. Compare and read user reviews of the best Graph Databases currently available using the table below. This list is updated regularly.

  • 1
    Redis

    Redis

    Redis Labs

    Redis Labs: home of Redis. Redis Enterprise is the best version of Redis. Go beyond cache; try Redis Enterprise free in the cloud using NoSQL & data caching with the world’s fastest in-memory database. Run Redis at scale, enterprise grade resiliency, massive scalability, ease of management, and operational simplicity. DevOps love Redis in the Cloud. Developers can access enhanced data structures, a variety of modules, and rapid innovation with faster time to market. CIOs love the confidence of working with 99.999% uptime best in class security and expert support from the creators of Redis. Implement relational databases, active-active, geo-distribution, built in conflict distribution for simple and complex data types, & reads/writes in multiple geo regions to the same data set. Redis Enterprise offers flexible deployment options, cloud on-prem, & hybrid. Redis Labs: home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.
    Starting Price: Free
  • 2
    Memgraph

    Memgraph

    Memgraph

    Memgraph is a high-performance, in-memory graph database that powers real-time AI context. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows - delivering sub-millisecond multi-hop traversals with full provenance for any system that needs structured, connected context alongside semantic search. The same architecture that makes Memgraph the context layer for AI also drives real-time graph analytics across fraud detection, network analysis, infrastructure monitoring, and other operational use cases where speed and connectivity matter.
  • Previous
  • You're on page 1
  • Next