Dgraph usages are quite wide-spread. It is being used for typical graph cases, like recommendation engines, real-time fraud detection, adtech, fintech uses, etc.
The design is very well suited for just building apps (scalable, flexible schema) as well, given the early choices to use GraphQL (modified) as the query lang and JSON as the response. So, we see even Fortune 500 companies using Dgraph to build mobile apps.
Most open source users use the simplest 2-node cluster, but we easily see enterprise customers use 6-node (High Availability) cluster or 12-node cluster (HA + Sharding). Given synchronous replication, query throughput can scale out linearly as you add more replicas/machines (each replica can reply without worrying about issues with typical eventual consistency models. Dgraph provides linearizable reads).
