ArangoDB
The native multi-model database for graphs, documents, and integrated AI vector search.
The native GraphQL distributed graph database built for high-performance AI and knowledge graphs.
Dgraph is a horizontally scalable, distributed graph database designed to provide low-latency, high-throughput queries for complex, interconnected data. Unlike traditional databases that struggle with deep joins, Dgraph's native graph engine executes multi-hop traversals with constant-time performance. Built in Go, it leverages a unique architecture where data is sharded and replicated across a cluster, utilizing the Raft consensus algorithm for ACID transactions. In the 2026 landscape, Dgraph has solidified its position as a premier backend for AI-driven applications by integrating native vector search capabilities directly into its graph schema. This allows developers to perform hybrid searches—combining semantic similarity with structural graph relationships—within a single query. Its native GraphQL support eliminates the need for complex ORMs, automatically generating a high-performance API from a simple schema definition. Whether powering real-time recommendation engines, sophisticated fraud detection systems, or enterprise-grade knowledge graphs for RAG (Retrieval-Augmented Generation), Dgraph provides the scalability and developer experience required for modern, data-intensive AI solutions.
Auto-generates a GraphQL API based on the graph schema without requiring separate middleware or resolvers.
The native multi-model database for graphs, documents, and integrated AI vector search.
Unlock the biological blueprint of your professional network with AI-driven relationship intelligence.
The World's Leading Graph Database for Knowledge Graphs and GraphRAG-powered AI.
The scalable, open-source graph database for massive datasets and complex relationship mapping.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses a timestamp-based concurrency control to ensure ACID compliance across a distributed cluster.
Integrates HNSW and Flat vector indexing directly into the graph nodes for RAG applications.
Shards data automatically and allows for the addition of nodes without downtime.
A powerful, custom query language based on GraphQL for complex graph traversals not possible in standard GraphQL.
Serverless JavaScript functions that can be triggered on database events or custom GraphQL queries.
Streams data changes to external systems like Kafka or S3 in real-time.
Identifying complex circular money laundering patterns that involve dozens of hop-traversals.
Registry Updated:2/7/2026
Generating real-time 'users who bought this also liked' lists based on live session data.
Mapping the relationships between malware samples, domains, and attack vectors.