Dgraph
The open, complete graph database used at terabyte scale for real-time use cases
About Dgraph
Dgraph is an open-source, distributed graph database with a native GraphQL API and its own query language, DQL. It shards data across a cluster for horizontal scale, supports ACID transactions with consistent replication, and adds vector similarity search for AI workloads. Istari Digital acquired Dgraph from Hypermode in October 2025 and maintains it under the Apache 2.0 license.
Pros & Cons
Pros
- Fully open source under Apache 2.0, including features that used to require an enterprise license
- Native GraphQL API turns a schema into a working graph backend quickly
- Distributed by design, with sharding, replication, and ACID transactions across the cluster
- Actively maintained: frequent v25.x releases through 2026 and a 21k-star GitHub repository
- Vector search lets you combine graph traversal and embedding similarity in one database
Cons
- No managed cloud offering after Dgraph Cloud was retired, so you run and operate the cluster yourself
- Ownership has changed twice (Dgraph Labs to Hypermode in 2023, then Istari Digital in 2025), which raises long-term roadmap questions
- DQL is Dgraph-specific, so skills and queries don't transfer to Cypher or Gremlin databases
- Official support covers Linux amd64 and arm64 only; Mac and Windows are for development via Docker
- Smaller ecosystem and fewer third-party tools than Neo4j
Key Features
Native GraphQL API
Generates a GraphQL API and graph backend directly from a GraphQL schema, with no separate resolver layer to write
DQL query language
GraphQL-inspired query language with recursive traversals, filtering at any depth, aggregation, regex, geo, and full-text search
Distributed sharding
Data is automatically sharded across server groups so the graph scales horizontally to billions of triples
ACID transactions
Distributed ACID transactions with consistent replication and linearizable reads
High availability
Multiple replicas per server group with automatic failover when nodes fail
Vector similarity search
Embedding fields and vector indexes for similarity search inside the graph, aimed at AI and RAG workloads
Backup and restore
Full and incremental backups to local or cloud storage with point-in-time recovery
Access control and encryption
Predicate-level ACLs plus encryption at rest and in transit
RDF and JSON ingestion
Load and mutate data as RDF triples or JSON, with live and bulk loaders for large imports
Ratel UI and observability
Ratel web UI for queries and schema, plus Prometheus and Grafana metrics for cluster health
Pricing
- Apache 2.0 license
- Self-hosted via Docker, Kubernetes, or binaries
- GraphQL API and DQL
- High availability, backups, ACLs, and encryption
- Vector similarity search
- Community support on GitHub and Discuss forum
Best For
GraphQL backends
Teams define a GraphQL schema and get a queryable, scalable graph backend without writing resolvers
Knowledge graphs for AI
Store entities and relationships alongside embeddings to power graph-based retrieval for LLM applications
Recommendation engines
Traverse user, product, and interaction graphs in real time to generate recommendations
Fraud and relationship analysis
Follow multi-hop connections between accounts, devices, and transactions to spot suspicious patterns
Social and network data
Model followers, groups, and interactions where deep relationship queries are the main workload
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