Top 5 Dgraph Alternatives of 2025

Choosing a graph database is often about balancing performance, scalability, and integration with existing systems. Dgraph has attracted attention for its native graph engine and distributed architecture, but it's not the only option, especially as teams reevaluate long-term support and ecosystem fit. Whether you're building a knowledge graph, analyzing network relationships, or layering graph analytics on top of relational data, it's worth considering what other platforms have to offer. This article reviews five alternatives to Dgraph in 2025—PuppyGraph, Neo4j, Amazon Neptune, ArangoDB, and JanusGraph, and highlights the unique strengths of each.

What is Dgraph?

Dgraph is an open-source, distributed graph database designed to handle complex queries on connected data at scale. It stores data in a native graph format and initially introduced its own query language, DQL (Dgraph Query Language), later adding full support for standard GraphQL. Dgraph is built to support high-throughput transactional workloads, making it suitable for use cases like social networking, recommendation systems, and access control graphs.

One of Dgraph’s defining features is its emphasis on performance and scalability. It partitions data across multiple nodes and uses the RAFT consensus algorithm to ensure consistency and high availability. This allows it to scale horizontally while maintaining strong transactional guarantees.

Dgraph also provides a built-in GraphQL API layer, which simplifies integration for developers building modern applications. It includes features such as full-text search, geospatial queries, and role-based access control.

In 2023, Dgraph was acquired by Hypermode, a company building developer infrastructure for AI applications. The project continues to evolve under open-source development, with updates aimed at improving performance and usability.

Introduction to Dgraph Architecture

Dgraph uses a distributed architecture composed of three main components: Zero nodes, Alpha nodes, and optional GraphQL servers. This design supports horizontal scaling, fault tolerance, and transactional guarantees.

• Zero Nodes handle cluster coordination. They manage metadata, distribute tablets (data shards), and ensure consensus through the RAFT protocol. They also perform leader election and keep track of which Alpha nodes are responsible for which tablets.
  
  
• Alpha Nodes are the core data-serving components. They store graph data, execute queries, process mutations, and manage transactions. Alphas periodically communicate with Zeros to update membership and tablet assignments.
  
  
• GraphQL Layer translates GraphQL queries into internal operations. Dgraph natively supports standard GraphQL in addition to DQL, offering flexibility in how developers build APIs and interact with the database.
  

For its persistent storage, Dgraph uses Badger, an embeddable key-value store developed by the Dgraph team. Badger is written in Go and optimized for performance with features like LSM trees, value log separation, and efficient compaction.

Data is automatically partitioned into tablets and distributed across Alpha nodes. Each Alpha node maintains a write-ahead log and supports multi-version concurrency control (MVCC), which enables ACID transactions and snapshot isolation.

Dgraph’s architecture aims to combine native graph storage with distributed system design, enabling it to serve both transactional and analytical graph workloads at scale.

Why Explore Dgraph Alternatives?

While Dgraph offers a native graph engine with strong performance and distributed capabilities, some teams may find that it doesn't align perfectly with their broader data architecture or tooling preferences. The graph database landscape is constantly evolving, and other solutions may better align with specific architectural needs, integration preferences, or cost models.

One area of consideration is its proprietary aspects. Although Dgraph is open source, its native query language DQL is unique to the platform. Teams already familiar with more widely adopted graph query languages like Cypher or Gremlin may face a learning curve when adopting DQL. And while Dgraph can automatically generate a GraphQL API, some organizations may prefer platforms that integrate more directly with existing data ecosystems without requiring full data ingestion into a new system.

Performance and scalability are also important. While Dgraph is optimized for distributed workloads, real-world performance varies depending on data models and usage patterns. Organizations with requirements for multi-model data, virtual graphs, zero-ETL architectures, or specific community and support models may find other platforms more aligned with their priorities. Pricing and licensing models also vary widely, from open-source frameworks to fully managed cloud services.

Top 5 Dgraph Alternatives

A number of graph systems offer strong alternatives to Dgraph, each with different architectural models, query capabilities, and integration strategies. Some focus on native graph storage, while others enable graph analytics directly on relational or multi-model data. This section highlights five notable alternatives: PuppyGraph, Neo4j, Amazon Neptune, ArangoDB, and JanusGraph.

PuppyGraph

PuppyGraph is a zero‑ETL graph analytics engine that enables graph querying directly on relational and lakehouse data without ingestion. Users define a graph schema via a JSON metadata file that maps tables or views (from multiple data sources) to vertices and edges.

PuppyGraph supports both openCypher and Gremlin, and connects with a wide range of storage systems including databases like PostgreSQL, MySQL, and DuckDB, as well as lakehouse formats such as Apache Iceberg and Delta Lake. It also supports cloud data warehouses like BigQuery and Redshift, and integrates with catalog services such as Hive Metastore and AWS Glue.

Because PuppyGraph operates on live data where it resides, it avoids data duplication and ensures queries always reflect the most current state. It scales to petabyte‑level datasets, auto‑sharding compute across sources, and handles multi‑hop, complex queries in seconds—even on very large graphs.

Key advantages include:

• Zero‑ETL architecture: no data movement or duplication
  
  
• Flexible schema-based mapping across diverse data sources
  
  
• Support for openCypher and Gremlin query languages
  
  
• Native integration with relational databases, cloud warehouses, and lakehouse formats
  

Neo4j

Neo4j is one of the most widely adopted graph database platforms, known for its native graph storage engine and long-term investment in graph query language development. Cypher, the declarative language widely used for querying property graphs, was originally invented by Neo4j and has since influenced the development of openCypher and GQL standards.

Neo4j provides both self-managed and fully managed cloud offerings (via Neo4j Aura), and is backed by a mature ecosystem of drivers, visualization tools, and integrations. It excels in use cases such as knowledge graphs, fraud detection, recommendation systems, and supply chain analysis.

Its developer-friendly tooling, strong documentation, and active community make it a popular choice for teams starting new graph projects or scaling existing ones. The platform also supports graph data science libraries, real-time analytics, and clustering for high availability.

Key advantages include:

• Native graph engine with Cypher support
  
  
• Originator of the Cypher query language
  
  
• Rich ecosystem of developer tools and integrations
  
  
• Available as both self-managed and fully managed service

Amazon Neptune

Amazon Neptune is a fully managed graph database service from AWS that supports three query languages: openCypher, Gremlin, and SPARQL. It allows users to choose between the property graph model and RDF, depending on their use case. With openCypher now generally available, developers who are familiar with Cypher-based tools can query Neptune with minimal adjustment.

As a native AWS service, Neptune benefits from tight integration with the broader AWS ecosystem. It supports features like IAM-based authentication, encryption at rest, automated backups to S3, and monitoring with CloudWatch. Deployment is handled through the AWS Management Console or CLI, and users can provision replicas across multiple availability zones for high availability and fault tolerance.

Neptune is typically used for applications like knowledge graphs, fraud detection, recommendation engines, and network analysis, especially when organizations are already committed to AWS infrastructure.

Key advantages include:

• Native support for openCypher, Gremlin, and SPARQL
  
  
• Fully managed deployment with minimal operational overhead
  
  
• Seamless integration with AWS security, monitoring, and storage services
  
  
• High availability via multi-AZ deployments and read replicas

ArangoDB

ArangoDB is a multi-model database that supports graph, document, and key-value data models within a single unified engine. This flexibility allows developers to use the most appropriate model for each part of their application without maintaining separate databases.

For graph use cases, ArangoDB provides native support for property graphs and exposes a custom query language called AQL (Arango Query Language), which supports graph traversals, joins, and filtering across collections. It also includes features like ACID transactions, horizontal scalability with cluster deployments, and integration with graph visualization tools.

ArangoDB’s multi-model design makes it a strong choice for applications that combine graph structures with document-based storage or search. It supports both single-node and distributed setups, with managed cloud offerings available through ArangoGraph.

Key advantages include:

• Multi-model: graph, document, and key-value in one engine
  
  
• Native support for graph queries via AQL
  
  
• ACID transactions and horizontal scalability
  
  
• Suitable for hybrid applications and consolidated data platforms
  

JanusGraph

JanusGraph is an open-source, distributed graph database optimized for large-scale analytics over property graphs. It is built on top of existing storage and indexing systems, such as Apache Cassandra, HBase, Google Bigtable, and ScyllaDB, and integrates with indexing backends like Elasticsearch and Apache Solr.

JanusGraph supports the Gremlin query language via the Apache TinkerPop stack, allowing for expressive traversals and graph pattern queries. It is designed to scale horizontally and can integrate with big data frameworks like Apache Spark and Hadoop for batch analytics.

This pluggable architecture makes JanusGraph a flexible choice for organizations with existing big data infrastructure who want to add graph capabilities without introducing a separate storage layer. It is commonly used in applications such as identity graphs, fraud detection, entity resolution, and network security analysis.

Key advantages include:

• Open-source and storage-agnostic architecture
  
  
• Gremlin support via Apache TinkerPop
  
  
• Compatible with scalable storage and indexing backends
  
  
• Integrates with Hadoop, Spark and big data ecosystems

Conclusion

Dgraph introduced a compelling approach to distributed graph processing with native storage and GraphQL support. But as the graph database landscape continues to expand, teams are evaluating alternatives that offer different advantages in terms of data integration, deployment models, scalability, and ecosystem maturity.

For organizations seeking to query existing relational or lakehouse data without ETL, PuppyGraph offers a unique zero-copy architecture with support for openCypher and Gremlin. Neo4j remains a strong choice for those prioritizing a mature ecosystem and native Cypher support. Amazon Neptune is ideal for AWS users who want managed graph infrastructure with support for openCypher, Gremlin, and SPARQL. ArangoDB stands out for multi-model flexibility, and JanusGraph fits well in big data environments that already use scalable storage backends.

The best choice depends on your specific needs—whether it's integration with existing data sources, language compatibility, deployment preferences, or scalability requirements. Exploring these options can help ensure your graph solution is a good fit not just today, but for the long term.

If you're looking to bring graph analytics to your existing data without moving it, try the forever-free PuppyGraph Developer Edition or book a demo with our team and see how quickly you can start querying your existing data as a graph.