Open-source TigerGraph alternatives

Dgraph is a distributed database that stores data as a graph while exposing it through GraphQL. It scales horizontally across machines and offers ACID transactions, consistent replication and linearizable reads, so a cluster behaves predictably even as it grows.

The project was built from scratch around the way graph data is laid out on disk, which it says reduces disk seeks and network calls in a cluster and improves query performance and throughput. The stated aim is production-level scale with latency low enough to answer live user queries across terabytes of structured data. Clients send queries in GraphQL syntax and receive JSON or Protocol Buffers over gRPC and HTTP. Topics point to uses such as knowledge graphs.

Dgraph is written in Go and licensed under Apache-2.0. The README describes it as production-ready at version 25 and in use at multiple large companies, and it officially supports Linux on amd64 and arm64 architectures. It is a fit for teams modeling highly connected data, such as social graphs, recommendations or knowledge bases, who also want a GraphQL API.

Neo4j is a graph database written in Java. Instead of tables, it stores data as nodes and relationships, which makes it a good fit for problems where the connections between records matter, such as social networks, fraud detection, recommendations and knowledge graphs. Data is queried with Cypher, the graph query language associated with the project.

The repository contains the open-source community edition of the database engine. Topics tag it as a NoSQL graph database and graphdb, and the project's tagline is simply that graphs are for everyone. The repository's homepage could not be fetched while preparing this entry, so details here are limited to the repository description and widely known facts about the product.

The community edition is licensed under GPL-3.0, while commercial editions with additional enterprise features and a managed cloud service are sold by Neo4j, Inc. You can run the database on your own servers or use the hosted service. It is aimed at developers and data teams modeling relationship-heavy data.

ArangoDB is a multi-model database that stores documents, graphs and key-value data in a single engine. Instead of running separate systems for each data model, applications query connected data through one SQL-like language, with JavaScript extensions available for custom logic.

Every node in a graph is a JSON document, so existing document data can be imported and relationships can be queried across multiple levels of joins. ArangoSearch, an integrated search engine, adds text search and ranking. For scale and resilience the database can shard data horizontally across machines and replicate it to cluster nodes with automatic failover.

ArangoDB can be installed from a download or started in Docker, and runs on-premises or in any cloud. The Arango Managed Platform (AMP) creates deployments on major cloud providers and can be tried for free. A free Community Edition and a commercial Enterprise Edition without a dataset size limit are offered; the repository lists its license simply as Other, so check the terms before production use.

NebulaGraph, shown here as Nebula, is an open-source graph database designed to handle large volumes of connected data with low latency. It is distributed and scales out horizontally, and it is used for areas such as social networks, recommendation systems, knowledge graphs, security and fraud scenarios and AI.

Its architecture is symmetrically distributed with storage and computing separated, and it uses the Raft protocol for strong data consistency. Queries are written in a language compatible with openCypher, and role-based access control adds security. The project also provides different graph analytics algorithms, a set of tools for managing and monitoring a deployment, and topics that reference GraphRAG and knowledge graphs for AI.

NebulaGraph is written in C++ under the Apache 2.0 licence, so the source can be downloaded, modified and deployed as a back-end service. It can be built from source or tried in the cloud on AWS, and there is documentation, an FAQ and community discussions. It is part of the CNCF database landscape.

FalkorDB is a graph database built for low-latency queries, with a particular focus on supplying knowledge graphs to large language models in GraphRAG setups. It stores data as property graphs and represents connections with sparse adjacency matrices, which the authors say keeps storage compact and traversal fast.

Instead of walking edges one at a time, queries are executed through linear algebra operations built on GraphBLAS. The project lists generative AI applications, agent memory, fraud detection and cloud security as target uses, and it is multi-tenant, so many separate graphs can share one deployment. Graphs are queried with Cypher-style statements, and repository topics also point to developer tooling and real-time use.

The code is published on GitHub under a license that GitHub classifies as 'Other', so check the repository's license terms before commercial use. You can run FalkorDB yourself, commonly as a container, or use the vendor's hosted cloud database service.

GraphScope is a unified distributed graph computing platform from Alibaba. It offers a single environment where many kinds of graph workloads run across a computer cluster, controlled from Python, so data scientists do not have to stitch several graph engines together.

It combines several Alibaba technologies: GRAPE for analytics, MaxGraph for interactive queries and Graph-Learn for graph neural network computation. The project also points to GraphScope Flex, a modular evolution described as LEGO-inspired. Repository topics list Gremlin, graph analytics, big data and data science. The engine is written in C++.

GraphScope is licensed under Apache-2.0 and is deployed on your own cluster, for example on Kubernetes. It suits teams working with very large graphs, such as social networks, knowledge graphs and fraud detection, who want analytics and machine learning in one system.