NebulaGraph
Open-source distributed graph database built for massive-scale GraphRAG and analytics
- Category
- Data Analysis
- Pricing
- Free and open-source (Apache 2.0 core); NebulaGraph Cloud and enterprise offerings are available with usage-based and custom pricing.
- Best for
- NebulaGraph is aimed at data engineering and infrastructure teams that need to model and query extremely large, highly connected datasets, such as social graphs, fraud networks, recommendation systems, or enterprise knowledge graphs.
- Official site
- www.nebula-graph.io
- Last updated
- August 2026
NebulaGraph is an open-source distributed graph database originally built to meet the scale demands of large internet companies needing to model social networks, recommendation systems, and knowledge graphs with billions of nodes and trillions of edges. Its shared-nothing, distributed architecture separates storage and compute, allowing it to scale horizontally across commodity hardware while maintaining low-latency query performance.
With the rise of retrieval-augmented generation, NebulaGraph has positioned itself as infrastructure for GraphRAG: combining graph-structured relationship data with vector search and LLM reasoning to produce more accurate, context-aware AI responses than pure vector-based RAG alone. The project provides integrations and reference architectures for building LLM applications on top of graph data, alongside its own query language, nGQL, which is designed to be approachable for those familiar with Cypher-style graph queries.
The project is open-source (Apache 2.0) with a company, NebulaGraph (formerly vesoft), behind it offering a managed cloud service and enterprise support. This gives adopters a path from free self-hosted deployment to managed infrastructure as their graph workloads grow.
NebulaGraph is aimed at data engineering and infrastructure teams that need to model and query extremely large, highly connected datasets, such as social graphs, fraud networks, recommendation systems, or enterprise knowledge graphs. It's a strong fit for organizations building GraphRAG pipelines that want the relationship-modeling power of a graph database alongside LLM-based retrieval. Teams with smaller graphs or without dedicated infrastructure resources may find lighter-weight or fully managed alternatives easier to adopt.
Key features
Distributed shared-nothing architecture
Separates storage and compute layers so the database can scale horizontally across many machines without a single point of contention.
Massive graph scale
Designed to handle billions of vertices and trillions of edges while maintaining millisecond-level query latency.
nGQL query language
A declarative, Cypher-like query language purpose-built for expressive graph traversal and pattern matching.
GraphRAG support
Provides patterns and integrations for combining graph queries with vector search and LLMs to improve retrieval accuracy in RAG pipelines.
Graph algorithms and visualization
Ships with built-in algorithms (e.g., PageRank, shortest path) and visual tools like NebulaGraph Studio and Explorer for exploring graph data.
High availability
Supports replication and fault tolerance to keep large distributed deployments resilient.
Pricing breakdown
Community (Open Source)
- Full core graph database
- Community support via forums/Slack
- Self-managed deployment and scaling
NebulaGraph Cloud
- Managed hosting and scaling
- Monitoring and backups included
- Reduced operational overhead
Enterprise
- Dedicated support and SLAs
- Advanced security and compliance features
- Professional services for large deployments
Pros and cons
Pros
- Purpose-built distributed architecture makes it one of the few graph databases capable of true web-scale graphs (billions of nodes/trillions of edges)
- Open-source licensing gives teams full control and no vendor lock-in for the core database
- Growing GraphRAG ecosystem makes it a relevant choice for teams building knowledge-graph-augmented LLM applications
- Active open-source community with regular releases and enterprise backing for support
- Flexible deployment options from self-hosted to fully managed cloud
Cons
- Operating a distributed graph database cluster at scale requires meaningful DevOps/infrastructure expertise
- nGQL, while Cypher-like, still requires ramp-up time for teams unfamiliar with graph query languages
- Documentation and third-party tooling, while improving, lag behind more established players like Neo4j
- Smaller talent pool of engineers experienced specifically with NebulaGraph compared to more mainstream databases
- GraphRAG tooling and best practices are still evolving industry-wide, so integration work may be needed for production use
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Compare →Frequently asked questions
Is NebulaGraph free to use?
Yes, the core NebulaGraph database is open-source under Apache 2.0 and free to self-host; a managed cloud service and enterprise tier are also available.
What is GraphRAG and how does NebulaGraph support it?
GraphRAG combines graph-structured data with retrieval-augmented generation; NebulaGraph provides integrations and reference patterns for using its graph queries alongside LLMs to improve retrieval accuracy.
What query language does NebulaGraph use?
NebulaGraph uses nGQL, a declarative graph query language with syntax similar to Cypher.
How large can a NebulaGraph deployment scale?
It is designed to handle billions of vertices and trillions of edges through its distributed, shared-nothing architecture.
How does NebulaGraph compare to Neo4j?
NebulaGraph focuses on distributed, horizontally scalable architecture for very large graphs, while Neo4j has a more mature ecosystem and tooling; the right choice depends on scale requirements and team familiarity.
Do I need a distributed systems background to run NebulaGraph?
Self-hosting a production cluster benefits from infrastructure/DevOps experience; teams without that can use NebulaGraph Cloud to offload operational management.
Ready to try NebulaGraph?
Head to the official site to explore pricing and start a free trial where available.
Visit NebulaGraph →