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Benchmarking the Mainstream Open Source Distributed Graph Databases at Meituan: NebulaGraph vs Dgraph vs JanusGraph

Graph Database vs Relational Database: What to Choose?

Difference Between Relational and Non Relational Database

Use cases of graph databases in real-time recommendation

![https://www-cdn.nebula-graph.io/blogs/brochure%20202602-1.png](https://www-cdn.nebula-graph.io/blogs/brochure%20202602-1.png)

![https://www-cdn.nebula-graph.io/blogs/brochure%20202602-2.png](https://www-cdn.nebula-graph.io/blogs/brochure%20202602-2.png)

>This blog post is based on insights shared by Zhechao Yang, VP of Product at NebulaGraph, exploring the critical intersection of graph databases and AI. It synthesizes key industry challenges and technological evolutions, focusing on practical applications and future trends.

Large language models (LLMs) promise to revolutionize industries, but a significant gap remains between this potential and their practical, scaled use in enterprises. Conventional Retrieval-Augmented Generation (RAG) systems often fall short because they fragment complex business knowledge, severing the deep semantic relationships and scattered context that are crucial for accurate understanding.

[Fusion GraphRAG](https://www.nebula-graph.io/posts/Fusion_GraphRAG_The_Next_Evolution_in_Enterprise_Knowledge_Intelligence), pioneered by the NebulaGraph team, is the industry's first full-chain enhancement of RAG built upon a native graph foundation. It moves beyond disparate tools to intelligently fuse knowledge graph technology, document structure, and semantic mapping into a single, cohesive framework. This core advancement, engineered and powered by the high-performance NebulaGraph database, provides LLMs with interconnected, context-rich intelligence that reflects real-world business logic, transforming scattered data into reliable enterprise AI.

# Why LLMs Struggle to Penetrate Core Industries
Despite massive investments, the widespread deployment of LLMs in mission-critical processes faces sobering hurdles. The root causes often boil down to two fundamental issues amplified by traditional data handling:

## The Hallucination Problem
At their core, LLMs are probabilistic prediction engines. They are designed to generate plausible-sounding text, not to act as deterministic databases of facts. This inherent characteristic makes their output unreliable in scenarios demanding high accuracy, auditability, and trust—precisely the requirements of finance, healthcare, logistics, and telecommunications.

## The Data Silo Problem
The most powerful general-purpose LLM is limited by the data it was trained on. Enterprise knowledge is inherently connected, but standard document processing severs these links. When content is broken into isolated chunks for retrieval, critical semantic relationships that span different sections or documents are lost. This leaves LLMs trying to answer complex questions with disconnected pieces of information, leading to incomplete or incoherent responses.

This divide creates a pressing need for a framework that grounds LLMs in verifiable, structured, and domain-specific knowledge.

# GraphRAG: A Paradigm Shift from Retrieving Answers to Reasoning Over Facts
The recent success of "second-brain" AI assistants points the way forward. Their effectiveness hinges on a technique called Retrieval-Augmented Generation (RAG), which enhances an LLM's response by pulling in relevant information from an external knowledge source. Think of it as shifting the LLM from relying solely on memory to cross-referencing facts from a trusted library.

![https://www-cdn.nebula-graph.io/blogs/RAG.png](https://www-cdn.nebula-graph.io/blogs/RAG.png)

Currently, three primary indexing methods power RAG systems:
* **Keyword/Full-Text Indexing:** Relies on literal string matching. It's fast but lacks semantic understanding.
* **Vector Indexing:** Enables semantic search by mapping text to numerical vectors. It excels at finding conceptually similar content, powering recommendations, and Q&A.
* **Graph Indexing:** The core is uncovering and traversing relationships. It preserves the rich, interconnected context within data—how entities link, influence, and relate to one another—preventing information from being reduced to isolated chunks.

**GraphRAG represents a revolutionary upgrade to traditional RAG.** It deeply integrates knowledge graphs and graph technology into the LLM architecture. Instead of retrieving isolated snippets, GraphRAG allows the LLM to reason over a network of facts. This dramatically improves contextual relevance, enables multi-hop reasoning, and provides inherent explainability, as every conclusion can be traced back through a path of nodes and relationships.

![https://www-cdn.nebula-graph.io/blogs/RAG%20vs.%20GraphRAG.png](https://www-cdn.nebula-graph.io/blogs/RAG%20vs.%20GraphRAG.png)

## The Challenges of GraphRAG
Despite its clear advantages, implementing GraphRAG has been prohibitive for many teams, facing two major barriers.

* **High Technical Threshold** 

Building a knowledge graph traditionally requires a suite of NLP expertise—named entity recognition, relationship extraction, and entity linking—along with significant volumes of labeled data and model fine-tuning.
* **High Operational Cost**

The process of constructing, maintaining, and querying a graph for RAG was historically complex and resource-intensive, often requiring specialized infrastructure and expertise, making it orders of magnitude more costly than simpler vector-based approaches.

# Fusion GraphRAG: The NebulaGraph Engineered Framework for Enterprise Intelligence
Building on the [GraphRAG foundation](https://www.nebula-graph.io/posts/graph-RAG), **Fusion GraphRAG is our strategic framework designed to overcome the industry's two biggest adoption barriers: high technical complexity and inconsistent performance.**

## Unleashing Superior, Measurable Performance
Unlike traditional methods that treat different retrieval techniques as separate components, Fusion GraphRAG is architected from the ground up within NebulaGraph to create a synergistic system. This deep integration yields demonstrably superior results:

* **Benchmark-Beating Accuracy:** On open-source KG-RAG datasets focused on complex multi-hop questions, our framework achieves **over a 10-percentage-point improvement in both recall rate and answer correctness** compared to leading state-of-the-art (SOTA) methods. This quantifiable lead underscores its ability to handle intricate queries.
* **Enterprise-Grade Reliability:** In real-world deployments, the performance is even more striking. For instance, in a proof-of-concept using financial regulatory documents, a system built with NebulaGraph's Fusion GraphRAG achieved a **95% correctness rate**, setting a new standard for reliable, mission-critical AI applications.

## Industrializing Practical Implementation with NebulaGraph
We've engineered Fusion GraphRAG to move from a research concept to a production-ready tool, directly addressing the high technical and operational costs that have hindered GraphRAG adoption.
* **Democratizing Knowledge Graph Creation:** Built into the NebulaGraph ecosystem, our framework automates the complex pipeline of entity extraction, relationship mapping, and graph construction. What traditionally took weeks of data science effort can now be accomplished in hours, empowering domain experts to build and manage sophisticated knowledge graphs without deep NLP expertise.
* **End-to-End Automation:** From data ingestion to query serving, Fusion GraphRAG leverages NebulaGraph's native performance and scalability. This integrated toolchain eliminates the need to stitch together disparate systems, resulting in **reported efficiency gains of 5–10x and significant cost savings** for our early adopters.

![https://www-cdn.nebula-graph.io/blogs/Fusion%20GraphRAG.png](https://www-cdn.nebula-graph.io/blogs/Fusion%20GraphRAG.png)

# Real-World Impact: From Post-Mortem to Proactive Intelligence
Consider the challenge faced by a major telecommunications provider. Network operators were overwhelmed by massive streams of alerts, forcing experts to manually sift through thousands of log lines to diagnose an incident—a slow and error-prone process.

A graph-powered solution transformed this by connecting work orders and alerts into a temporal knowledge graph. By applying graph algorithms to this interconnected model, the system could visually trace relationships and automatically pinpoint the most probable root cause.

The results were transformative:
* **Positioning Accuracy** improved to 85%.
* **Mean Time to Resolution (MTTR)** dropped from an average of 60 minutes to just 5 minutes—a **12x efficiency gain.**

This methodology, applicable to any domain with complex event logs (finance, IT, manufacturing), highlights the shift Fusion GraphRAG enables: from reactive, post-mortem analysis to proactive, relationship-driven intelligence.

>For those interested, you can refer to similar cases, such as [*20-Second Root Cause Identification: BOSS Zhipin Builds Intelligent Operations and Maintenance Based on NebulaGraph*](https://www.nebula-graph.io/posts/use_case_boss_zhipin)

# Conclusion: Building the Enterprise Knowledge Core with NebulaGraph
The future of enterprise AI lies in intelligent systems that understand how knowledge connects. **Fusion GraphRAG** is not just a concept but a core, productive innovation from NebulaGraph, designed to bridge the final gap between LLM potential and enterprise reality.

**As the industry's most scalable and performant graph database, NebulaGraph provides the essential, high-performance infrastructure that makes Fusion GraphRAG possible.** This combination transforms scattered enterprise data into a connected, dynamic "Enterprise Knowledge Core." This moves applications beyond basic retrieval toward reliable, contextual, and proactive decision-making—finally bridging the gap between the LLMs and the rigorous demands of enterprise AI.

Fusion GraphRAG bridges the gap between LLMs and enterprise AI by fusing knowledge graphs with advanced retrieval. It overcomes traditional RAG limitations, enabling precise, context-aware intelligence for reliable business applications.

How NebulaGraph Fusion GraphRAG Bridges the Gap Between LLMs and Enterprise AI

As 2025 comes to a close, the team at NebulaGraph reflects on a transformative year filled with innovation, growth, and community-driven progress. We extend our deepest gratitude to our global community of developers, users, contributors, and everyone passionate about graph database technology. In 2025, we’ve pushed the boundaries of what graph databases can achieve—especially in the age of AI—and we’re excited to share a recap of our journey.

# Foundation & Evolution: Strengthening the Core for Demanding Workloads

In 2025, our first commitment was to evolve our core database and cloud platform to meet the escalating demands of enterprise-scale applications, ensuring they are faster, more reliable, and more intelligent.

## Cloud Democratization and Enterprise-Grade Trust

We believe powerful technology should be accessible. This core principle guided our cloud strategy throughout 2025. It began with the launch of the [NebulaGraph Cloud Standard Tier with a 14-Day Free Trial,](https://www.nebula-graph.io/posts/NebulaGraph_Cloud_Standard_Tier_Launches_with_14_Day_Free_Trial) which lowered the barrier to entry and allowed more teams to experience a fully-managed graph database service. 

Our commitment to building Enterprise-Grade Trust was underscored by our NebulaGraph Enterprise v5.2 release with expanded support for the Graph Query Language (GQL) standard which offers clients peace of mind by eliminating vendor lock-in. Our new native GQL procedures lower the barrier for graph algorithms and enable seamless transitions from experimentation to production.  More importantly, they lay the groundwork for future integration with popular graph neural network (GNN) frameworks like PyTorch Geometric (PyG) and DGL.

This journey of innovation culminated in the release of the [NebulaGraph Cloud Latest Version](https://www.nebula-graph.io/posts/NebulaGraph_Cloud_Latest_Version), designed to unleash AI-ready graph capabilities and advanced data visualization. This update empowers users to not just store connected data but to intuitively explore and derive actionable insights from it, further democratizing the power of graph intelligence for teams aiming to build next-generation AI applications.

## The Enterprise Platform: Embedding Intelligence

For our enterprise users, 2025 was defined by two major releases that fused graph and vector capabilities:

*   [**NebulaGraph Enterprise v5.1**](https://www.nebula-graph.io/posts/NebulaGraph_v5.1_Embeds_Vector_Search) made a pivotal leap by **natively embedding vector search.** This transformed the platform into a unified database for AI-grade data fusion, enabling developers to manage both relational and semantic data in one place.  This enhancement significantly expands NebulaGraph Enterprise’s ability to handle unstructured data such as text, audio, and video—unlocking richer insights across domains like financial risk control and knowledge graph construction.
    
*   [**NebulaGraph Enterprise v5.2**](https://www.nebula-graph.io/posts/NebulaGraph_Enterprise_v5.2_Powers_the_Next_Generation_of_AI_Applications) is built upon this foundation to **power the next generation of AI applications.** It natively supports full-text indexes, complementing its existing vector search capabilities. Now, developers can run graph traversals, semantic vector lookups, and keyword-based text searches—all within a single query and a single system. 

# The Confluence: Where Graph Technology Meets Artificial Intelligence
In 2025, we advanced the thesis that graph databases are the essential structural layer for reliable, contextual, and explainable AI.
## Architecting the Future: Open Source and Innovation

We championed open collaboration as the engine of innovation. The [open-source release of the NebulaGraph MCP Server](https://www.nebula-graph.io/posts/Announcing_the_Open_Source_Release_of_NebulaGraph_MCP_Server) provided a critical bridge, allowing developers to explore infinite possibilities for integrating graph databases with diverse AI agents and applications. Our foundational article, ["Why Graph Databases Like NebulaGraph Are Essential for the Next Generation of AI and LLMs,"](https://www.nebula-graph.io/posts/Announcing_the_Open_Source_Release_of_NebulaGraph_MCP_Server) laid out the compelling architectural reasoning for this convergence.

## From Theory to Practice: The GraphRAG Revolution

The concept of Retrieval-Augmented Generation (RAG) evolved significantly with graph technology. We introduced [Fusion GraphRAG](https://www.nebula-graph.io/posts/Fusion_GraphRAG_The_Next_Evolution_in_Enterprise_Knowledge_Intelligence), positioning it as the next evolution in enterprise knowledge intelligence. This was complemented by domain-specific breakthroughs like **BioGraphRAG for Biomedical Knowledge Graph Retrieval Augmented Generation,** demonstrating how graphs bring precision to LLMs in critical fields. Our [two-part series, ](https://www.nebula-graph.io/posts/When_AI_Meets_Graph_Databases_Innovating_with_Multimodal_Data_Fusion)"[When AI Meets Graph Databases: Innovating with Multimodal Data Fusion](https://www.nebula-graph.io/posts/When_AI_Meets_Graph_Databases_Innovating_with_Multimodal_Data_Fusion_Part2)," provided a comprehensive guide to the architectures and benefits of this powerful combination.

# Impact Amplified: Graph-Powered Solutions Across the Globe

The true measure of technology is its real-world impact. In 2025, organizations across industries used NebulaGraph to solve mission-critical challenges, delivering exceptional business value.

| Industry Sector             | Use Case Highlight                                                                 | Key Outcome / NebulaGraph Value                                                                                             |
|-----------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
| Social Media & E-Commerce   | **[Snapchat](https://www.nebula-graph.io/posts/snapchat_enhances_friend_recommendations_with_nebulagraph)** for Recommendation Systems                                           | Improved performance;  Very large graph support, High Availability, Cost Effective Hardware.                            |
| Financial Technology & Risk Control | **[Airwallex](https://www.nebula-graph.io/posts/Use-case-Airwallex)** for Cross-Border Payment Solutions;  **[EasyCash](https://www.nebula-graph.io/posts/Easy_Cash_Real-Time_Risk_Detection)** for Real-Time Risk Detection at High Concurrency | Streamlined complex transaction networks; Enabled sub-second fraud prevention in high-growth scenarios.               |
| Web3 & Digital Assets       | **[BlockSec](https://www.nebula-graph.io/posts/use_case_BlockSec)** Crypto Risk Control System; Insights on **[Stablecoin Monitoring](https://www.nebula-graph.io/posts/Graph_Technology_for_Stablecoin_Transaction_Monitoring)** & **[Web3 Risk Management](https://www.nebula-graph.io/posts/why_graph_models_outperform_traditional_tools_in_web3_risk_management)** | Mapped address labels and fund flows to preempt threats; Outperformed traditional risk models.                        |
| Enterprise Operations       | **[BOSS Zhipin](https://www.nebula-graph.io/posts/use_case_boss_zhipin)** Intelligent O&M; **[ZKH](https://www.nebula-graph.io/posts/Use-case-ZKH)** Core Business Optimization           | Achieved 20-second root cause identification; Drove 15% gains in key business areas.                                  |

These stories, along with explorations into **scaling connections to solve modern data complexity** and the power of [real-world graph algorithms](https://www.nebula-graph.io/posts/Transforming_Risk_Control_using_Real-World_Graph_Algorithms_with_NebulaGraph), show that graph technology is becoming a competitive imperative.

# Recognition and Forward Momentum

Our community's efforts garnered significant global recognition in 2025. A milestone that filled us with immense pride was [NebulaGraph being ranked #2 globally among graph databases](https://www.nebula-graph.io/posts/NebulaGraph_Ranks_2_Globally_Among_Graph_Databases). This achievement is a direct reflection of the trust placed in us by our users and the vibrant activity of our community. It reinforces our mission to build world-class graph technology.

# Looking to 2026: The Intelligent Connected Future

As we stand on the threshold of 2026, the trajectory is clear. The fusion of **Graph + AI + Real-Time Processing** will become the standard stack for building intelligent applications. We envision a future where graph intelligence moves beyond databases to become a pervasive, actionable layer across all data systems.

NebulaGraph is committed to leading this charge. We will continue to innovate at the intersection of these technologies, focusing on:

*   **Unmatched Scalability and Performance:** Continuing our leadership in this area by introducing innovative new designs, efficiency improvements, and workload specific query support.    
*   **Seamless Multimodal Fusion**: Further blurring the lines between structured, unstructured, and semantic data.    
*   **Real-Time Graph Intelligence**: Powering instantaneous decision-making in dynamic systems.   
*   **Democratized Development**: Making graph and AI capabilities more accessible to developers everywhere.

# Building the Connected World, Together

2025 has been a defining year. From strengthening our core and pioneering the GraphRAG revolution to witnessing our technology drive tangible global impact, every step was a collective achievement. The journey of a graph is about connections, and our greatest connection is with you—our community and users.

Thank you for being an integral part of this story. Your curiosity, challenges, and innovations inspire us daily. Let's continue to explore, build, and shape a more intelligent and connected future together.

Here's to a brilliant and groundbreaking 2026!

**Explore the Full 2025 Blog Archive:** [NebulaGraph Blogs](https://www.nebula-graph.io/posts)

A comprehensive look at our advancements in graph database technology, AI integration with GraphRAG, and major product releases like NebulaGraph Enterprise v5.2. Discover real-world use cases in fintech, Web3, and AI, and see how we achieved global recognition as a top graph database.