We are proud to be named a Visionary in the 2023 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS) among 19 other recognized global DBMS vendors to assess their completeness of vision and ability to execute. This is the second year Neo4j has been positioned in the Gartner report. We believe our placement as a Visionary this year recognizes our continued pace of innovation to ground GenAI applications and solve customer problems by uncovering hidden relationships in data. Furthermore, we feel it reflects our ongoing customer success as organizations use our products on the cloud provider of their choice.
Gartner assessed vendors on criteria such as market understanding, innovation, data science support, and customer experience. Gartner defines Visionaries as having a strong market understanding and a robust roadmap for the cloud DBMS market. They have innovative ideas about functionality and demonstrate advanced use of new deployment models.
For us, being named a Visionary is a testament to our approach to helping businesses make sense of data through relationships in a cloud-first world. As written in, Gartner, Emerging Tech: Venture Capital Growth Insights for Graph Technologies, Aakanksha Bansal, Alys Woodward, 27 March 2023, “Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise”.Cloud Database Management Systems refer to provider-managed software systems that leverage cloud platforms to handle data. Core capabilities involve catering to diverse data models while data resides in a scalable, flexible cloud storage tier reducing overhead. Gartner evaluates DBMS vendors in the market based on their ability to execute and completeness of vision. 15 criteria assess vendors across areas, including product offerings, market responsiveness, and innovation strategy. To qualify for the Cloud Database Management Systems Magic Quadrant, companies must meet product capability and market awareness thresholds defined in Gartner’s index.
Empowering Customer Breakthroughs
Over 1,700 customers, including 75 Fortune 100 companies, use Neo4j to implement breakthrough solutions across diverse use cases like fraud detection, supply chain optimization, real-time recommendations, and identity & network security. We’re changing the rules of the database market by enabling organizations to uncover hidden relationships and patterns in their data. Graph inherently represents business logic better than traditional relational databases. Developers can easily perform multi-hop graph queries to uncover these connections while ensuring speed and continuity.
Here’s how we’ve helped customers achieve extraordinary outcomes:-
- Adobe reduced the hardware footprint for their Behance network from 125 servers to just three with Neo4j, reducing maintenance by 3x, storage by 1/1000, and costs by 16x while improving UX and extensibility.
- Dun & Bradstreet, powering 90% of the Fortune 500 with data insights, launched a new service to rapidly interpret complex corporate structures and ownership. Previously, a single query tied up staff for 10-15 days. With Neo4j, they reduced customer risk profile research from days to hours by applying graph algorithms.
- Transport for London built a real-time digital twin of London’s intricate transport network using Neo4j’s graph database with the aim to cut congestion by 10%, and deliver $750 million in annual savings by optimizing incident response times.
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- Native integration with Amazon Bedrock: Customers using Amazon Bedrock foundational models can reduce hallucinations by grounding their LLM/RAG use cases of virtual agents, real-time search, text, and summarization with an enterprise knowledge graph. With the addition of vector search, Neo4j can capture both explicit and implicit relationships and patterns, enabling AI systems to reason, infer, and retrieve relevant information effectively.
- Native integration with Google Cloud Vertex AI: Customers can now leverage knowledge graph with Google’s large language models to make generative AI outcomes more accurate, transparent, and explainable
- GenAI stack with Docker, LangChain, and Ollama: Out-of-the-box ready-to-code secure stack designed to help developers get a running start with generative AI applications in minutes.
- Vector Search: Integrated approximate nearest neighbor search natively into Neo4j, enabling contextual similarity queries on graph data that drives AI applications.
- Parallel Runtime: Introduced morsel-based parallel processing to run graph analytics queries concurrently across multiple cores, with customers seeing up to 100x faster complex queries.
- Change Data Capture: Added native capability to track data changes in real-time and take instant action across use cases like identity management.
- Pathfinding Algorithms: Released new algorithms for longest path identification, topological sort, and more that optimize workflows in supply chain, logistics, and beyond.
The database market is rapidly evolving as artificial intelligence, and especially large language models (LLMs), become more central to intelligent applications. Organizations seek to tap into AI to drive insights, personalization, and automation.
Graph databases like Neo4j that capture relationships and context are crucial to enhancing AI with knowledge graphs and tackling issues like LLM hallucinations. As Gartner analysts write in the June 2023 Gartner® report, AI Design Patterns for Knowledge Graphs and Generative AI, “Knowledge graphs provide the perfect complement to LLM-based solutions where high thresholds of accuracy and correctness need to be attained.”
Neo4j is well-positioned to lead in an AI-driven future. Our integrations with AI platforms like Google Vertex AI and Amazon Bedrock solve key challenges like accuracy and transparency. Neo4j provides the connections and context that generative AI needs to deliver more accurate, transparent, and explainable outcomes. Through native vector search and configurable data access policies, Neo4j enables AI systems to generate individualized responses grounded in the context of highly interrelated data. The market shift towards GenAI represents an opportunity for us to empower more organizations and developers to leverage relationships in their data.
Cloud-First Development
Neo4j has deep ecosystem partnerships, making graph database and analytics available across all the major cloud providers. This allows us to meet customers on their preferred cloud. Our focus is on delivering an enterprise-grade experience optimized for the cloud. Over the past year, we’ve deepened our product integrations with Google Cloud, Amazon Web Services (AWS), and Microsoft Azure:-
- Google Cloud: Integrated with Google Cloud’s Vertex AI platform so customers can now leverage Neo4j knowledge graphs to enhance Vertex AI outcomes with greater accuracy, transparency, and explainability.
- Amazon Web Services: Formed a strategic, multi-year collaboration with AWS to accelerate enterprise AI development. We launched Neo4j AuraDB Pro on AWS Marketplace and integrated with Amazon Bedrock, helping enterprises solve key AI challenges by grounding them in Neo4j knowledge graphs.
- Microsoft Azure: Collaborated closely with Microsoft to bring Neo4j AuraDB Enterprise to Azure customers worldwide. Joint initiatives enhance performance, scalability, and ease of use for AuraDB customers through integration with Azure services like Active Directory, Azure PrivateLink, and Azure Marketplace.
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