Graph technology landscape

Graph technology is built to handle complex networks of data, surfacing connections and highlighting patterns and anomalies far more efficiently than more traditional technologies. In a world where organizations rely on increasing amounts of data for critical business decisions, high-stakes investigations, and more, it’s no wonder that graph has been gaining more traction.

Graph technology is still a relatively young domain - which means it’s still evolving a lot. Our current context of big data and increasingly data-driven business operations has in turn driven growth and a lot of innovation in the sector, as a wide variety of organizations have come to see the value that graph technology can provide in delivering deeper insights that were previously inaccessible.

Graph technology: quickly evolving and expanding

We originally published an introduction to the graph technology landscape back in 2014. Linkurious was still young. And Google’s PageRank algorithm and Facebook’s Graph Search were still fresh examples of what you could do with graph technology. At the time we described the graph ecosystem as “emerging”, and there were far fewer tools available on the market.

The graph technology market has vastly expanded since then as existing companies have matured and built on their graph products, and as new players have emerged. Graph enthusiasts and graph companies have also made major strides in further democratizing graph technology.

The result has been that more and more organizations are adopting graph technology, as they find it to be an asset for an ever increasing number of use cases. We’ll be honest, this has been a thrill to watch over the years as the graph industry finds new ways to deliver value for all kinds of applications. We now see graphs being used for cybersecurity, drug discovery, finance, anti-money laundering, intelligence, manufacturing, IT management… the list goes on.

We’re not the only ones to notice this, of course. The proof is in the market growth. The graph database market is expected to grow to a value of US$ 3.78 billion by 2027, up from US$ 1.13 billion in 2021.

Gartner has also observed the ways in which graph serves modern enterprises and has predicted that this technology will continue to grow. “Graph forms the foundation of modern data and analytics with capabilities to enhance and improve user collaboration, machine learning models and explainable AI,” Gartner writes in their Top 10 Data and Analytics Trends for 2021.“

Graph and AI

Another factor driving the popularity of graph technology is its ability to enhance various artificial intelligence (AI) applications. This includes generative AI applications like large language models (LLMs), whose accuracy can be improved using knowledge graphs. “Although graph technologies are not new to data and analytics, there has been a shift in the thinking around them as organizations identify an increasing number of use cases,” writes Gartner. “In fact, as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology.”

Understanding the graph technology landscape

With all this change and increasing growth, it seemed high time to revisit the graph technology overviews we published in 2014 and in 2019. Our goal is to introduce you to the key categories within the world of graph tech, and to the key players within those categories, as we’ve seen them emerge and evolve over our 10+ years of experience in the field. We cover tools all across the graph data value chain, from data injection to end-user analytics. To help you understand where each of these tools fits along the value chain, we’ve drawn up a diagram to keep in mind as you read.

To be clear, this list is not all encompassing. The graph technology landscape has become very large and is evolving quickly, so it would be tough to capture a complete picture - and even if we did, it would soon be outdated by the many changes that are happening all the time in this field. Instead, we’re delivering an overview of the main tools and solutions on the market, alongside some important trends to keep an eye on. To keep things simple and (relatively) concise, we’ve limited the list to companies of at least 15 people.

The graph technology landscape in 2025

Graph databases

When discussing the graph technology landscape, graph databases are the logical place to start. Graph databases are foundational for the adoption of graph technology. These systems help organizations tackle the technical challenges of storing complex connected data and extracting insights from very large datasets. If you have a graph application, chances are that you will require a graph DB.

There are several types of databases that are designed for graph data, which include property graph databases, RDF databases, multi-model databases, and high-performance computing databases. We’ll briefly get into what defines each of these categories below. There are fewer new players today than there were some years back, but the graph database space remains active. Google is the most recent big player to enter the ring, announcing their Spanner Graph in August 2024.

There is an increasing offer of cloud graph databases, many of them coming from major providers already established in the graph space, supporting teams building cloud-based applications.

Despite growth in the sector, the graph DB space isn’t without its challenges. Certain graph database companies have recently seen layoffs. Redis announced the end of life of RedisGraph in mid-2023.

And as with any newer technology sector, the space is still shifting. Some of the most recent changes are related to query languages. Graph Query Language Standard (GQL), a standardized query language for property graphs, was released in April 2024. And Property Graph Query Language (PGQL) is a standard built on top of SQL, that brings graph queries to SQL databases.

Finally, we are seeing more and more SaaS offerings among graph databases.

Property graphs

Property graphs are entirely optimized to work with graph-like data. Here, Neo4j is the market leader, having launched their first native graph database in 2010. Since then several other players have emerged on the market.

• Neo4j. Their property graph database remains the most popular one. A closed source database, Neo4j exists in both on-premises and cloud versions (Neo4j Aura). Neo4j uses Cypher query language.
• Amazon Neptune. Amazon Neptune is closed source and is available both on-premises and on the cloud. Neptune supports graph models including Property Graph and W3C’s RDF and their query languages: Apache TinkerPop Gremlin, SPARQL and OpenCypher.
• Memgraph. Memgraph, publicly available since 2017, is a graph streaming platform built on top of an in-memory graph database. It is open source and uses Cypher query language. Memgraph can be run locally, on-prem or as a managed service through Memgraph Cloud.
• TigerGraph. This is a hybrid transactional/analytical processing database and analytics software, implemented in C++. It has its own graph query language similar to DQL called GSQL.
• JanusGraph. Originally called “TitanDB”, JanusGraph is the open-source project that invented the Gremlin query language. They provide a compatibility layer that allows to turn almost any Key-Value store into a distributed graph database.
• NebulaGraph. This is an open source, distributed, and easily scalable graph database that uses nGQL query language, and more recently GQL. It’s built for super large-scale graphs and can process trillions of nodes and edges.
• DGraph. DGraph is an open source native and distributed graph database, with native GraphQL support. It uses DQL, DGraph’s proprietary query language.
• Ultipa. This graph database and knowledge graph system supports real-time computing and analytics. Ultipa is closed source and uses a proprietary query language called UQL.

RDF

An RDF triplestore is a category of graph database in which data is stored as a network of objects. They use inference to discover new information from existing relations.

• OntoText (Graphwise). This is a graph database and knowledge discovery tool compliant with RDF and SPARQL. It recently merged with Semantic Web Company (SWC) to create a graph platform that combines knowledge graphs and semantic AI capabilities.
• Stardog. Stardog is a knowledge graph platform and graph DBMS with high availability and performance. It combines graph database technology with an AI-based knowledge toolkit.
• AllegroGraph. A high-performance, persistent RDF store with support for graph DBMS. AllegroGraph supports queries through SPARQL, Prolog and languages like JavaScript.
• AnzoGraph. This is a scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization.
• Eccenca. An enterprise knowledge graph platform that helps scale use of knowledge in automatic decisions across processes.

Multi-model

Multi-model databases emerged as an answer to the complexity that the multiplication of siloed systems was creating. These databases are designed to support various data types, handling in one single data store various models such as document, key-value, RDF and graphs. They are particularly convenient if you need to work with multiple data types but want to avoid the operational complexity of managing various silos.

• ArangoDB. This is a native multi-model, open-source database with flexible data models for documents, graphs and key-values.
• Microsoft Azure Cosmos DB. This is a fully managed NoSQL database for modern app development, available on-prem and on the cloud. Users can choose between document mode or graph mode, and in the latter, you can query your graph in SQL or Gremlin.
• Oracle Spatial and Graph. This product includes a property graph database with built-in graph analytics, and a range of spatial analysis functions. It uses PGQL query language and newly supports GQL.
• Google Spanner Graph. Spanner Graph supports a graph query interface compatible with GQL. It supports interoperability between relational and graph models, combining SQL capabilities with graph pattern matching from GQL.
• MarkLogic. MarkLogic is a historical stakeholder with a document-oriented database. It evolved from an XML database to natively store JSON documents and RDF triples.
• TerminusDB. This is an open source knowledge graph and document store, used to build versioned data products.
• Aerospike. The Aerospike database is a real-time data platform for multi-cloud JSON and SQL use cases.