Knowledge Graph Market Size & Share 2026-2035

Knowledge Graph Market Size

The global knowledge graph market was valued at USD 1.5 billion in 2025. The market is expected to grow from USD 1.7 billion in 2026 to USD 8.4 billion in 2035 at a CAGR of 19.4%, according to latest report published by Global Market Insights Inc.

The rapid adoption of generative AI tools by enterprises has resulted in an immense increase in the demand for knowledge graph tools. Traditional Large Language Models (LLM) suffer from issues related to accuracy, domain specificity, and interpretability. An increasing number of companies are adopting GraphRAG framework that integrates knowledge graphs with LLM.^[1]  GraphRAG architectures improve factual grounding in LLM outputs by coupling vector similarity search with explicit graph traversal, enabling AI systems to reason across interconnected entities through multi-hop relationships rather than isolated text fragments. The underlying driver is the imperative for enterprise AI systems to deliver auditable, explainable outputs at production scale a requirement that standard flat-retrieval approaches cannot satisfy in environments where data spans organizational hierarchies, contracts, product taxonomies, and regulatory frameworks simultaneously.

There are increasing amounts of data being generated within organizations due to various means such as documents, emails, websites, social media, Internet-of-things sensors, databases, enterprise applications, and interaction with customers. In many cases, there are high amounts of unstructured data being produced which make it hard to establish relations via traditional data management systems. This problem is solved by knowledge graphs which enable connections between different data sources in terms of a semantic model. OECD digital economy data indicates that global data generation continues to compound at rates exceeding 20% annually, with the proportion classified as unstructured or semi-structured growing disproportionately across sectors including healthcare, finance, and logistics.^[2]

More regulatory scrutiny and risks are creating the need for explainable artificial intelligence solutions. Various sectors including health care, financial services, government and insurance need to know how particular AI recommendations or decisions were generated. By capturing the relationships and reasonings between data points, knowledge graphs improve explainability. With more regulations in the AI space being adopted across the globe, more firms are adopting knowledge graphs. The EU Artificial Intelligence Act (Regulation 2024/1689), which entered full enforcement in August 2024, classifies AI systems used in credit scoring, healthcare triage, employment screening, and law enforcement as high-risk, requiring conformity assessments, technical documentation, and ongoing monitoring logs. Knowledge graphs satisfy explainability requirements by making the evidence structure underlying AI decisions machine-readable and auditable, enabling organizations to trace the precise graph paths that informed any given output.^[3]

Adoption of W3C-defined interoperability standards including the Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL query protocol is creating a compounding network effect across enterprise and government data ecosystems.^[4] As public sector agencies and large enterprises standardize on semantic data layers for master data management and cross-system interoperability, the knowledge graph industry emerges as the natural integration substrate. EU’s Interoperability Framework for European Public Services and US Federal Data Strategy both acknowledge semantic data as a long-term architectural vision, giving rise to a policy whose implementation through procurement is anticipated within the timeframe from 2027 to 2032.^[5]

Knowledge Graph Market Trends

GraphRAG is the integration of knowledge graph retrieval with large language model generation is transitioning from research concept to production infrastructure at measurable velocity. The mechanism is precise, where standard retrieval-augmented generation relies on vector similarity matching across unstructured text chunks. The underlying driver is the inadequacy of flat vector retrieval for queries requiring relational context a limitation that becomes acute in enterprise environments where data spans contracts, organizational hierarchies, product taxonomies, and regulatory frameworks simultaneously. In July 2024, Microsoft's open-source GraphRAG framework, released and subsequently integrated into Azure AI Search, established a reference architecture that enterprise data engineering teams have adopted at scale, with demonstrable outperformance over standard RAG on multi-hop reasoning benchmarks. 

There have been shifts in how knowledge graph technologies have been implemented. This time around, there is an abundance of vendors offering knowledge graph technologies through cloud-based managed services. KGaaS products provide features of automation for ontology management, integration, security, and graph technologies that are ready to use for artificial intelligence. Their adoption is driven by shifting from expensive capital expenditure models to operational expenses. In June 2025, Neo4j added more advanced capabilities of generative AI and GraphRAG to its cloud-native service AuraDB. Thus, firms were provided with the possibility of using knowledge graph applications without handling underlying infrastructure. The reason for this development is the growing demand for managed KGaaS services.

Traditional knowledge graphs were mostly updated through batch processes with synchronized data. Nevertheless, modern enterprises are more inclined towards using continuously updating knowledge graphs based on stream processing technologies.
It has become possible due to an increase in popularity of real-time graph solutions that can ingest real-time data from IoT sensors, operational systems, trading platforms, and digital communication channels. In August 2025, TigerGraph improved its graph analytics engine, enabling dynamic fraud, cybersecurity, and supply chain management applications. This improvement highlighted the increasing relevance of streaming knowledge graphs in analysing constantly changing business data.

Organizations have moved from using general-purpose graphs to using domain-specific knowledge graphs working within certain industries and business capabilities. Such domain knowledge graphs allow for better semantic consistency and faster deployment while improving AI operations. Specifically, many industries including healthcare, pharma, finance, manufacturing, energy, and government have increasingly been investing in building their own knowledge graph environments. In July 2025, AstraZeneca utilized biomedical knowledge graphs in its drug discovery processes to integrate knowledge from scientific literature, clinical data, and molecular databases. This move proved an increased need for ontologies specific to different domains and geared towards managing knowledge within industries.

Knowledge Graph Market Analysis

Based on offering, knowledge graph market is divided into solutions and services. Solutions segment dominated the market, accounting for 72% in 2025 and is expected to grow at a CAGR of 18.6% through 2026 to 2035.

• Solutions segment emerged as the major contributor to the knowledge graph industry value in 2025 due to the rising needs for enterprises to deploy knowledge graph solutions that enable them to organize, link, and analyze interconnections within and between data sources. Knowledge graph solutions have been deployed by enterprises to enhance their capabilities in semantic search, data integration, business intelligence, and AI-enabled decision-making.
• Growing applications of GraphRAG, enterprise AI, and semantic data architectures have been further fuelling the rise in demand for knowledge graph solutions. Knowledge graph solutions include components like enterprise knowledge graph platforms, graph databases, visualization tools, and graph analytics solutions.
• The services component is witnessing significant growth among firms due to the increasing requirement for the involvement of experts that can help deploy and manage knowledge graph platforms. These services play an important role in resolving issues associated with the creation of ontologies, integration of data, graph modeling, deployment, and system optimizations to achieve success in the projects.
• Due to the increasing complexity of knowledge graphs, and also due to the lack of experience in this industry, there is a growing demand for managed services and professional services. Organizations are now using managed services to counteract implementation risk and improve efficiency and scalability.

Based on deployment model, knowledge graph market is segmented into cloud-based, on-premises and hybrid. Cloud-based segment dominates the market, accounting for 61.6% share in 2025, and the segment is expected to grow at a CAGR of 19.5% from 2026 to 2035. 

• Cloud-based solutions account for the biggest market share owing to their scalability and reduced need for upfront infrastructure. Cloud-based deployments are preferred by companies to process AI workload, GraphRAG use cases, and Enterprise Knowledge Management systems.
• There is a continued significance of on-premise deployments within companies that have security, privacy, and regulatory compliance concerns. Government, military, healthcare, and finance industries tend to go for on-premise installations to gain more control over their data assets.
• Hybrid deployment is emerging as a favored solution by companies looking for the best of both worlds. By using hybrid architecture, enterprises can store sensitive data within on-premise storage while relying on cloud computing for data analytics and expanding knowledge graphs.

Based on organization size, knowledge graph market is divided into large enterprises and small and medium enterprises (SMEs). Large enterprises segment dominates the market, accounting for 73.2% share in 2025, and the segment is expected to grow at a CAGR of 18.6% from 2026 to 2035.

• The large enterprises were the primary players in the market, owing to the presence of an intricate data ecosystem along with heavy investments in AI and digital transformation technologies. These organizations have implemented knowledge graph technology to enhance their capabilities with respect to data integration, enterprise search, customer insights, and decision-making across various divisions.
• The large enterprises have been the key players in adopting the GraphRAG architecture and other innovative AI applications. This requires the large firms to build complex knowledge management systems.
• SME segment is anticipated to see higher growth rates in the forecast period, thanks to rising popularity in cloud and KGaaS solutions. This allows lowering the cost of implementing knowledge graphs and overcoming any technical difficulties in doing so.
• SMEs use knowledge graphs to gain more insights about customers, increase efficiency, and enable business process automation using artificial intelligence.

Based on application, knowledge graph market is divided into semantic search & information retrieval, fraud detection & risk management, recommendation systems, data analytics & business intelligence, data governance & master data management (MDM), virtual assistants & question answering systems and others. Semantic search & information retrieval segment dominates the market, accounting for 22.3% share in 2025, and the segment is expected to grow at a CAGR of 19.6% from 2026 to 2035.

• The use of semantic search and information retrieval accounted for the biggest market segment. Knowledge graphs help make searches accurate, since they consider relationships, context, and meaning, allowing end-users to access relevant data through enterprise datasets.
• Banks and insurance companies have started using knowledge graphs to detect any fraud, hidden relationships, and unusual patterns. They use knowledge graphs to help analyze risks and improve compliance activities.
• Recommendation engines benefit from the ability of knowledge graphs to analyze relationships between customers, products, and services. Companies use their features to provide better customer experiences and engagement opportunities.
• Analytical systems can benefit from knowledge graphs because they allow organizations to connect different datasets together, thus helping them discover new insights.

U.S. knowledge graph market reached USD 526.5 million in 2025, with a CAGR of 18.1% from 2026 to 2035.

• U.S. dominates the international market of knowledge graphs, fueled by substantial investments in the areas of artificial intelligence, cloud computing, and advanced analytics software.
• Integration of knowledge graphs with large language models via GraphRAG architecture is becoming more prevalent among organizations looking for accuracy, context, and explanations.
• Knowledge graphs find application in financial institutions, healthcare, retail organizations, and governments in the sphere of intelligent search, fraud prevention, and knowledge management. Increasing use of data for decision-making contributes to the adoption rates.
• The market is set to sustain its growth driven by artificial intelligence investments and the growing need for reliable enterprise data platforms.

North America dominated the knowledge graph market with a market size of USD 611.3 million in 2025. 

• North America represents the dominant player in the knowledge graph industry owing to its established technology base and advanced digital infrastructure. Companies within various sectors are adopting intelligent data solutions powered by AI.
• The North American region enjoys a significant level of investments made by enterprises in cloud computing solutions, analytical platforms, and knowledge management technologies, which helps create a conducive environment for knowledge graphs.
• Increasing focus on data integration and semantic intelligence is making firms adopt such platforms for the same purpose. Businesses are resorting to use of knowledge graphs for improving their processes and deriving valuable insights.
• GraphRAGs are likely to witness significant growth in coming years alongside growing interest shown by firms in adopting enterprise AI projects. Knowledge graphs will serve an important role in future data platforms.

Europe knowledge graph market accounted for a share of 26.4% and generated revenue of USD 387.4 million in 2025. 

• In Europe, there is considerable growth in knowledge graph technology because more organizations are placing their attention on digital transformation and data governance.
• More companies operating in different sectors including health care, financial services, manufacturing, and government have started using graph databases to connect data and enhance decision making capabilities.
• There are rising investments in innovation in semantic technology, which is contributing to the growth in demand for graph database solutions. Knowledge graphs have become a vital part of Europe’s digital economy.
• The existence of stringent regulations is causing companies to use AI technology that promotes explainability and information management. The knowledge graph solution enables organizations to meet these objectives effectively.

Germany dominates the knowledge graph market, showcasing strong growth potential, with a CAGR of 19.8% from 2026 to 2035. 

• Germany is one of the major players contributing to the knowledge graph industry in Europe, owing to its robust industry and manufacturing sector. There are many opportunities available in the country with the adoption of Industry 4.0 initiatives.
• The use of knowledge graphs by manufacturers is helping them in linking factory data, supply chain data, and industrial machines. This allows greater visibility and the ability to predict machine breakdowns through predictive maintenance.
• The rising adoption of digital twins and industrial artificial intelligence applications is also boosting the demand for knowledge graphs. They are enabling companies to make decisions using contextual intelligence. There will be further growth of the market driven by smart manufacturing initiatives and enterprise digitalization efforts.

The Asia Pacific knowledge graph market is anticipated to grow at the highest CAGR of 22.1% from 2026 to 2035 and generated revenue of USD 324.6 million in 2025.  

• Asia Pacific is forecasted to be the leading regional market owing to digital transformation coupled with increased usage of AI. Businesses are increasingly deploying sophisticated data management solutions.
• There is huge growth being witnessed in terms of enterprise data creation in areas like finance, healthcare, telecommunication, and e-commerce. Knowledge graphs are assisting businesses in maximizing their benefits from these data assets.
• Various government policies encouraging development in the field of artificial intelligence and digital innovations are contributing to the market growth. Various nations have launched smart city and intelligent infrastructure projects.
• Increased cloud deployments along with knowledge about semantic technologies are anticipated to fuel the growth further. Asia Pacific provides immense growth potential for market players.

China knowledge graph market is estimated to grow with a CAGR of 23.2% from 2026 to 2035. 

• China’s knowledge graphs market is one of the fastest-growing due to the government's active sponsorship of the ai development. The technology adoption is consistently stimulated owing to continuous investments into the digital infrastructure.
• The growing trend in applying knowledge graphs can be seen in relation to such applications as recommendation engines, enterprise search, and intelligent automation, allowing firms to take advantage of data more efficiently.
• Furthermore, manufacturing firms use knowledge graphs in creating smart factories, which allows implementing AI solutions. Digitalization across different industries results in an increase in demand for graph technology.
• The upcoming innovations in AI and big data analytics will support the rapid growth rates of the market. The knowledge graphs become an essential part of China's digitalization strategy.

Latin America knowledge graph market shows lucrative growth over the forecast period. 

• The knowledge graph industry in Latin America is slowly evolving as businesses leverage new-age data management and analytics platforms. Digital transformation strategies are providing a conducive environment for the market.
• In LATAM, banks are turning to knowledge graphs to detect fraud and assess risks and also gain customer insights. This trend is aiding companies in becoming more effective.
• Knowledge graph technologies are also being leveraged by retailers, telecom, and the public sector to gain better insights into their operations. Advanced analytics are highly sought after.
• With AI technologies witnessing growing adoption along with enhanced cloud capabilities, the use of knowledge graphs is expected to rise. There are new opportunities for vendors.

Brazil knowledge graph market is estimated to grow with a CAGR of 20.3% from 2026 to 2035 and reach USD 175.7 million in 2035.  

• The Brazilian market constitutes the leading one in terms of the use of knowledge graphs, as more and more companies are becoming digitally active. In particular, the financial sector in the region uses graph analytics to prevent fraud and gain valuable insights into the behavior of customers. There is an evident need among such businesses to develop better visibility and risk assessment capabilities through technology usage.
• Moreover, e-commerce and retail companies are utilizing knowledge graphs to enhance their recommendation systems and customer experience strategies. The expansion in online activities has led to vast amounts of data being generated.
• Increasing cloud usage and technological investments are projected to fuel further market growth in Brazil. This country will be the dominant market in Latin America throughout the forecast period.

Middle East and Africa knowledge graph market accounted for USD 64.7 million in 2025 and is anticipated to show lucrative growth over the forecast period. 

• The knowledge graph industry in the Middle East & Africa is growing due to rising efforts toward digital transformation. There are investments by governments and businesses in technologies enabling intelligent data management.
• There is a growing use of knowledge graphs in smart cities, cybersecurity, and modernization of the public sector, among other areas. Such use cases help organizations become more efficient in operations and services delivery.
• Rising usage of AI solutions in various business verticals leads to rising need for solutions that facilitate data contextuality and integration. Graph-based technologies are gaining more recognition within enterprises.
• Future investments in cloud infrastructure and digital innovations will further contribute to the growth of the market. The Middle East & Africa have considerable potential in knowledge graphs.

Saudi Arabia knowledge graph market is expected to experience substantial growth in the Middle East and Africa market, with a CAGR of 22.2% from 2026 to 2035. 

• Saudi Arabia is becoming one of the rapidly developing markets in the Middle East region due to the vision-driven strategy of the country regarding its digital transformation efforts.
• Government-led projects aimed at advancing artificial intelligence, digital governance, and digital economy have created a favorable environment for the use of knowledge graphs in both private and public sectors.
• In Saudi Arabia, many businesses are now increasingly adopting advanced technologies related to data management and AI to increase efficiency and effectiveness in their operations. The adoption of knowledge graphs has become more prevalent as many organizations have decided to integrate various fragmented data sources and enhance semantic search to build a trusted foundation for the development of their applications based on AI.
• Some of the industries that will see considerable growth and adoption of knowledge graph solutions include financial services, energy, telecommunication, and government. Oil and gas companies, for example, are looking to use knowledge graphs to help optimize their asset management, increase operational intelligence, and implement predictive maintenance practices.

Knowledge Graph Market Share

The top 7 companies in the knowledge graph industry includes Neo4j, Microsoft, Amazon Web Services (AWS), Google (Alphabet), IBM, Oracle, TigerGraph contributing 54.2% of the market in 2025.

• Neo4j is one of the most prominent companies providing graph databases and offering knowledge graphs and graph analytics capabilities. This vendor's products allow handling complex interconnected data sets, and are popularly used in fraud detection, recommendation systems, networking analysis, and GraphRAG implementations. It operates in financial services, healthcare, telecoms, and government industries.
• Microsoft is a software company that has provided knowledge graphs through its cloud computing platform referred to as Azure. It has used graph technology in enhancing enterprise search, data integration, semantics, and generative artificial intelligence. The software giant has increasingly been focusing on the application area of GraphRAG.
• AWS offers graph databases as part of its Amazon Neptune suite of services, thus helping companies design knowledge graphs at scale. AWS is able to do so thanks to the powerful infrastructure that AWS has developed in the cloud environment.
• Google (Alphabet) operates one of the biggest knowledge graphs of the world using its Google Knowledge Graph to power entity cards and featured snippets in Google Search. Enterprise-focused offerings include Google Cloud's Vertex AI platform, which offers knowledge graph building capabilities as well as graph neural network modeling along with the knowledge graph query capabilities of Google Cloud Spanner.
• IBM provides knowledge graph functionalities via its IBM Watson Knowledge Catalog, IBM OpenPages (for governance, risk, and compliance relationship graphs), and IBM Db2 Graph. The positioning of IBM within its target market segments healthcare, financial services, and government is supported by compliance considerations within its product design as well as its compatibility with IBM's AI governance products, such as AI Fairness 360 and AI Explainability 360 toolkits.
• Oracle incorporates graph processing functionality in its database offering, Oracle Database 23ai, by incorporating Oracle Property Graph, making it possible for graph queries to be run against relational data without any data transfer - a unique feature that benefits the vast number of Oracle ERP customers. By using a graph query language based on SQL (SQL/PGQ), which adheres to ISO/IEC GQL standards, Oracle makes it easier for Oracle data engineers to work with graphs.
• TigerGraph specializes in delivering high-performance graph analytics, characterized by the use of distributed computing, thus making it possible to conduct deep-link analytics within billions of edges in real-time. The GSQL query language used by TigerGraph offers SQL-like semantics in pattern-matching for graphs and lowers the learning curve for data engineers in enterprises. TigerGraph Cloud ensures that consumption-based graph analytics can be scaled elastically while tying up the performance capabilities of the platform with the budgets of mid-market and SMEs. In November 2025, TigerGraph received USD 105 million investment in Series D financing.

Knowledge Graph Market Companies

Major players operating in the knowledge graph industry are: 

• Amazon Web Services (AWS)
• Google (Alphabet)
• IBM
• Microsoft
• Neo4j
• Ontotext
• Oracle
• SAP
• Stardog
• TigerGraph

 

• Knowledge graph competition consists of traditional tech suppliers, cloud hyperscalers, graph database experts, and AI software vendors. Some notable players like Neo4j, Microsoft, Amazon Web Services (AWS), Google, IBM, Oracle, and TigerGraph have emphasized the development of graph analytics, GraphRAG features, semantics, and enterprise-level AI integration. Building strategic partnerships, developing cloud solutions, and investing in generative AI seem to be crucial for competing in the market.
• Scalability of the offered platforms, ability to conduct analytics, build explainable AI systems, and integrate large language models become central to competition in the field of knowledge graphs. In order to compete, companies develop new products, perform acquisitions, and create solutions for specific industries like healthcare, finance, manufacturing, and governments. As interest in AI-enabled data architecture increases, companies that are capable of seamlessly integrating knowledge graphs into their cloud platforms and enterprise-level AI solutions will enjoy an undeniable competitive edge.

Knowledge Graph Industry News

• In May 2026, Neo4j announced the general availability of Neo4j 6.0, featuring native multi-model support that combines property graph and vector index capabilities within a unified query interface, directly targeting enterprise generative AI application development workflows.
• In Mar 2026, Amazon Web Services expanded Neptune Analytics with Bedrock Graph Connectors, enabling direct, low-latency integration between Amazon Neptune knowledge graphs and Amazon Bedrock foundation model APIs for production GraphRAG deployments. 
• In Jan 2026, Microsoft integrated GraphRAG capabilities natively into Microsoft Copilot for Enterprise, enabling knowledge graph-grounded reasoning across SharePoint, Teams, and Exchange data assets for Microsoft 365 E5 subscribers. 
• In Nov 2025, TigerGraph secured a USD 105 million Series D funding round, earmarked for expansion of its real-time graph analytics platform across Asia Pacific financial services markets and enhancement of its cloud-native GSQL development environment. 
• In Sep 2025, The World Wide Web Consortium (W3C) published updated recommendations for SPARQL 1.2, introducing graph pattern composition enhancements that improve query performance on federated knowledge graph deployments across distributed triple stores.
• In Jul 2025, Ontotext released GraphDB 11, featuring built-in LLM connector pipelines for multiple foundation model APIs, enabling configuration-driven construction of knowledge graph-grounded retrieval workflows without custom integration engineering.

The knowledge graph market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn/Bn) from 2022 to 2035, for the following segments:

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Market, By Offering

• Solutions
  • Enterprise knowledge graph platforms
  • Graph database engines
  • Knowledge management toolsets
  • Graph visualization & exploration tools
  • Graph analytics & querying tools

• Services
  • Professional services
  • Managed services

Market, By Model Type

• Labeled property graph (LPG)
• RDF/ Triple store
• Ontology-based/ OWL

Market, By Deployment Model

• Cloud-Based
• On-Premises
• Hybrid

Market, By Organization Size

• Large enterprises
• Small & Medium Enterprises (SMEs)

Market, By Application

• Semantic search & information retrieval
• Fraud detection & risk management
• Recommendation systems
• Data analytics & business intelligence
• Data governance & master data management (MDM)
• Virtual assistants & question answering systems
• Others

Market, By End Use

• BFSI
• Healthcare & life sciences
• Government & public sector
• IT & telecommunications
• Retail & e-commerce
• Media & entertainment
• Manufacturing
• Others

 

The above information is provided for the following regions and countries: 

• North America 
  • U.S.
  • Canada 

• Europe 
  • Germany 
  • UK 
  • France 
  • Italy 
  • Spain 
  • Sweden
  • Switzerland
  • Netherlands

• Asia Pacific 
  • China 
  • India 
  • Japan 
  • South Korea 
  • Australia 
  • Australia 
  • Singapore
  • Malaysia
  • Thailand
  • Indonesia
• MEA 
  • South Africa 
  • Saudi Arabia 
  • UAE 

• Latin America
  • Brazil 
  • Mexico 
  • Argentina