Graph Analytics Market by Component (Services, Software), Organization Size (Large Enterprises, Small And Medium Enterprises), Deployment Model, Application, Industry Vertical - Global Forecast 2025-2032
Description
The Graph Analytics Market was valued at USD 1.99 billion in 2024 and is projected to grow to USD 2.41 billion in 2025, with a CAGR of 21.56%, reaching USD 9.49 billion by 2032.
An authoritative introduction explaining why graph analytics has emerged as a strategic imperative for modern enterprises seeking relationship-aware intelligence and risk mitigation
Graph analytics is reshaping how organizations understand complex relationships, detect anomalies, and surface latent patterns across interconnected datasets. As enterprises increasingly confront challenges such as fraud, network performance degradation, and customer churn, graph technologies provide a natural way to model entities and their relationships, enabling richer context and faster insight generation. Over the past few years, improvements in graph processing frameworks, query languages, and integration with machine learning have accelerated adoption across both legacy and cloud-native environments, driving a shift from experimental pilots to production deployments.
Moreover, the confluence of scalable cloud infrastructures and specialized graph databases has reduced barriers to entry, allowing organizations of various sizes to test and deploy graph-driven solutions with lower upfront costs. This technological evolution is complemented by a growing body of practical use cases where relationship-aware analytics deliver measurable operational value. In turn, decision-makers are prioritizing investments that can strengthen security postures, optimize network operations, and enhance customer intelligence. Consequently, executives must view graph analytics not merely as a niche capability but as a strategic enabler of data-driven differentiation and resilience.
A forward-looking synthesis of the technological, architectural, and ecosystem shifts that are accelerating enterprise adoption and operationalization of graph analytics
The landscape for graph analytics is experiencing several transformative shifts that are redefining how value is created and captured. First, there is a move from single-node, research-oriented implementations to distributed, production-grade architectures that support real-time inference across highly connected datasets. This transition enables organizations to operationalize graph insights for mission-critical functions, such as fraud detection and network optimization, where latency and scale are non-negotiable. Second, graph analytics is increasingly integrated with machine learning pipelines, allowing feature engineering to leverage topological properties and community structures for superior predictive performance.
In parallel, deployment models are diversifying: some enterprises prefer cloud-native managed services that offer elasticity and rapid provisioning, while others retain on-premises or private cloud deployments to meet regulatory and latency requirements. Interoperability has become a focal point, with efforts to standardize connectors, APIs, and query interfaces to bridge graph stores with data lakes, streaming platforms, and analytics toolchains. Finally, the vendor ecosystem is maturing, with specialized platform software and broader solution suites that provide domain templates and prebuilt workflows, thereby shortening time-to-value for industry-specific applications. These shifts collectively point toward a future where graph capabilities are embedded across organizational processes rather than isolated in analytics labs.
A rigorous analysis of how recently enacted United States tariffs in 2025 are reshaping procurement strategies, architectural choices, and vendor relationships across graph analytics deployments
The introduction of new tariffs in the United States in 2025 has introduced a layer of complexity for organizations that source hardware, software, and managed services across international supply chains. Tariff adjustments impact procurement strategies by altering the cost calculus for infrastructure components such as high-performance servers, network equipment, and specialist accelerators that are often used to optimize graph workloads. As a result, procurement teams and architects must re-evaluate total cost of ownership and consider alternative sourcing, local manufacturing partnerships, or architectural designs that reduce dependency on tariff-affected imports.
In addition to hardware, tariffs can influence vendor selection and contract structures for both platform software and solution software, prompting firms to negotiate revised terms, seek localized licensing arrangements, or shift toward cloud-based consumption models to mitigate capital expenditure exposure. Professional services and managed services engagements may also be affected when cross-border labor and subcontracting costs change. Consequently, risk management teams should embed tariff scenario planning into procurement playbooks and technology roadmaps to preserve deployment timelines and avoid unintended cost escalations. By proactively modeling these implications, leaders can maintain momentum on strategic initiatives while managing financial and operational disruptions.
Comprehensive segmentation insights that illuminate how component, organization size, deployment model, application, and industry vertical drive differentiated requirements and buying behavior
Understanding segmentation is essential to tailoring product strategies and go-to-market approaches for graph analytics solutions. When evaluating offerings based on component, it is important to differentiate between software and services, where software divides into platform software that provides core graph processing and management capabilities and solution software that packages domain-specific workflows; services encompass managed services that run and maintain deployments as well as professional services that design, integrate, and optimize implementations. Considering organization size, large enterprises often demand enterprise-grade security, compliance, and integration with legacy systems, while small and medium enterprises prioritize ease of deployment, lower total cost of ownership, and rapid time-to-value.
Deployment model choices further refine positioning: cloud deployments, whether in public cloud environments or private cloud configurations, offer elasticity and managed infrastructure benefits, whereas on-premises deployments remain relevant where data residency, latency, or regulatory constraints prevail. Application-driven segmentation highlights varied buyer motivations; customer analytics initiatives seek to map behavior and relationships to improve retention and lifetime value, fraud detection implementations focus on real-time pattern recognition and anomaly scoring, network performance management requires topological insights for root cause analysis, and risk management leverages connectivity to surface systemic vulnerabilities. Finally, industry verticals shape solution requirements and value propositions, with banking, financial services and insurance demanding stringent security and auditability, government emphasizing sovereignty and mission assurance, healthcare prioritizing patient data protection and interoperability, information technology and telecom requiring scale and telemetry integration, and retail seeking unified views of customer journeys and omnichannel fulfillment. By weaving these dimensions together, vendors and buyers can better align capabilities to use cases and procurement cycles.
Key regional dynamics and regulatory considerations across the Americas, Europe, Middle East & Africa, and Asia-Pacific that influence adoption patterns and deployment preferences
Regional dynamics play a significant role in how graph analytics solutions are adopted, regulated, and monetized. In the Americas, a combination of mature cloud adoption, a strong ecosystem of technology partners, and aggressive digital transformation programs has generated high demand for production-grade graph analytics across financial services, retail, and telecommunications. The region’s emphasis on rapid innovation and scale favors managed services and public cloud deployments, while regulatory scrutiny in certain sectors compels careful attention to data governance and privacy practices.
Across Europe, the Middle East & Africa, regulatory frameworks and data sovereignty considerations often steer organizations toward hybrid or private cloud models, and public-sector investments create opportunities for government-focused solutions. In addition, a growing emphasis on cross-border data protection and interoperability influences procurement cycles and vendor accreditation. In Asia-Pacific, diverse market maturity levels produce a spectrum of adoption patterns: advanced markets prioritize real-time analytics at scale and cloud-native architectures, whereas emerging markets emphasize cost-effective, packaged solutions and local partnerships that accelerate deployment. Trade policy shifts and regional supply chain strategies also influence infrastructure decisions, creating nuanced opportunities for vendors that can localize offerings and support multi-jurisdictional compliance. These regional contours shape product roadmaps and go-to-market priorities for solution providers aiming to serve global customers with differentiated needs.
Critical competitive and partnership dynamics among platform innovators, solution specialists, and service providers that determine execution capabilities and customer outcomes
Competitive dynamics in the graph analytics sector are driven by a blend of platform innovators, specialized solution providers, and systems integrators that bring domain expertise to complex deployments. Leading companies are investing in performance optimizations, native graph processing engines, and developer-friendly tooling, while also expanding through partnerships and verticalized offerings to meet industry-specific requirements. Strategic moves include bundling analytics capabilities with machine learning feature stores, enhancing connectors to data lakes and streaming platforms, and offering managed cloud services that abstract operational complexity for customers.
At the same time, partnerships between technology vendors and channel partners are enabling faster implementations and localized support, which is particularly important for regulated sectors and international customers. Talent and professional services remain differentiators, as the successful operationalization of graph analytics often depends on careful schema design, feature engineering, and integration with enterprise workflows. Organizations that combine strong platform capabilities with consultative services and a clear set of domain templates are best positioned to accelerate customer outcomes and deepen account relationships.
Actionable recommendations for leaders to align deployment strategy, governance, and commercial negotiations so graph analytics delivers measurable business impact and resilience
Industry leaders should prioritize a pragmatic roadmap that aligns technology choices with measurable business outcomes and risk tolerances. Begin by establishing clear use-case prioritization criteria that balance strategic impact and implementation complexity, ensuring initiatives such as fraud detection and customer analytics receive focused investment and executive sponsorship. Concurrently, adopt an architecture that supports hybrid deployment patterns to reconcile regulatory and latency constraints with the agility benefits of public cloud. This includes designing for interoperability with existing data lakes, streaming platforms, and identity infrastructure to reduce integration friction and accelerate time-to-value.
Invest in skills and governance: build cross-functional teams that pair data engineers with domain experts and security personnel, and institute rigorous data governance practices that protect sensitive information while enabling analytic exploration. Negotiate flexible commercial terms with vendors to accommodate tariff and supply chain volatility, and consider managed services where operational maturity is a barrier. Finally, measure success through leading indicators tied to operational performance, risk reduction, or revenue impact, and iterate rapidly based on feedback loops. By combining strategic focus, architectural flexibility, and strong execution disciplines, leaders can capture disproportionate value from graph analytics investments.
Transparent research methodology describing how practitioner interviews, technical literature, and scenario planning were integrated to produce actionable and reproducible insights
This research synthesized primary and secondary inputs to construct a robust, practice-oriented view of the graph analytics landscape. Primary inputs included interviews with practitioners across industries, technical leads responsible for deployments, and procurement professionals who manage sourcing and vendor relationships. These conversations provided firsthand perspectives on deployment challenges, performance requirements, and integration patterns. Secondary inputs comprised peer-reviewed publications, vendor technical documentation, standards and interoperability guidelines, and publicly available regulatory frameworks that influence deployment choices. Triangulation across these inputs helped validate technology trends and use-case adoption patterns.
Analytic methods emphasized qualitative synthesis and comparative analysis rather than predictive modeling. Use-case mapping was employed to connect technical capabilities to operational outcomes, and vendor capability matrices were constructed to surface differentiators in performance, interoperability, and service delivery. Scenario planning exercises were used to assess the potential impact of external factors such as tariff changes, regulatory shifts, and supply chain disruptions on procurement and architecture decisions. Throughout the process, emphasis was placed on reproducibility and transparency of assumptions so that readers can adapt findings to their organizational context.
Concluding synthesis that underscores graph analytics as a strategic capability and the organizational practices required to translate analytical insight into operational outcomes
Graph analytics represents a durable shift in how organizations model complexity and derive actionable intelligence from relationships. The technology’s maturation-driven by improvements in distributed processing, tighter integration with machine learning, and the rise of domain-focused solutions-has expanded its relevance from exploratory projects to operational systems that support fraud detection, network performance, customer analytics, and risk management. Regional variance, regulatory constraints, and procurement dynamics such as tariff-induced cost pressures create a nuanced environment that rewards vendors and adopters who can offer configurable, compliant, and service-rich solutions.
Going forward, success will depend on aligning use-case selection, deployment models, and governance frameworks so that analytical outputs can be trusted and operationalized. Leaders who invest in cross-functional teams, hybrid architectures, and vendor engagements that include both platform capabilities and consultative services will be best positioned to translate graph-derived insights into measurable outcomes. In short, graph analytics is no longer an experimental curiosity but a strategic capability that, when implemented thoughtfully, strengthens operational resilience and competitive differentiation.
Please Note: PDF & Excel + Online Access - 1 Year
An authoritative introduction explaining why graph analytics has emerged as a strategic imperative for modern enterprises seeking relationship-aware intelligence and risk mitigation
Graph analytics is reshaping how organizations understand complex relationships, detect anomalies, and surface latent patterns across interconnected datasets. As enterprises increasingly confront challenges such as fraud, network performance degradation, and customer churn, graph technologies provide a natural way to model entities and their relationships, enabling richer context and faster insight generation. Over the past few years, improvements in graph processing frameworks, query languages, and integration with machine learning have accelerated adoption across both legacy and cloud-native environments, driving a shift from experimental pilots to production deployments.
Moreover, the confluence of scalable cloud infrastructures and specialized graph databases has reduced barriers to entry, allowing organizations of various sizes to test and deploy graph-driven solutions with lower upfront costs. This technological evolution is complemented by a growing body of practical use cases where relationship-aware analytics deliver measurable operational value. In turn, decision-makers are prioritizing investments that can strengthen security postures, optimize network operations, and enhance customer intelligence. Consequently, executives must view graph analytics not merely as a niche capability but as a strategic enabler of data-driven differentiation and resilience.
A forward-looking synthesis of the technological, architectural, and ecosystem shifts that are accelerating enterprise adoption and operationalization of graph analytics
The landscape for graph analytics is experiencing several transformative shifts that are redefining how value is created and captured. First, there is a move from single-node, research-oriented implementations to distributed, production-grade architectures that support real-time inference across highly connected datasets. This transition enables organizations to operationalize graph insights for mission-critical functions, such as fraud detection and network optimization, where latency and scale are non-negotiable. Second, graph analytics is increasingly integrated with machine learning pipelines, allowing feature engineering to leverage topological properties and community structures for superior predictive performance.
In parallel, deployment models are diversifying: some enterprises prefer cloud-native managed services that offer elasticity and rapid provisioning, while others retain on-premises or private cloud deployments to meet regulatory and latency requirements. Interoperability has become a focal point, with efforts to standardize connectors, APIs, and query interfaces to bridge graph stores with data lakes, streaming platforms, and analytics toolchains. Finally, the vendor ecosystem is maturing, with specialized platform software and broader solution suites that provide domain templates and prebuilt workflows, thereby shortening time-to-value for industry-specific applications. These shifts collectively point toward a future where graph capabilities are embedded across organizational processes rather than isolated in analytics labs.
A rigorous analysis of how recently enacted United States tariffs in 2025 are reshaping procurement strategies, architectural choices, and vendor relationships across graph analytics deployments
The introduction of new tariffs in the United States in 2025 has introduced a layer of complexity for organizations that source hardware, software, and managed services across international supply chains. Tariff adjustments impact procurement strategies by altering the cost calculus for infrastructure components such as high-performance servers, network equipment, and specialist accelerators that are often used to optimize graph workloads. As a result, procurement teams and architects must re-evaluate total cost of ownership and consider alternative sourcing, local manufacturing partnerships, or architectural designs that reduce dependency on tariff-affected imports.
In addition to hardware, tariffs can influence vendor selection and contract structures for both platform software and solution software, prompting firms to negotiate revised terms, seek localized licensing arrangements, or shift toward cloud-based consumption models to mitigate capital expenditure exposure. Professional services and managed services engagements may also be affected when cross-border labor and subcontracting costs change. Consequently, risk management teams should embed tariff scenario planning into procurement playbooks and technology roadmaps to preserve deployment timelines and avoid unintended cost escalations. By proactively modeling these implications, leaders can maintain momentum on strategic initiatives while managing financial and operational disruptions.
Comprehensive segmentation insights that illuminate how component, organization size, deployment model, application, and industry vertical drive differentiated requirements and buying behavior
Understanding segmentation is essential to tailoring product strategies and go-to-market approaches for graph analytics solutions. When evaluating offerings based on component, it is important to differentiate between software and services, where software divides into platform software that provides core graph processing and management capabilities and solution software that packages domain-specific workflows; services encompass managed services that run and maintain deployments as well as professional services that design, integrate, and optimize implementations. Considering organization size, large enterprises often demand enterprise-grade security, compliance, and integration with legacy systems, while small and medium enterprises prioritize ease of deployment, lower total cost of ownership, and rapid time-to-value.
Deployment model choices further refine positioning: cloud deployments, whether in public cloud environments or private cloud configurations, offer elasticity and managed infrastructure benefits, whereas on-premises deployments remain relevant where data residency, latency, or regulatory constraints prevail. Application-driven segmentation highlights varied buyer motivations; customer analytics initiatives seek to map behavior and relationships to improve retention and lifetime value, fraud detection implementations focus on real-time pattern recognition and anomaly scoring, network performance management requires topological insights for root cause analysis, and risk management leverages connectivity to surface systemic vulnerabilities. Finally, industry verticals shape solution requirements and value propositions, with banking, financial services and insurance demanding stringent security and auditability, government emphasizing sovereignty and mission assurance, healthcare prioritizing patient data protection and interoperability, information technology and telecom requiring scale and telemetry integration, and retail seeking unified views of customer journeys and omnichannel fulfillment. By weaving these dimensions together, vendors and buyers can better align capabilities to use cases and procurement cycles.
Key regional dynamics and regulatory considerations across the Americas, Europe, Middle East & Africa, and Asia-Pacific that influence adoption patterns and deployment preferences
Regional dynamics play a significant role in how graph analytics solutions are adopted, regulated, and monetized. In the Americas, a combination of mature cloud adoption, a strong ecosystem of technology partners, and aggressive digital transformation programs has generated high demand for production-grade graph analytics across financial services, retail, and telecommunications. The region’s emphasis on rapid innovation and scale favors managed services and public cloud deployments, while regulatory scrutiny in certain sectors compels careful attention to data governance and privacy practices.
Across Europe, the Middle East & Africa, regulatory frameworks and data sovereignty considerations often steer organizations toward hybrid or private cloud models, and public-sector investments create opportunities for government-focused solutions. In addition, a growing emphasis on cross-border data protection and interoperability influences procurement cycles and vendor accreditation. In Asia-Pacific, diverse market maturity levels produce a spectrum of adoption patterns: advanced markets prioritize real-time analytics at scale and cloud-native architectures, whereas emerging markets emphasize cost-effective, packaged solutions and local partnerships that accelerate deployment. Trade policy shifts and regional supply chain strategies also influence infrastructure decisions, creating nuanced opportunities for vendors that can localize offerings and support multi-jurisdictional compliance. These regional contours shape product roadmaps and go-to-market priorities for solution providers aiming to serve global customers with differentiated needs.
Critical competitive and partnership dynamics among platform innovators, solution specialists, and service providers that determine execution capabilities and customer outcomes
Competitive dynamics in the graph analytics sector are driven by a blend of platform innovators, specialized solution providers, and systems integrators that bring domain expertise to complex deployments. Leading companies are investing in performance optimizations, native graph processing engines, and developer-friendly tooling, while also expanding through partnerships and verticalized offerings to meet industry-specific requirements. Strategic moves include bundling analytics capabilities with machine learning feature stores, enhancing connectors to data lakes and streaming platforms, and offering managed cloud services that abstract operational complexity for customers.
At the same time, partnerships between technology vendors and channel partners are enabling faster implementations and localized support, which is particularly important for regulated sectors and international customers. Talent and professional services remain differentiators, as the successful operationalization of graph analytics often depends on careful schema design, feature engineering, and integration with enterprise workflows. Organizations that combine strong platform capabilities with consultative services and a clear set of domain templates are best positioned to accelerate customer outcomes and deepen account relationships.
Actionable recommendations for leaders to align deployment strategy, governance, and commercial negotiations so graph analytics delivers measurable business impact and resilience
Industry leaders should prioritize a pragmatic roadmap that aligns technology choices with measurable business outcomes and risk tolerances. Begin by establishing clear use-case prioritization criteria that balance strategic impact and implementation complexity, ensuring initiatives such as fraud detection and customer analytics receive focused investment and executive sponsorship. Concurrently, adopt an architecture that supports hybrid deployment patterns to reconcile regulatory and latency constraints with the agility benefits of public cloud. This includes designing for interoperability with existing data lakes, streaming platforms, and identity infrastructure to reduce integration friction and accelerate time-to-value.
Invest in skills and governance: build cross-functional teams that pair data engineers with domain experts and security personnel, and institute rigorous data governance practices that protect sensitive information while enabling analytic exploration. Negotiate flexible commercial terms with vendors to accommodate tariff and supply chain volatility, and consider managed services where operational maturity is a barrier. Finally, measure success through leading indicators tied to operational performance, risk reduction, or revenue impact, and iterate rapidly based on feedback loops. By combining strategic focus, architectural flexibility, and strong execution disciplines, leaders can capture disproportionate value from graph analytics investments.
Transparent research methodology describing how practitioner interviews, technical literature, and scenario planning were integrated to produce actionable and reproducible insights
This research synthesized primary and secondary inputs to construct a robust, practice-oriented view of the graph analytics landscape. Primary inputs included interviews with practitioners across industries, technical leads responsible for deployments, and procurement professionals who manage sourcing and vendor relationships. These conversations provided firsthand perspectives on deployment challenges, performance requirements, and integration patterns. Secondary inputs comprised peer-reviewed publications, vendor technical documentation, standards and interoperability guidelines, and publicly available regulatory frameworks that influence deployment choices. Triangulation across these inputs helped validate technology trends and use-case adoption patterns.
Analytic methods emphasized qualitative synthesis and comparative analysis rather than predictive modeling. Use-case mapping was employed to connect technical capabilities to operational outcomes, and vendor capability matrices were constructed to surface differentiators in performance, interoperability, and service delivery. Scenario planning exercises were used to assess the potential impact of external factors such as tariff changes, regulatory shifts, and supply chain disruptions on procurement and architecture decisions. Throughout the process, emphasis was placed on reproducibility and transparency of assumptions so that readers can adapt findings to their organizational context.
Concluding synthesis that underscores graph analytics as a strategic capability and the organizational practices required to translate analytical insight into operational outcomes
Graph analytics represents a durable shift in how organizations model complexity and derive actionable intelligence from relationships. The technology’s maturation-driven by improvements in distributed processing, tighter integration with machine learning, and the rise of domain-focused solutions-has expanded its relevance from exploratory projects to operational systems that support fraud detection, network performance, customer analytics, and risk management. Regional variance, regulatory constraints, and procurement dynamics such as tariff-induced cost pressures create a nuanced environment that rewards vendors and adopters who can offer configurable, compliant, and service-rich solutions.
Going forward, success will depend on aligning use-case selection, deployment models, and governance frameworks so that analytical outputs can be trusted and operationalized. Leaders who invest in cross-functional teams, hybrid architectures, and vendor engagements that include both platform capabilities and consultative services will be best positioned to translate graph-derived insights into measurable outcomes. In short, graph analytics is no longer an experimental curiosity but a strategic capability that, when implemented thoughtfully, strengthens operational resilience and competitive differentiation.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
190 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Real-time fraud detection optimization using graph analytics and machine learning
- 5.2. Integration of graph databases with AI-driven recommendation engines for personalized marketing
- 5.3. Deployment of graph analytics for supply chain risk forecasting and mitigation strategies
- 5.4. Evolution of open source graph analytics frameworks accelerating community-driven innovation
- 5.5. Adoption of property graph modeling standards to enhance interoperability across platforms
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Graph Analytics Market, by Component
- 8.1. Services
- 8.1.1. Managed Services
- 8.1.2. Professional Services
- 8.2. Software
- 9. Graph Analytics Market, by Organization Size
- 9.1. Large Enterprises
- 9.2. Small And Medium Enterprises
- 10. Graph Analytics Market, by Deployment Model
- 10.1. Cloud
- 10.1.1. Private Cloud
- 10.1.2. Public Cloud
- 10.2. On Premises
- 11. Graph Analytics Market, by Application
- 11.1. Customer Analytics
- 11.2. Fraud Detection
- 11.3. Network Performance Management
- 11.4. Risk Management
- 12. Graph Analytics Market, by Industry Vertical
- 12.1. Banking Financial Services And Insurance
- 12.2. Government
- 12.3. Healthcare
- 12.4. Information Technology And Telecom
- 12.5. Retail
- 13. Graph Analytics Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Graph Analytics Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Graph Analytics Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Amazon Web Services, Inc.
- 16.3.2. ArangoDB GmbH
- 16.3.3. Cambridge Semantics, Inc.
- 16.3.4. DataStax, Inc.
- 16.3.5. Google LLC
- 16.3.6. Graphistry, Inc.
- 16.3.7. International Business Machines Corporation
- 16.3.8. Linkurious SAS
- 16.3.9. MarkLogic Corporation
- 16.3.10. Microsoft Corporation
- 16.3.11. Neo4j, Inc.
- 16.3.12. NVIDIA Corporation
- 16.3.13. Oracle Corporation
- 16.3.14. SAP SE
- 16.3.15. Teradata Corporation
- 16.3.16. TIBCO Software Inc.
- 16.3.17. TigerGraph, Inc.
- 16.3.18. Tom Sawyer Software, Inc.
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