Agentic AI In Semantic Layer And Knowledge Graph Market Size and Share
Market Overview
| Study Period | 2019 - 2030 |
|---|---|
| Market Size (2025) | USD 0.85 Billion |
| Market Size (2030) | USD 2.83 Billion |
| Growth Rate (2025 - 2030) | 27.15% CAGR |
| Fastest Growing Market | Asia Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0. |
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Agentic AI In Semantic Layer And Knowledge Graph Market Analysis by Mordor Intelligence
The agentic AI in semantic layer and knowledge graph market size stands at USD 0.85 billion in 2025 and is forecast to reach USD 2.83 billion by 2030, translating into a 27.15% CAGR over the period. Growing enterprise urgency to deploy autonomous agents that can reason over well-structured knowledge assets rather than only parametric learning from large language models fuels this rise. Software components keep their dominant position, yet advisory and integration services outpace them in growth as companies look for hands-on support. Cloud deployments still command the lion’s share of implementations, although on-premises roll-outs are expanding faster as data-sovereignty concerns mount. North America remains the revenue leader, but Asia-Pacific’s public-sector AI initiatives and manufacturing digitalization programs push it to the top of the growth leaderboard. Competitive intensity is heightening as graph database incumbents secure record funding and hyperscale clouds embed graph services natively.
Key Report Takeaways
- By component, software accounted for 68.2% of the agentic AI in the semantic layer and knowledge graph market share in 2024, while services are advancing at a 27.8% CAGR through 2030.
- By knowledge-graph type, enterprise knowledge graphs held a 52.3% share of the agentic AI in the semantic layer and knowledge graph market size in 2024, whereas domain-specific graphs are widening at a 29.4% CAGR.
- By application, customer and 360-view analytics led with 24.7% revenue share in 2024; conversational and agentic AI assistants are projected to expand at a 34.1% CAGR.
- By deployment mode, cloud installations captured 71.5% of 2024 revenue, yet on-premises configurations are growing at a 32.5% CAGR.
- By end-use industry, BFSI dominated with a 31.2% share in 2024, while healthcare and life sciences are slated for a 30.7% CAGR.
- By geography, North America contributed 38.9% of 2024 revenue; Asia-Pacific is forecast to post a 28.9% CAGR to 2030.
Global Agentic AI In Semantic Layer And Knowledge Graph Market Trends and Insights
Drivers Impact Analysis
| Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Generative-AI pushes for Retrieval-Augmented Generation (RAG) workflows | +6.2% | Global, with concentration in North America and the EU | Short term (≤ 2 years) |
| Exploding volumes of connected enterprise data | +5.8% | Global, with Asia-Pacific manufacturing leading adoption | Medium term (2-4 years) |
| Cloud-native graph platforms lower the total cost of ownership | +4.1% | North America and the EU are primary, Asia-Pacific is emerging | Medium term (2-4 years) |
| Regulatory and risk-compliance demand in BFSI | +3.7% | Global, with EU GDPR and US state privacy laws driving | Long term (≥ 4 years) |
| Model-Context-Protocol (MCP) standardization unlocks plug-and-play layers | +2.9% | Global, with enterprise software vendors leading | Short term (≤ 2 years) |
| VC funding boom in domain-specific semantic-layer start-ups | +2.1% | North America and EU concentration, expanding to the Asia-Pacific | Short term (≤ 2 years) |
| Source: Mordor Intelligence | |||
Generative-AI Push for Retrieval-Augmented Generation (RAG) Workflows
Enterprises are pivoting from plain prompt engineering toward RAG architectures that couple large language models with verified organizational knowledge graphs. Neo4j’s integration with Azure OpenAI Service allows teams to ground generative outputs in trustworthy data, mitigating hallucinations that stall adoption in regulated industries. TigerGraph’s January 2025 TigerVector release merges vector search with graph queries, letting one platform manage unstructured embeddings and structured relationships.[1]TigerGraph, “TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs,” tigergraph.com Early pilots at global banks indicate 40% shorter compliance reviews when GraphRAG pipelines replace document retrieval systems. These wins reinforce management's appetite for semantic layers tailored to agentic AI assistants.
Exploding Volumes of Connected Enterprise Data
IoT roll-outs, multi-cloud environments, and digital supply chains flood firms with relationship-rich data that traditional warehouses cannot model. ABB consolidated feeds from 40 ERP systems into a unified semantic layer to unlock millions of USD in cost savings. Siemens Energy used metaphactory to trim 1,500 manual hours in its first year while optimizing turbine-spares logistics.[2]Metaphacts, “Siemens Energy accelerates application development with metaphactory Knowledge Graph,” metaphacts.com Automotive leaders such as Jaguar Land Rover cut supply-chain query times from 3 weeks to 45 minutes after implementing TigerGraph. Such returns validate investments that underpin agentic AI reasoning across complex asset, process, and supplier networks.
Cloud-Native Graph Platforms Lower Total Cost of Ownership
Elastic architectures and managed services reduce the skills burden and infrastructure outlay linked to graph workloads. AWS reports up to 40% savings for I/O-intensive graph traversals using Aurora I/O-Optimized configurations. Neo4j Aura removes routine database administration duties, matching capacity to query peaks. Serverless options such as Neptune Serverless further align spend with variable semantic-layer traffic typical in conversational AI use cases. Firms employing selective entity extraction and incremental updates lower RAG indexing costs while retaining accuracy.
Regulatory and Risk-Compliance Demand in BFSI
Financial institutions face strict mandates for explainability, data lineage, and privacy. GDPR and emerging US state laws force banks to map personal information flows precisely, a task that knowledge graphs automate. The forthcoming EU AI Act will require risk assessments for high-impact systems, encouraging BFSI incumbents to embed semantic layers for audit trails and algorithmic transparency. Morgan Stanley’s GPT-4 knowledge-management deployment relies on graph-backed explanations to satisfy internal compliance teams.
Restraints Impact Analysis
| Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Scarcity of graph data engineering talent | -4.3% | Global, with acute shortages in the Asia-Pacific emerging markets | Long term (≥ 4 years) |
| Dual-standard friction (RDF vs. property graph) | -2.8% | Global, with enterprise software vendors most affected | Medium term (2-4 years) |
| High upfront licensing and integration costs | -2.1% | Global, with SME adoption particularly affected | Medium term (2-4 years) |
| IP-licensing uncertainty around open-source graph query languages | -1.4% | Global, with enterprise legal departments most concerned | Long term (≥ 4 years) |
| Source: Mordor Intelligence | |||
Scarcity of Graph-Data Engineering Talent
The demand for Cypher, SPARQL, and emerging GQL skills far exceeds supply. Salary packages topping USD 1 million for senior graph specialists highlight the shortage. Asia-Pacific enterprises struggle the most as rapid AI adoption outpaces regional training pipelines, leaving thousands of roles open. German industry groups forecast 780,000 unfilled tech positions by 2026, with graph engineering among the hardest to staff. Corporate training initiatives help, but rarely reach deep semantic-modelling expertise, prolonging project timelines.
Dual-Standard Friction (RDF vs Property Graph)
Enterprises hesitate when platform choice appears to lock them into one standard. RDF brings ontology rigor, yet property graphs deliver speed for operational analytics. RDF and ISO GQL seek convergence, but vendor timelines vary, so architects building hybrid stacks face costly connectors and duplicated data. Proprietary extensions add further fragmentation, raising switching barriers and slowing broad adoption.
Segment Analysis
By Component: Services Accelerate Despite Software Dominance
The software slice of the agentic AI in semantic layer and knowledge graph market generated 68.2% of 2024 revenue, driven by recurring subscriptions for graph databases and semantic engines. Services, however, register a 27.8% CAGR as enterprises lean on specialist consultancies to weave graphs into legacy stacks. Integration partners command premium fees because success hinges on nuanced ontology design and secure pipeline orchestration.
Implementation roadmaps often pair platform licenses with multi-year support retainers. Vendors respond by packaging reference ontologies and low-code tooling that lower the threshold for in-house teams, yet demand for external expertise remains robust. This dynamic positions services to keep chipping away at software’s revenue share without toppling its primacy.
Note: Segment shares of all individual segments available upon report purchase
By Knowledge-Graph Type: Domain-Specific Graphs Drive Innovation
Enterprise knowledge graphs held 52.3% of 2024 spending, reflecting firms’ need for broad, cross-function repositories. Domain-specific alternatives now post a 29.4% CAGR thanks to laser-focused ROI in niches such as clinical trials or semiconductor yields. Organizations value a tight scope because outcomes materialize quickly and models stay manageable.
Web-scale graphs delivered by hyperscalers continue to grow but at steadier rates, often serving as external context layers rather than core reasoning engines. Midsize firms increasingly blend bought-in open-web triples with proprietary domain graphs to balance breadth and depth, extending overall knowledge coverage without inflating maintenance budgets.
By Application: Agentic AI Assistants Lead Growth Trajectory
Customer and 360-view analytics retained 24.7% of 2024 revenue as companies unify omnichannel behavior into single records. Agentic AI assistants, though, are scaling at 34.1% CAGR as executives green-light autonomous systems that can take action rather than just report. Early deployments show assistants trimming call-center handling times and orchestrating complex workflows like invoice reconciliation.
Fraud detection remains a steady generator of contract renewals, given graphs’ aptitude for highlighting hidden relationships. Recommendation engines keep pace as retailers chase hyper-personalization gains. Knowledge discovery platforms round out demand, particularly in R&D-heavy verticals where semantic search boosts researcher productivity.
By Deployment Mode: On-Premises Growth Reflects Data Sovereignty Concerns
Cloud stood at 71.5% of 2024 spending, yet on-premises installations clock a 32.5% CAGR as privacy regimes tighten. European banks and US healthcare providers push sensitive workloads into private clusters while retaining cloud sandboxes for prototyping.
Hybrid architectures combining managed services and edge nodes are emerging. Firms place low-risk inference tasks on serverless endpoints while keeping raw datasets in-house. This mix adds operational complexity but satisfies regulators and finance chiefs alike, balancing cost efficiency with governance obligations.
By End-Use Industry: Healthcare Accelerates Beyond BFSI Leadership
BFSI produced 31.2% of the total 2024 revenue, propelled by compliance and risk use cases. Healthcare and life sciences now outstrip every other vertical at a 30.7% CAGR. Pharmaceutical giants deploy domain graphs to shorten molecule discovery cycles, and hospital systems exploit semantic layers for holistic patient records.
Retail follows closely as recommendation algorithms drive basket sizes. Manufacturing taps graphs for supply-chain visibility and predictive quality analytics. Government uptake quickens, with agencies linking disparate citizen databases to improve service delivery while honoring data-sovereignty statutes.
Geography Analysis
North America generated 38.9% of 2024 revenue, underpinned by Silicon Valley’s vibrant start-up pipeline and New York’s finance-driven adoption. Neo4j’s USD 325 million Series F round, the largest ever for a database vendor, exemplifies investor conviction.[3]Neo4j, “Neo4j Announces USD 325 Million Series F Investment, the Largest in Database History,” neo4j.com AWS, Microsoft, and Google integrate graph services with AI stacks, lowering entry hurdles and anchoring regional dominance.
Asia-Pacific is advancing at a 28.9% CAGR, fuelled by Beijing’s “AI-Plus” programs, Tokyo’s manufacturing digitization push, and India’s burgeoning services sector. Local vendors localize ontologies for Mandarin, Japanese, and Hindi datasets, widening addressable markets. Government incentives subsidize pilot projects in smart-factory and smart-city configurations that demand semantic interoperability.
Europe maintains steady growth under GDPR and the forthcoming AI Act, which prioritizes explainability. German automakers deploy knowledge graphs in production planning, while London-based fintechs adopt graphs for real-time anti-money-laundering checks. Post-Brexit data-transfer rules complicate cross-border implementations, nudging multinationals toward hybrid deployments that split data across EU-based and UK-based clusters.
Competitive Landscape
The semantic layer and knowledge graph industry sit in the middle of the concentration spectrum. Neo4j tops the leaderboard, surpassing USD 200 million in annual recurring revenue in late 2024 and entering a deep co-development pact with AWS to embed graph reasoning in generative workflows. TigerGraph differentiates through massive-parallel analytics and now adds vector search to court RAG workloads. Stardog leans on a semantic-web pedigree and enterprise ontologies to win regulated accounts.
Strategic acquisitions reshape the field. Samsung’s July 2024 purchase of Oxford Semantic Technologies injects on-device knowledge graphs into consumer electronics. Altair folded Cambridge Semantics into its data-fabric suite to simplify AI data access. Databricks acquired Neon for USD 1 billion to underpin its AI agent framework with serverless Postgres capabilities.[4]Databricks, “Databricks Acquires Neon in USD 1B Database Deal,” databricks.com
Hyperscalers democratize access with managed graph offerings, but specialist start-ups push innovation at the edge of vertical specificity and AI-native design. WisdomAI and Illumex raise fresh capital to tackle chemical process knowledge and natural-language data cataloging, respectively. Price competition intensifies on commodity storage, moving the battleground to query speed, ML integration, and developer experience.
Agentic AI In Semantic Layer And Knowledge Graph Industry Leaders
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Neo4j, Inc.
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TigerGraph, Inc.
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Stardog Union, Inc.
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Ontotext AD
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AtScale, Inc.
- *Disclaimer: Major Players sorted in no particular order
Recent Industry Developments
- January 2025: TigerGraph integrated TigerVector into v4.2, uniting vector and graph search for RAG scenarios.
- December 2024: Anthropic released the open Model Context Protocol to streamline AI-tool interoperability.
- November 2024: Neo4j surpassed USD 200 million ARR and deepened AWS collaboration for hallucination-free generative AI.
- July 2024: Samsung Electronics acquired Oxford Semantic Technologies for on-device knowledge-graph capabilities.
Global Agentic AI In Semantic Layer And Knowledge Graph Market Report Scope
| Software (Graph DB, Semantic Layer Engine, Tooling) |
| Services (Integration, Consulting, Support) |
| Enterprise Knowledge Graph |
| Domain-specific Knowledge Graph |
| Web-scale Knowledge Graph |
| Customer and 360-view Analytics |
| Fraud Detection and Risk Management |
| Recommendation and Personalization Engines |
| Conversational / Agentic AI Assistants |
| Knowledge Discovery and Research |
| Cloud |
| On-premises |
| BFSI |
| Healthcare and Life Sciences |
| Retail and E-commerce |
| Manufacturing and Supply-chain |
| Government and Public Sector |
| North America | United States | |
| Canada | ||
| Mexico | ||
| South America | Brazil | |
| Argentina | ||
| Rest of South America | ||
| Europe | Germany | |
| United Kingdom | ||
| France | ||
| Italy | ||
| Spain | ||
| Russia | ||
| Rest of Europe | ||
| Asia-Pacific | China | |
| Japan | ||
| India | ||
| South Korea | ||
| Rest of Asia-Pacific | ||
| Middle East and Africa | Middle East | United Arab Emirates |
| Saudi Arabia | ||
| Turkey | ||
| Qatar | ||
| Rest of Middle East | ||
| Africa | South Africa | |
| Nigeria | ||
| Egypt | ||
| Rest of Africa | ||
| By Component | Software (Graph DB, Semantic Layer Engine, Tooling) | ||
| Services (Integration, Consulting, Support) | |||
| By Knowledge-Graph Type | Enterprise Knowledge Graph | ||
| Domain-specific Knowledge Graph | |||
| Web-scale Knowledge Graph | |||
| By Application | Customer and 360-view Analytics | ||
| Fraud Detection and Risk Management | |||
| Recommendation and Personalization Engines | |||
| Conversational / Agentic AI Assistants | |||
| Knowledge Discovery and Research | |||
| By Deployment Mode | Cloud | ||
| On-premises | |||
| By End-use Industry | BFSI | ||
| Healthcare and Life Sciences | |||
| Retail and E-commerce | |||
| Manufacturing and Supply-chain | |||
| Government and Public Sector | |||
| By Geography | North America | United States | |
| Canada | |||
| Mexico | |||
| South America | Brazil | ||
| Argentina | |||
| Rest of South America | |||
| Europe | Germany | ||
| United Kingdom | |||
| France | |||
| Italy | |||
| Spain | |||
| Russia | |||
| Rest of Europe | |||
| Asia-Pacific | China | ||
| Japan | |||
| India | |||
| South Korea | |||
| Rest of Asia-Pacific | |||
| Middle East and Africa | Middle East | United Arab Emirates | |
| Saudi Arabia | |||
| Turkey | |||
| Qatar | |||
| Rest of Middle East | |||
| Africa | South Africa | ||
| Nigeria | |||
| Egypt | |||
| Rest of Africa | |||
Key Questions Answered in the Report
What is the current size of the agentic AI in semantic layer and knowledge graph market?
The agentic AI in semantic layer and knowledge graph market size is valued at USD 0.85 billion in 2025.
How fast will the market grow over the next five years?
It is projected to advance at a 27.15% CAGR, reaching USD 2.83 billion by 2030.
Which component segment is expanding the quickest?
Services are growing at a 27.8% CAGR as enterprises seek integration and support expertise.
Why are semantic layers critical for agentic AI assistants?
They ground large language models in factual organizational knowledge, improving accuracy and reducing hallucinations that impede regulated-industry adoption.
Which region is forecast to record the highest growth?
Asia-Pacific is poised for a 28.9% CAGR through 2030, outpacing all other regions due to government AI initiatives and manufacturing digitalization.
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