iFenxi has released the "2026 China AI Data Infrastructure Market Size Report". According to iFenxi's estimates, the market size of China's AI data infrastructure will reach approximately RMB 45 billion in 2025 and is expected to hit RMB 198.4 billion by 2029, with a compound annual growth rate (CAGR) of about 45% from 2025 to 2029.
The report suggests that as large models and Agents integrate into operational analysis, knowledge retrieval, process collaboration, and business execution, enterprise data systems must further enhance their capabilities in semantic understanding, data invocation, permission auditing, and context management. In the future, the competitive focus of enterprise AI data infrastructure will center on the "enterprise AI data entry point," with ontology platforms, multimodal data management, high-quality datasets, and Agent data invocation becoming key drivers of market growth.
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01、Core Perspectives
Enterprise AI construction is driving a systematic upgrade of data infrastructure. Models, computing power, and applications have initially become the focus of enterprise investment. However, in real business scenarios, whether AI can accurately understand metric calibers, business objects, process rules, and permission boundaries is becoming a critical variable affecting application outcomes. Consequently, the importance of enterprise AI data infrastructure is rising.
According to iFenxi's estimates, the market size of China's AI data infrastructure will be approximately RMB 45 billion in 2025 and is expected to reach RMB 198.4 billion by 2029, with a CAGR of about 45% during this period. The growth of this market mainly stems from the upgrade of existing data platforms, the construction of ontology platforms, the assetization of multimodal data, the supply of high-quality datasets, and the release of Agent data invocation demands. iFenxi believes that the core of competition in AI data infrastructure lies in capturing the "enterprise AI data entry point." Enterprise AI applications need to accomplish data access, semantic understanding, tool invocation, permission control, and process auditing through a unified entry point.
In the future, the value of AI data infrastructure will be concentrated in its unified hosting capacity for data entry points, semantic entry points, and Agent invocation entry points. The ontology platform will become a key capability for enterprises to accumulate semantic assets and support AI in understanding business. Enterprises need to structure knowledge such as processes, metric calibers, and business rules to form semantic assets that can be recognized, invoked, and reused by AI.
As AI applications evolve from Q&A to analysis, collaboration, and execution, the ontology platform will become the core capability layer in enterprise AI data infrastructure that undertakes business semantics and action rules. Overall, the construction focus of enterprise AI data infrastructure is shifting towards ontology platforms, driving accelerated growth in the construction of AI-native capabilities such as multimodal data management. Future incremental investments will increasingly target business semantic modeling, assetization of unstructured data, high-quality datasets, and Agent data invocation.
02、Current Progress of China's AI Data Infrastructure
2.1 Data Consumption and Usage Patterns are the Core Changes
AI data infrastructure is a foundational data capability system built for large model applications, intelligent agent systems, and AI-native business processes. It covers capabilities such as data storage and computing, data development and governance, semantic modeling, multimodal data management, high-quality data supply, Agent data invocation, and data security governance. It is a critical support for enterprise AI applications to transition from pilot validation to production-level deployment. Compared with traditional data infrastructure, the core change in AI data infrastructure lies in data consumers and usage patterns.
In the past, enterprise data primarily served business personnel, data analysts, managers, and various digital systems, with usage patterns mainly focused on querying, analysis, reporting, and operational decision-making. In the AI era, data must also serve Agents. Agents not only need to read data but also understand business objects, metric calibers, rule constraints, and contextual relationships, invoking tools, triggering processes, and participating in business execution under controllable permissions. Therefore, the value of AI data infrastructure is no longer limited to "managing and using data" but further supports AI's understanding and invocation of enterprise data, business semantics, and action rules.
2.2 Seven Segmented Markets Deserve Attention
This report divides enterprise AI data infrastructure into the AI data resources and processing layer, the AI data governance and service layer, and the data security layer.
Figure 1: Panoramic Map of China's AI Data Infrastructure Market
Among them, AI databases and multimodal data management constitute the underlying data resources and processing capabilities, mainly addressing the data storage, computing, retrieval, and assetization of unstructured data required by enterprise AI applications; DataOps, ontology platforms, high-quality datasets, and Agent data invocation constitute the upper-level data governance and service capabilities, mainly supporting data development and governance, business semantic modeling, AI-available data supply, and intelligent agent data invocation; data security provides permission, auditing, and compliance guarantees for data access, invocation, and usage processes.
- AI Databases: Covering capabilities such as databases, data warehouses, data lakes, lakehouse architectures, graph databases, vector databases, hybrid retrieval, knowledge bases, and RAG retrieval.
- Multimodal Data Management: Covering the parsing, governance, indexing, and assetization of unstructured data such as documents, images, audio and video, drawings, logs, emails, and knowledge files.
- DataOps: Covering capabilities such as data collection, integration, development, scheduling, quality, master data, metadata, data asset management, and data servitization.
- Ontology Platforms: Covering capabilities such as metric systems, business semantic layers, knowledge graphs, ontology modeling, business object modeling, rule constraint modeling, and semantic mapping.
- Agent Data Invocation: Covering capabilities such as data APIs, tool invocation, context management, permission orchestration, invocation chain tracing, invocation auditing, and operation monitoring.
- High-Quality Datasets: Covering datasets, industry datasets, reusable data services, and data APIs for model training, fine-tuning, evaluation, knowledge enhancement, and industry intelligent agent applications.
- Data Security: Covering capabilities such as data classification and grading, permission control, identity authentication, desensitization, encryption, watermarking, access auditing, and compliance governance.
2.3 Potential Market Space of Nearly RMB 200 Billion
AI data infrastructure is becoming one of the fastest-growing and most strategically valuable infrastructure tracks in the deployment of enterprise-level AI applications. iFenxi's estimates show that the market size of China's AI data infrastructure will grow from RMB 45 billion in 2025 to RMB 198.4 billion in 2029, with a CAGR of approximately 44.9% during this period.
Figure 2: Market Size and Growth Rate of China's AI Data Infrastructure, 2025-2029E
As enterprise AI applications continue to deepen, market growth drivers will shift from one-time platform construction to the continuous upgrade of existing data systems, as well as the reinforcement of capabilities such as semantic modeling, unstructured data management, and intelligent retrieval. The driving factors for AI data infrastructure are divided into two stages. In the initial stage of enterprise AI construction, it is usually promoted in a project-based manner, focusing on building knowledge bases, upgrading databases and data platforms, and constructing basic data governance capabilities to meet the validation needs of a few AI application scenarios. Therefore, early investments have strong characteristics of one-time platform construction. When AI applications enter more business processes, data demands will continue to change.
Different departments, scenarios, and Agents have different requirements for data scope, metric calibers, permission boundaries, invocation methods, and response performance. It is difficult for enterprises to complete all preparations through one-time construction. Data infrastructure needs to be continuously adjusted, supplemented, and optimized as AI scenarios expand.
Therefore, subsequent investments in enterprise AI data infrastructure will increasingly manifest as continuous upgrades: on the one hand, adapting and transforming existing databases, data platforms, and data governance systems; on the other hand, continuously supplementing semantic, knowledge, permission, auditing, and data service capabilities around new business scenarios. Market growth will also gradually shift from early centralized construction to long-term investments driven by the diffusion of AI applications.
2.4 Ontology Platforms and Multimodal Data Management Grow Fastest
According to iFenxi's estimates, AI databases remain the largest segmented market, with the market size expected to reach RMB 81 billion in 2029, accounting for 40.8% of the overall market; the market sizes for multimodal data management, ontology platforms, and DataOps will reach RMB 39 billion, RMB 28.5 billion, and RMB 28 billion, respectively.
Figure 3: Market Size and Growth Rate of Segmented Markets in China's AI Data Infrastructure, 2025-2029E
Overall, AI databases provide scalable storage, computing, and retrieval capabilities, while ontology platforms further undertake the responsibilities of business semantic organization and knowledge association. The relationship between the two is shifting from single-point product competition to capability synergy around enterprise AI application scenarios. The ontology platform is the fastest-growing segmented market.
According to iFenxi's estimates, the market size of ontology platforms will grow from RMB 2.8 billion in 2025 to RMB 28.5 billion in 2029, with a CAGR reaching 78.6%. Behind this high growth rate is the fact that enterprises are beginning to include metric calibers, business objects, rule constraints, and process relationships in the construction scope of AI data infrastructure.
In scenarios such as operational analysis, process collaboration, and Agent execution, AI needs to invoke not only fields and documents but also business meanings, object relationships, and action rules. Ontology platforms can uniformly organize these contents into semantic assets, supporting AI in making judgments and invocations in more complex business environments. AI databases will still maintain a large market volume. Enterprise AI applications require stable data storage, computing, lakehouse architectures, graph databases, vector retrieval, hybrid retrieval, and RAG retrieval capabilities. AI databases will continue to undertake underlying data processing and knowledge retrieval demands.
However, as capabilities such as databases, lakehouses, vector retrieval, and RAG gradually enter mainstream data platforms, the capability boundaries of AI databases will tend to be standardized. For enterprise customers, the underlying data engines remain important, but their differentiation space will depend more on whether they can form synergy with upper-level semantics, ontologies, knowledge organization, and Agent invocation systems. In other words, AI databases determine whether data can be efficiently stored and retrieved, while ontology platforms further determine whether data can be understood, organized, and executed by AI.
03、Future Trends of China's AI Data Infrastructure
3.1 Enterprise AI Data Entry Point is the Key to Competition
After enterprise AI applications enter the stage of scaled deployment, the competitive focus of data infrastructure is extending from single platform capabilities to the competition for enterprise AI data entry points. The enterprise AI data entry point refers to the unified entry point for internal AI applications, intelligent agent systems, and business personnel to access, understand, invoke, and govern data.
Figure 4: Enterprise AI Data Entry Point is the Key to Competition
In the past, enterprise data infrastructure was more focused on data collection, development, governance, storage, and analysis, with the core goal of improving data management efficiency. In the AI era, enterprises not only need to manage data but also enable large models and Agents to invoke data in an environment with controllable permissions, clear semantics, and complete context.
Therefore, whoever masters the data access entry point, semantic understanding entry point, and tool invocation entry point for enterprise AI applications will more easily form a sustained influence in subsequent AI application construction.
In the future, AI data entry points will mainly be reflected in three levels: first, intelligent data querying, metric analysis, and knowledge retrieval entry points for business personnel; second, data API, tool invocation, and context management entry points for large models and Agents; third, permission, security, auditing, and trusted governance entry points for the enterprise governance system.
3.2 Ontology Platforms Accumulate Enterprise Semantic Assets
According to iFenxi's research, after enterprise AI applications enter scenarios such as operational analysis, process collaboration, and Agent execution, what data infrastructure needs to bear is no longer just data querying and result returning, but also business semantic recognition, relationship reasoning, and rule constraint expression.
Only by forming stable semantic relationships among business elements such as customers, orders, and processes can AI be supported to complete judgments, invocations, and executions in complex business environments. The core value of ontology platforms lies in transforming knowledge scattered in business systems, reports, process systems, data models, and expert experience into semantic assets that can be understood, invoked, and reused by AI. Such semantic assets include business objects, entity relationships, metric systems, rule constraints, business terms, semantic mappings, and execution contexts, forming the foundation for enterprise AI to move from "invoking data" to "understanding business." As the number of internal Agents increases, ontology platforms will also undertake more important context organization capabilities.
When executing tasks, Agents need to understand the relationships between business objects, identify invocable tools, follow permission boundaries, and complete process collaboration across different systems. Ontology platforms can provide Agents with a more stable business semantic framework, reducing the risks of models misunderstanding metrics, misreading relationships, and incorrectly invoking data.
3.3 Engineering Governance Shifts to AI-Native Capability Construction
According to iFenxi's estimates, the CAGR of the ontology platform market size from 2025 to 2029 will reach 78.6%, making it the fastest-growing segmented market in enterprise AI data infrastructure; the CAGR of multimodal data management will reach 59.7%, also maintaining rapid growth.
This indicates that enterprise data infrastructure construction is shifting from engineering governance represented by data middle platforms to AI-native capability construction represented by ontology platforms, multimodal data management, high-quality datasets, and Agent data invocation. Data middle platforms remain an important foundation for enterprise data governance and data servitization, but they mainly solve problems of data aggregation, development, governance, assetization, and servitization. AI applications further require enterprises to construct business semantics, knowledge structures, and executable contexts.
Compared with traditional data middle platforms, ontology platforms are closer to the needs of AI applications for business understanding, knowledge organization, and task execution, and are therefore becoming an important direction for the upgrade of enterprise AI data infrastructure. The growth of multimodal data management forms a synergy with ontology platforms. A large number of documents, drawings, images, audio and video, logs, emails, and knowledge files accumulated by enterprises over the long term are becoming important knowledge sources for large models and Agents.
In the future, enterprises need to parse, govern, index, and assetize these unstructured data, and establish associations with business objects, metric calibers, and process rules through ontology platforms.
04、Conclusion
Overall, enterprise AI data infrastructure is becoming an important support for enterprise intelligent construction. As large model and Agent applications enter more business scenarios, enterprise data systems need to be further upgraded from traditional data storage, development, governance, and analysis capabilities to a data understanding, data invocation, and data governance capability system oriented towards AI applications.
This report focuses on analyzing the market definition, market scope, market size, and development trends of enterprise AI data infrastructure. iFenxi believes that in the coming years, the enterprise AI data infrastructure market will maintain high growth. AI databases will still maintain the largest market volume, and directions such as ontology platforms, multimodal data management, high-quality datasets, and Agent data invocation will become important drivers of market growth.
Moving forward, iFenxi will continue to conduct a series of research around the enterprise AI data infrastructure market, focusing on three directions:
First, conduct research on representative vendors to sort out the capability layout, product positioning, and value propositions of different types of vendors in the enterprise AI data infrastructure market.
Second, construct an evaluation system for enterprise AI data infrastructure vendors, systematically evaluating representative vendors from dimensions such as market capability, product capability, customer capability, ecosystem capability, and strategic investment.
Third, release the enterprise AI data infrastructure vendor quadrant report to help enterprise users identify capability differences among different types of vendors, and also provide references for vendors to judge market positioning, optimize product strategies, and enhance market influence.
Scan the QR code to get the "2026 iFenxi · China AI Data Infrastructure Market Size Report"