AI Large Models and Intelligent Agent Enterprise Services

📌 Background

The large model industry completed the crucial transition from a parameter race to application implementation in 2025, with enterprise demand upgrading from trial usage to production-grade deployment. The market size is projected to exceed 50 to 70 billion RMB in 2026, which is widely recognized as the breakout year for AI Agents. Cloud and model vendors are accelerating the construction of enterprise productivity tool matrices covering customer service, marketing, coding, documentation, and other scenarios.

👤 Target Customers

Paid customers are primarily IT and business departments of medium to large enterprises. They procure AI Agent services for scenarios such as customer service automation, intelligent marketing, assisted programming, internal knowledge management, and document processing to improve workforce efficiency, reduce operational costs, and ensure data sovereignty compliance.

💰 Revenue Streams

Cloud and model companies commercialize through multiple revenue streams: first, charging enterprises based on API calls or token consumption using post-paid or prepaid commitment models; second, providing software licensing combined with annual operation and maintenance subscriptions for private deployment, integrating single-deployment fees with subsequent annual fees; third, charging seat subscription fees per workstation per month for packaged Agent products covering specific functional scenarios such as customer service and coding.

🧮 Cost Structure

Major expenses include hardware and operation costs for large-scale GPU computing clusters, research and development investments for ongoing base model training and fine-tuning, labor costs for enterprise-grade private delivery and on-site adaptation, and sales channel commissions.

🛡️ Moat

The moat stems from the ability to build a multi-model and multi-Agent ecosystem, first-mover advantages in customer trust and scenario optimization driven by the data flywheel, and private hybrid deployment solutions meeting the data compliance needs of top-tier clients. Together, these raise switching costs, making it difficult for latecomers to catch up quickly.

🔑 Keys to Success

• Deep scenario implementation in vertical industries
• Data compliance in private and hybrid deployments
• Agent stability and quantifiable return on investment

⚠️ Risks

• High token usage costs compressing gross margins
• Price wars triggered by general model homogenization
• Difficulty in renewing subscriptions due to unquantifiable implementation results

🏢 Cases

• Volcengine / ByteDance Doubao Model
• Alibaba Cloud Bailian
• DeepSeek Open Platform
• Zhipu GLM

📊 SWOT Analysis