AI Full-Stack Operations Platform (MLOps, Model Monitoring & Edge Inference)

📌 Background

In 2026, enterprise model scale is growing rapidly, with surging demand for full lifecycle management, real-time monitoring, and edge inference. Cloud-edge-device integrated computing power has become the platform foundation. According to Moore Threads' 2026 semi-annual report, its H1 revenue reached 1.736 billion yuan (a year-on-year increase of 147.42%), exceeding the full year of 2025; gross profit was 989 million yuan (a year-on-year increase of 103.78% based on the company's financial reporting standards), indicating that domestic GPU-driven cloud-edge-device inference demand is expanding.

👤 Target Customers

Large and medium-sized enterprise AI R&D teams, as well as business departments in finance, manufacturing, and other sectors requiring large-scale model deployment. The paying entities are technical departments or CIOs.

💰 Revenue Streams

1) Usage-based billing for computing resources; 2) Subscription fees for model monitoring and alerting packages; 3) Edge node rental or software-hardware integrated service fees.

🧮 Cost Structure

1) Cloud computing and storage costs; 2) Platform R&D, operations personnel, and technical support expenses; 3) Edge device procurement, deployment, and maintenance expenses.

🛡️ Moat

1) Deep integration of underlying computing resources for efficient resource scheduling; 2) Provision of complete model pipeline standardization and automated rollback; 3) Enterprise-grade security and compliance auditing capabilities.

🔑 Keys to Success

• Computing and edge integration
• Unified monitoring and automated rollback
• Enterprise-grade security compliance

⚠️ Risks

• Fluctuations in computing supply leading to service instability
• Changes in compliance policies increasing audit costs
• Rapid technological iteration causing platform obsolescence

🏢 Cases

• Huawei ModelArts provides full-process model management
• EdgeOne Makers enables rapid deployment of AI Agents
• Moore Threads provides GPU-accelerated edge computing services

📊 SWOT Analysis