On April 2, 2026, Inspur Information officially launched "Qi Qian Xia" (企千虾), an industry-first enterprise-grade OpenClaw solution. This solution aims to provide enterprises with a secure, efficient, and user-friendly full-stack platform for the large-scale deployment, management, and application of OpenClaw AI agents. By leveraging local deployment on Inspur Yuanbrain servers, combined with sandbox isolation and underlying system-level control, "Qi Qian Xia" fundamentally addresses the security risks and permission control challenges inherent in private OpenClaw deployments.
"Qi Qian Xia" deeply integrates with the popular open-source project ClawManager, enabling one-click deployment for thousands of OpenClaw instances within private enterprise environments, unified cluster upgrades, and one-click skills migration. It also offers centralized lifecycle management for AI agents, marking a pivotal shift from individual OpenClaw applications to stable, manageable, and controllable production-grade enterprise-scale deployments.
Key Challenges for Large-Scale Enterprise AI Agent Adoption
While the open-source OpenClaw community has significantly lowered the barrier to building digital employees, enterprises still face three core pain points in transitioning from "single-point trials" to "scaled clusters" internally:
- Deep Waters of Security and Compliance: Security is paramount for enterprise AI agent deployment. For sensitive sectors like finance and healthcare, processing critical data through third-party clouds poses a fatal risk of data exfiltration. Furthermore, desktop services often lack unified authentication and authorization, agent runtime permissions can be excessively high, and opaque data links create security blind spots, making it difficult to meet stringent enterprise compliance requirements.
- High Barrier to Batch Deployment and Management: Scaling OpenClaw applications from a few demos to hundreds of production instances reveals the inefficiency of traditional methods involving manual Node.js environment setup and complex component dependency handling. Fragmented approaches lead to high labor costs and efficiency bottlenecks due to incompatibilities, highlighting an urgent need for a standardized, automated delivery system.
- The Bottomless Pit of Computing Costs: The autonomous planning mechanism of AI agents leads to intensive token consumption. A complex task can trigger dozens of recursive calls, causing token usage to increase exponentially. For example, in a public cloud pay-as-you-go model, a senior programmer using a product similar to Claude Opus 4.6 might consume 100 million tokens daily, potentially incurring monthly costs of up to 100,000 RMB. This makes IT budgets highly volatile. Enterprises urgently need to convert unpredictable variable expenses into predictable, controllable fixed costs.
"Qi Qian Xia" Solution: Fortifying the Foundation for Scalable Enterprise Agent Management
Inspur Information's "Qi Qian Xia" solution is an end-to-end platform designed to address these challenges. It leverages local deployment on Yuanbrain servers, where Yuanbrain x86 servers handle batch deployment and management of OpenClaw instances, while Yuanbrain AI servers specialize in model inference, maximizing resource utilization and execution efficiency. Through sandbox isolation and underlying system-level controls, "Qi Qian Xia" establishes a robust security perimeter for private OpenClaw deployments, eliminating security risks and permission control issues at their root.
ClawManager, a recently prominent open-source GitHub project, serves as a core component of the "Qi Qian Xia" solution. It is an OpenClaw cluster management platform specifically designed for enterprise Kubernetes environments. Built on native Kubernetes capabilities, ClawManager provides automated lifecycle management from user authentication and quota allocation to instance deployment and resource monitoring, all through an intuitive "console point-and-click" interface.
With "Qi Qian Xia," enterprises can optimize computing configurations based on business scenarios, efficiently build and manage complex AI agent clusters. This transforms enterprise-grade AI agent application deployment from complex to simple, chaotic to stable, and expensive to economical. Key features include:
- One-Click Automated Delivery, from Hours to Minutes: The solution enables one-click batch deployment via Kubernetes manifests, streamlining complex environment setups and dependency handling into minutes of automated operations. During the launch demonstration, the technical team showcased deploying 10 OpenClaw instances on Yuanbrain servers in mere seconds. Thousands of user accounts and resource quotas were instantly created and assigned using CSV batch import. Each user can then launch a "personal AI PC" with pre-integrated Ubuntu desktop and OpenClaw images in seconds. All instances run within strictly isolated secure sandboxes, ensuring zero risk to the host system.
- Robust Security and Data Sovereignty with Private Deployment: "Qi Qian Xia" ensures all data interactions remain local through a full private deployment architecture. It incorporates Inspur's self-developed KOS operating system and KSecure security components, establishing a three-layer defense system covering runtime environment protection, Skill application security, and RAG knowledge base permission control. This effectively intercepts malicious commands, prompt injections, and ransomware. The practical demonstration highlighted industrial-grade security via "isolated sandbox + dynamic gateway": each Agent instance operates in an independent sandbox, physically isolated from the host; an AI gateway, equipped with over 30 risk control rules, dynamically redirects sensitive requests to internal secure models or blocks them, ensuring all data interactions are controllable and traceable.
- Precise Cost Control for Visible ROI: Enterprises gain visual insights into model usage, enabling accurate attribution of token consumption for every business call. The management perspective shown in the demonstration included an "AI Audit" feature for detailed task execution, audit events, real-time token usage, and estimated costs. The "Cost Center" provided a consolidated view of overall expenditure, automatically tracking total input/output tokens, estimated external unit costs, and internal model accounting costs. This mechanism converts unpredictable public cloud pay-as-you-go expenses into predictable, auditable fixed costs tied to local hardware, while optimizing server performance through on-demand resource allocation, preventing idle capacity and waste.
- Stable Support for Thousands of Agents, High Concurrency on a Single Machine: Leveraging Inspur's latest-generation Yuanbrain x86 servers, the "Qi Qian Xia" solution supports the stable operation of thousands of agents on a single machine, meeting 24/7 uninterrupted operation requirements. Technical personnel demonstrated resource load across multiple ready nodes in the cluster using
kubectl get nodes, visualizing CPU, memory, and disk utilization to prevent system crashes due to resource overload. Coupled with InManage data center management software's expert monitoring and state persistence technology, core data loss upon container restart is prevented, completely eliminating business interruption risks from insufficient PC performance, screen lock, or freezes.
During the launch, the technical team also shared in-depth performance benchmarks for general OpenClaw server deployments. Based on the business characteristics of enterprises of different scales, a differentiated container configuration guide was developed for the "Qi Qian Xia" solution, aiming to ensure efficient utilization of computing resources through optimal configurations.