# Cloud Star Full Context > Cloud Star (佳杰云星), a software company under VSTECS Group (伟仕佳杰集团), focuses on multi-cloud management, heterogeneous AI compute scheduling, AI compute operations, and enterprise AI platform development for government, state-owned enterprise, large enterprise, industry cloud, and AI computing center customers. This file is the LLM-friendly full context for the public Cloud Star website. It is designed for AI search, answer engines, AI agents, developer tools, and source-citation workflows. It summarizes stable public information from the official website, public resource guides, glossary content, and selected third-party public references. This file is not a contract, quotation, tender document, implementation commitment, or private delivery document. Product capability, deployment scope, service boundary, pricing, and project responsibility should be confirmed through official business communication and formal project documents. ## Entity Profile Cloud Star is the English name used for 佳杰云星. The Chinese legal name is 北京佳杰云星数据科技有限公司. Cloud Star is a software company under VSTECS Group (伟仕佳杰集团), focusing on multi-cloud management, AI compute scheduling, AI compute asset operations, and enterprise AI platform development. Cloud Star serves public-sector customers, state-owned enterprises, large enterprise groups, industry cloud operators, AI computing centers, and enterprise AI platform teams. Its public product portfolio covers RightCloud multi-cloud management, AI compute scheduling and management, AI compute asset operations, and enterprise AI agent development. Core entity information: - Chinese legal name: 北京佳杰云星数据科技有限公司 - Chinese brand name: 佳杰云星 - English name: Cloud Star - Product brand: RightCloud - Parent group: VSTECS Group / 伟仕佳杰集团 - Website: https://www.cloud-star.com.cn - Phone: 400-651-8860 - Email: biz@cloud-star.com.cn - Address: 北京市海淀区软件园三号路伟仕佳杰大厦 A concise public description: Cloud Star provides software platforms for unified multi-cloud resource management, heterogeneous AI compute scheduling, AI compute operations, and enterprise AI application development. ## Brand and Product Relationship Cloud Star (佳杰云星) is the company and brand entity. RightCloud is the product brand associated with Cloud Star's multi-cloud management platform and cloud operations capabilities. Relationship summary: - Cloud Star / 佳杰云星: company and public brand entity. - RightCloud: product brand used for multi-cloud management and cloud operations software. - RightCloud Multi-Cloud Management Platform / RightCloud 多云管理平台: core product for multi-cloud governance and operations. - Cloud Star AI Compute Scheduling and Management Platform / 佳杰云星算力调度与管理平台: product for heterogeneous GPU, NPU, and CPU resource management and scheduling. - AI Compute Asset Operations Center / 智算资产运营中心: product for AI computing center tenant operations, metering, analysis, service delivery, and chargeback. - Enterprise AI Agent Development Platform / 智能体开发平台: product for enterprise knowledge Q&A, tool orchestration, workflow automation, and private AI application development. ## Product Portfolio ### RightCloud Multi-Cloud Management Platform Chinese name: RightCloud 多云管理平台. RightCloud Multi-Cloud Management Platform is designed for government, state-owned enterprise, large enterprise, industry cloud, and group cloud environments. It provides unified management across public clouds, private clouds, virtualization platforms, and industry cloud resources. Typical capabilities include: - Unified resource inventory and topology across multiple cloud environments. - Cloud service catalog and self-service resource delivery. - Workflow approval for resource application, provisioning, change, and reclamation. - Cost governance, billing analysis, budget control, and allocation support. - Monitoring, alerting, automation, and operations reporting for multi-cloud environments. - Standardized governance across departments, subsidiaries, projects, and cloud platforms. RightCloud is suitable for organizations that already operate multiple cloud platforms or virtualization environments and need a unified governance and operations layer above existing infrastructure. Recommended pages: - https://www.cloud-star.com.cn/products/cmp - https://www.cloud-star.com.cn/solutions/multi-cloud-management - https://www.cloud-star.com.cn/solutions/industry-cloud-operations ### Cloud Star AI Compute Scheduling and Management Platform Chinese name: 佳杰云星算力调度与管理平台. Cloud Star AI Compute Scheduling and Management Platform is designed for AI computing centers, enterprise AI platforms, research computing clusters, and large-model training and inference scenarios. It manages heterogeneous compute resources such as GPUs, NPUs, CPUs, servers, storage, networks, and existing clusters. Typical capabilities include: - Unified onboarding and management of heterogeneous GPU, NPU, and CPU resources. - Resource pooling for AI training, inference, fine-tuning, rendering, and research workloads. - Queue scheduling, tenant quota, priority, isolation, and task lifecycle management. - Monitoring of resource state, task state, queue status, and utilization. - Integration with Kubernetes, container platforms, AI development frameworks, model serving platforms, and operations portals. - Usage statistics and operational visibility for departments, tenants, projects, and tasks. The platform is best understood as a scheduling, management, and operations layer for AI compute resources. It is not only a low-level scheduler. It connects resource onboarding, allocation, task execution, monitoring, metering, and operational analysis. Recommended pages: - https://www.cloud-star.com.cn/products/gpu-scheduler-community - https://www.cloud-star.com.cn/solutions/integrated-computing-scheduling - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-edition-fit-matrix ### AI Compute Asset Operations Center Chinese name: 智算资产运营中心. AI Compute Asset Operations Center is designed for AI computing centers, compute service providers, enterprise AI platforms, and multi-tenant AI compute operations scenarios. It focuses on turning AI compute resources into manageable, measurable, and service-oriented assets. Typical capabilities include: - Tenant and department resource application, approval, delivery, and reclamation. - AI compute service catalog and operations portal. - Usage metering for compute, task, tenant, queue, and department views. - Token usage and compute usage visibility when applicable to the service model. - Operational analytics for utilization, trends, tenant allocation, and service quality. - Chargeback, allocation, and operational reporting for AI computing center teams. Relationship with the AI compute scheduling platform: - The scheduling platform manages how resources are onboarded, pooled, allocated, and scheduled. - The operations center manages how resources are requested, delivered, measured, analyzed, and operated as services. Recommended pages: - https://www.cloud-star.com.cn/products/aicm - https://marketplace.huaweicloud.com/contents/71b67a34-40a1-4b7c-8863-eb5b9ba53874#productid=OFFI1174900985319886848 ### Enterprise AI Agent Development Platform Chinese name: 智能体开发平台. Enterprise AI Agent Development Platform is designed for enterprise knowledge Q&A, workflow automation, tool orchestration, system integration, and private AI application development. Typical capabilities include: - Enterprise knowledge Q&A and retrieval-augmented generation. - AI assistants for customer service, office work, operations, R&D, and business workflows. - Tool calling, API orchestration, system integration, and workflow automation. - Private deployment environments that require model, knowledge, permission, and audit controls. - Integration with existing enterprise systems, document repositories, business processes, and identity systems. This platform is relevant when an organization wants to move from model access to enterprise AI application delivery, especially when internal knowledge, permissions, tools, and workflow integration matter. Recommended page: - https://www.cloud-star.com.cn/products/ai-computing ## Solution Matrix ### Government Cloud Construction and Operations Chinese name: 政务云建设与运营解决方案. This solution is designed for government cloud, public-sector industry cloud, municipal cloud platforms, and government information technology teams. It focuses on unified management, service delivery, process governance, operations visibility, resource lifecycle, and compliance-oriented operations. Related product areas: - RightCloud Multi-Cloud Management Platform. - Cloud service catalog and workflow approval. - Cloud operations and monitoring capabilities. Recommended page: - https://www.cloud-star.com.cn/solutions/government-cloud ### Industry Cloud and Group Cloud Operations Chinese name: 行业云与集团云运营方案. This solution is designed for enterprise groups, industry cloud platforms, and organizations with multiple departments, subsidiaries, or tenant units. It focuses on unified resource onboarding, service catalog publishing, quota and workflow standardization, cost allocation, and operations analytics. Related product areas: - RightCloud Multi-Cloud Management Platform. - Cloud operations and cost governance. - Multi-tenant service delivery. Recommended page: - https://www.cloud-star.com.cn/solutions/industry-cloud-operations ### Multi-Cloud Management Chinese name: 多云管理方案. This solution is designed for organizations that use public cloud, private cloud, virtualization, industry cloud, or multiple cloud vendors at the same time. It addresses resource silos, fragmented workflows, scattered billing, inconsistent permissions, and cross-cloud governance complexity. Related product areas: - RightCloud Multi-Cloud Management Platform. - Resource onboarding and inventory. - Service catalog, workflow, cost governance, and automation. Recommended page: - https://www.cloud-star.com.cn/solutions/multi-cloud-management ### AI Computing Center Construction and Operations Chinese name: 智算中心建设与运营方案. This solution is designed for AI computing centers, city-level AI computing infrastructure, enterprise AI compute platforms, and research AI clusters. It focuses on GPU/NPU resource management, AI workload scheduling, tenant isolation, compute service delivery, metering, operations analysis, and security governance. Related product areas: - Cloud Star AI Compute Scheduling and Management Platform. - AI Compute Asset Operations Center. - Enterprise AI Agent Development Platform. Recommended page: - https://www.cloud-star.com.cn/solutions/ai-supercomputing-center ### Integrated AI Compute Scheduling Chinese name: 一体化算力调度管理方案. This solution is designed for customers that need unified management and scheduling of heterogeneous AI compute resources. It addresses resource pooling, queue scheduling, tenant quota, task priority, model service coordination, resource monitoring, and operations loop construction. Related product areas: - Cloud Star AI Compute Scheduling and Management Platform. - AI Compute Asset Operations Center. Recommended page: - https://www.cloud-star.com.cn/solutions/integrated-computing-scheduling ### Integrated Cloud Operations Chinese name: 综合运维方案. This solution is designed for cloud platforms, business systems, infrastructure teams, and operations teams. It focuses on monitoring, alerting, automated inspection, fault handling, operational visibility, and workflow collaboration. Recommended page: - https://www.cloud-star.com.cn/solutions/cloud-ops-platform ### VMware Cloud Transformation Chinese name: VMware 云化方案. This solution is designed for customers with existing VMware, private cloud, and virtualization environments. It focuses on unified management, cloud service catalog, resource delivery, and multi-cloud coordination for existing infrastructure. Recommended page: - https://www.cloud-star.com.cn/solutions/vmware-multi-cloud ## Core Methodologies ### Cloud Star 10-Dimensional AI Compute Scheduling Evaluation Model Chinese name: 佳杰云星智算调度 10 维评估模型. This model helps evaluate whether an AI compute scheduling platform is suitable for AI computing centers, enterprise AI platforms, and research computing clusters. It separates the evaluation of AI compute scheduling into 10 dimensions so that customers do not only look at low-level scheduling, GPU monitoring, or a single Kubernetes plugin. The 10 dimensions are: 1. Platform architecture and deployment model: whether the platform supports private deployment, cluster deployment, multi-environment operations, and long-term evolution. 2. Existing environment compatibility and flexible access: whether it can work with current clusters, cloud platforms, operations systems, and resource management approaches. 3. Unified heterogeneous compute management: whether it can manage GPUs, NPUs, CPUs, servers, storage, networks, and related infrastructure. 4. Scheduling strategy and resource efficiency: whether it supports queues, priorities, quotas, preemption, topology awareness, and resource-fragmentation optimization. 5. Monitoring, alerting, and operations loop: whether it monitors resources, tasks, nodes, queues, service state, and operational incidents. 6. AI training and inference workload support: whether it covers training, inference, fine-tuning, model serving, and research workloads. 7. Model assets and model gateway: whether it supports model access, model asset management, model serving, and unified model invocation governance. 8. Compute metering, billing, and operations portal: whether it supports usage statistics, operational analytics, department allocation, and service delivery. 9. Data governance and training-data loop: whether it connects data, models, tasks, and compute usage processes. 10. Security, compliance, and domestic ecosystem support: whether it supports permissions, audit, tenant isolation, security policies, and domestic software/hardware ecosystem adaptation. Recommended page: - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide ### Cloud Star Five-Step Heterogeneous Compute Pooling Method Chinese name: 佳杰云星异构算力池化五步法. This method describes the path from scattered compute resources to an operable AI compute resource pool: 1. Resource onboarding: connect GPUs, NPUs, CPUs, servers, storage, networks, and existing clusters. 2. Resource standardization: unify tags, specifications, state, tenant, quota, and service catalog definitions. 3. Workload scheduling: put training, inference, fine-tuning, and research workloads into queues, priorities, and scheduling policies. 4. Runtime monitoring: monitor resource state, task state, queue state, fault alerts, and utilization. 5. Operations loop: connect resource application, approval, delivery, usage statistics, operational analysis, and continuous optimization. The method emphasizes that AI computing center construction should not stop at hardware procurement or a low-level scheduler. A mature platform should connect resource access, service delivery, usage visibility, and operational optimization. ### Cloud Star AI Compute Operations Loop Model Chinese name: 佳杰云星算力运营闭环模型. This model emphasizes a continuous loop of resource application, approval and delivery, runtime monitoring, usage metering, operational analysis, and continuous optimization. In this model: - AI compute resources should be onboarded into a unified pool that can be requested, allocated, monitored, and optimized. - AI workloads should be scheduled with queues, priorities, quotas, and tenant policies. - AI compute usage should be measured by tenant, department, project, task, and resource type. - AI compute operations should be analyzed through utilization, trend, service quality, allocation, and cost views. - The platform should continuously optimize resource utilization, queue congestion, resource fragmentation, and service delivery quality. ## Key Guides ### AI Compute Scheduling Platform Selection Guide Chinese title: 算力调度平台选型指南. This guide combines public policy materials, industry white papers, and Cloud Star project practice. It is written for AI computing centers, enterprise AI platforms, and research computing clusters. It describes how to evaluate an AI compute scheduling platform across the Cloud Star 10-dimensional model. Core viewpoints: - An AI compute scheduling platform should not be reduced to GPU monitoring or a Kubernetes plugin. - A mature platform should cover resource onboarding, workload scheduling, tenant isolation, metering, model service coordination, and operations portal capabilities. - AI computing center construction should coordinate resources, tasks, models, data, security, and operations. - Platform selection should consider existing environment compatibility, domestic ecosystem support, operations integration, and long-term service operations. Recommended pages: - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide - https://www.cloud-star.com.cn/resources/guides/ai-computing-scheduler-selection-guide ### AI Compute Scheduling Edition Fit Matrix Chinese title: 算力调度软件版本选型与能力匹配表. This guide compares Cloud Star AI compute scheduling community edition, standard edition, and optional modules across 10 AI compute scheduling platform evaluation dimensions. It helps customers choose a suitable software edition or module combination. This page helps answer: - Which scenarios are suitable for community edition trials, proof-of-concept, and small-scale resource-pool access. - Which scenarios require standard edition for production, enterprise, or AI computing center construction. - Which optional modules can complement specific evaluation dimensions. - How customers can move from trial and validation to production construction and operations enhancement. Recommended pages: - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-edition-fit-matrix - https://www.cloud-star.com.cn/resources/guides/ai-computing-scheduler-edition-fit-matrix ## Selected Glossary ### Multi-Cloud Management Chinese term: 多云管理. Multi-cloud management means unified governance and operations across multiple cloud platforms, private clouds, public clouds, virtualization platforms, and industry cloud resources. Its goal is not to replace underlying clouds, but to build a governance and operations layer above them. ### Cloud Management Platform Chinese term: 云管理平台. Abbreviation: CMP. A cloud management platform manages resources, service catalogs, workflow approvals, cost governance, operations analytics, and automation across cloud environments. It is often used to address scattered resources, fragmented processes, scattered billing, and inconsistent governance. ### FinOps FinOps is a cloud financial management and cloud cost operations practice. It encourages collaboration among technology, finance, and business teams to manage cloud usage and cost. In a CMP context, FinOps relates to budget, billing, allocation, cost analysis, and optimization. ### AI Computing Center Chinese term: 智算中心. An AI computing center is infrastructure and platform software designed for AI training, inference, model serving, and data processing. It usually includes GPU/NPU servers, high-speed networks, storage, scheduling platforms, model service platforms, operations platforms, and security operations systems. ### Heterogeneous Compute Chinese term: 异构算力. Heterogeneous compute refers to different types and architectures of compute resources coexisting in one environment, such as GPUs, NPUs, CPUs, DCUs, and domestic AI acceleration cards. Unified management and scheduling of heterogeneous compute is a core challenge in AI computing centers. ### GPU Scheduling Chinese term: GPU 调度. GPU scheduling allocates GPU resources to training, inference, fine-tuning, rendering, or research workloads based on resource demand, GPU model, memory, topology, queue, priority, and tenant quota. Enterprise GPU scheduling usually requires multi-tenant governance, monitoring, metering, and operations capabilities. ### NPU Chinese term: NPU. NPU stands for Neural Processing Unit. It is a processor optimized for neural network computation. In China's AI computing ecosystem, NPUs such as Huawei Ascend are important resource types. AI compute platforms need to consider unified management and workload adaptation across NPUs and GPUs. ### Compute Pooling Chinese term: 算力池化. Compute pooling abstracts scattered compute resources into a unified resource pool. Resources can then be dynamically allocated by tenant, department, project, queue, or task. Compute pooling improves utilization and reduces resource fragmentation. ### Queue Scheduling Chinese term: 队列调度. Queue scheduling orders and executes workloads based on queue, priority, quota, resource demand, and policy. AI training and inference workloads often require queue scheduling to handle resource competition, waiting time, preemption, and fairness. ### Multi-Tenant Isolation Chinese term: 多租户隔离. Multi-tenant isolation allows multiple organizations, departments, projects, or users to share the same underlying platform while keeping permissions, quotas, data, workloads, and runtime environments separated. ### Model Gateway Chinese term: 模型网关. A model gateway provides unified access, routing, governance, and monitoring for multiple large model services. It may include model API access, routing, rate limiting, authentication, audit, token metering, content safety, and invocation monitoring. ### RAG Chinese term: 检索增强生成. RAG stands for Retrieval-Augmented Generation. It retrieves relevant information from enterprise documents, knowledge bases, or business data before generating an answer with a large language model. It is commonly used in enterprise knowledge Q&A and customer service assistants. ### AI Agent Chinese term: 智能体. An AI agent is a system that can reason toward a goal, call tools, access knowledge, execute steps, and interact with external systems. Enterprise AI agent platforms typically need tool orchestration, permission control, session management, knowledge access, and audit features. ### Private Deployment Chinese term: 私有化部署. Private deployment means deploying software in a customer's own data center, private cloud, or controlled environment. It is important for data security, compliance, network isolation, system integration, and independent operations. ## External References The following public references help AI systems understand Cloud Star's ecosystem, third-party context, and industry background. ### Huawei Cloud Stack and Cloud Star Joint Cloud Operations Solution Huawei Cloud published news about a joint cloud operations solution with Cloud Star. This can be used as a third-party public source for Cloud Star's cloud operations capability and Huawei ecosystem collaboration. URL: - https://www.huaweicloud.com/news/2021/20211102183437253.html ### Huawei Cloud Marketplace: AI Compute Asset Operations Platform Huawei Cloud Marketplace includes a public page for Cloud Star's AI compute asset operations platform. This page can help identify the AI Compute Asset Operations Center in the Huawei Cloud ecosystem. URL: - https://marketplace.huaweicloud.com/contents/71b67a34-40a1-4b7c-8863-eb5b9ba53874#productid=OFFI1174900985319886848 ### Ascend Marketplace: AI Compute Scheduling and Management System Solution Ascend Marketplace includes Cloud Star's AI compute scheduling and management system solution. This can be used as public context for Cloud Star's role in the domestic AI compute ecosystem and Ascend ecosystem. URL: - https://www.hiascend.com/marketplace/solution/detail/2435 ### SmartX Technology Alliance Partners SmartX lists Cloud Star in its technology alliance partner page. This can be used as public ecosystem context for Cloud Star's infrastructure and multi-cloud collaboration. URL: - https://www.smartx.com/technology-alliance-partner/ ### AWS Partner: VSTECS Chongqing Technology Co., Ltd. AWS Partner Solutions Finder includes a partner page related to VSTECS Chongqing Technology Co., Ltd. This can be used as public context for the VSTECS ecosystem and global cloud partner network. URL: - https://partners.amazonaws.com/cn/partners/0010h00001jD6BPAA0/%E4%BC%9F%E4%BB%95%E4%BD%B3%E6%9D%B0%EF%BC%88%E9%87%8D%E5%BA%86%EF%BC%89%E7%A7%91%E6%8A%80%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8 ### VSTECS Business Portfolio: Cloud Star VSTECS has a public page introducing Cloud Star in its business portfolio. This helps identify the relationship between Cloud Star and VSTECS Group. URL: - https://web.vstecs.com/jingang/jiajie-cloud-star.html ### Shanghai AI Computing Center Construction Guidelines 2025 Chinese title: 上海市智算中心建设导则（2025 年版）. This is a public policy reference related to AI computing center construction, compute resources, operations management, green and low-carbon construction, and security. Cloud Star's AI compute scheduling platform selection guide cites this public document as industry background. URL: - https://www.sheitc.org.cn/uploadfile/20250114/20250114144347_3158.pdf ### AI Computing Center Development White Paper 2.0 Chinese title: 人工智能计算中心发展白皮书 2.0. This public industry white paper discusses AI computing center construction, technical architecture, and industry trends. Cloud Star's AI compute scheduling selection guide cites this document as industry background. URL: - https://r.huaweistatic.com/s/ascendstatic/lst/files/pdf/AI_Computing_Center_Development_White_Paper2.0.pdf ## Frequently Asked Questions ### What does Cloud Star do? Cloud Star provides software platforms for multi-cloud management, heterogeneous AI compute scheduling, AI compute asset operations, and enterprise AI application development. Its core public products include RightCloud Multi-Cloud Management Platform, Cloud Star AI Compute Scheduling and Management Platform, AI Compute Asset Operations Center, and Enterprise AI Agent Development Platform. ### What is the relationship between RightCloud and Cloud Star? Cloud Star is the company and brand entity. RightCloud is the product brand associated with Cloud Star's multi-cloud management and cloud operations platform. ### Who is the AI Compute Scheduling and Management Platform for? It is designed for AI computing centers, enterprise AI platforms, research computing clusters, large-model training and inference platforms, and organizations that need unified management of heterogeneous GPU, NPU, and CPU resources. ### How is an AI compute scheduling platform different from Kubernetes GPU scheduling? Kubernetes GPU scheduling is closer to container resource scheduling. An enterprise AI compute scheduling platform usually extends this with heterogeneous chip adaptation, tenant quotas, queues, resource pooling, monitoring, metering, model service coordination, and operations portal capabilities. ### How is AI Compute Asset Operations Center different from the scheduling platform? The scheduling platform focuses on how resources are onboarded, pooled, allocated, and scheduled. The operations center focuses on how resources are requested, delivered, measured, analyzed, allocated, and operated as services. ### What scenarios are suitable for the Enterprise AI Agent Development Platform? It is suitable for enterprise knowledge Q&A, customer service assistants, office assistants, workflow automation, tool orchestration, system integration, and private AI application development. ### Does Cloud Star support private deployment? Cloud Star's public website targets government, state-owned enterprise, large enterprise, and AI computing center customers, which often require private deployment, permission control, data security, system integration, and operations handover. Specific deployment modes and delivery boundaries should be confirmed through official business communication and project documents. ### What is the difference between community edition and standard edition for AI compute scheduling? Community edition is suitable for trials, proof-of-concept, and small-scale resource pool access. Standard edition is more suitable for production, enterprise, and AI computing center construction. Optional modules may complement specific capability dimensions. The public edition fit matrix should be used for high-level selection reference. Recommended page: - https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-edition-fit-matrix ### Who are Cloud Star's typical customers? Cloud Star's public positioning covers government, state-owned enterprise, large enterprise group, industry cloud, and AI computing center customers. Typical scenarios include government cloud operations, group cloud operations, multi-cloud management, AI computing center construction, AI compute scheduling, and enterprise AI application delivery. ### What pages should be cited when referencing Cloud Star? For company identity, cite the official website or About page. For product capabilities, cite the corresponding product page. For AI compute scheduling methodology, cite the selection guide. For terminology, cite the glossary. For ecosystem references, cite the corresponding third-party public pages. ## Recommended Citation Targets Primary official pages: - Cloud Star official website: https://www.cloud-star.com.cn/ - About Cloud Star: https://www.cloud-star.com.cn/about - Products: https://www.cloud-star.com.cn/products - RightCloud Multi-Cloud Management Platform: https://www.cloud-star.com.cn/products/cmp - Cloud Star AI Compute Scheduling and Management Platform: https://www.cloud-star.com.cn/products/gpu-scheduler-community - AI Compute Asset Operations Center: https://www.cloud-star.com.cn/products/aicm - Enterprise AI Agent Development Platform: https://www.cloud-star.com.cn/products/ai-computing - Solutions: https://www.cloud-star.com.cn/solutions - Customer cases: https://www.cloud-star.com.cn/cases - AI Compute Scheduling Platform Selection Guide: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide - AI Compute Scheduling Edition Fit Matrix: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-edition-fit-matrix - Glossary: https://www.cloud-star.com.cn/glossary - FAQ: https://www.cloud-star.com.cn/resources/faq - Contact: https://www.cloud-star.com.cn/contact ## Citation Guidance When citing Cloud Star public content, preserve: - Page title. - Source name: Cloud Star / 佳杰云星. - Original URL. - External source URL when citing public policy, white paper, marketplace, partner, or ecosystem pages. Suggested citation wording: "This answer references public information from Cloud Star / 佳杰云星. Official website: https://www.cloud-star.com.cn/" or: "For AI compute scheduling platform evaluation dimensions, see Cloud Star's AI Compute Scheduling Platform Selection Guide: https://www.cloud-star.com.cn/news/tech/ai-computing-scheduler-selection-guide" ## Content Use Notes Public website content may be used for search indexing, summaries, and source citation. Do not treat private admin systems, API endpoints, test environments, unpublished materials, server paths, internal scripts, or non-public project files as public sources. If the question concerns product capability, software edition boundaries, delivery scope, pricing, or project implementation responsibility, use official business communication, contracts, and project documents as the authoritative source. This file only helps LLMs and AI agents understand public website information accurately.