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成功请求率、错误率、限流率 |\n| 质量 | 结果是否满足任务 | 任务通过率、格式遵从、人工接受率 |\n| 成本 | 完成任务消耗多少 | 每成功任务成本、Token或GPU消耗 |\n\n四个维度需要联合观察，但不能简单合并为一个分数。\n\n## 第一步：定义服务与用户旅程\n\nSLO对象应是稳定的模型服务或业务能力，而不是一个底层实例。明确调用方、任务、输入范围、关键路径和失败后果。同一模型用于实时客服和离线摘要时，应设置不同SLO。\n\n## 第二步：选择可测量的SLI\n\n每项SLI应明确：\n\n- 数据来源和采集位置；\n- 分子、分母与排除项；\n- 聚合窗口和分位数；\n- 是否包含重试、缓存和降级；\n- 按租户、应用、版本或路由如何切分。\n\n质量指标应基于任务评测与抽样复核，不能仅依赖接口返回码。\n\n## 第三步：建立基线再设目标\n\n先观察真实负载，了解流量、延迟、失败、质量和成本分布。目标应能支持业务，又能让团队识别风险和做出取舍。没有基线就直接设置极高目标，往往只会产生噪声告警或昂贵冗余。\n\n## 第四步：设计误差预算\n\n误差预算用于把可靠性目标转化为可操作边界。当错误、超时或质量失败消耗过快时，可以暂停高风险变更、限制非关键流量或启用降级策略。\n\n质量和成本同样可以设置运行预算，例如评测失败比例或单位成本偏离范围。\n\n## 第五步：把SLO连接到处置\n\nSLO只有触发行动才有价值：\n\n- 延迟超限：检查队列、容量、上下文和路由；\n- 可用性下降：有限重试、切换或降级；\n- 质量下降：回滚模型、提示或路由变更；\n- 成本超限：检查高价路由、重试、异常用量和资源效率。\n\n## AGIOne可提供什么基础\n\nAGIOne知识库确认模型服务层涉及统一API、策略路由、Token计量和运营分析，算力运行层涉及资源监控与计量，公共治理域覆盖配额、账单和运营规则。这些数据维度可以为SLO测量和处置提供基础。\n\n\n\n## SLO评审清单\n\n- SLO是否对应明确服务和用户；\n- 指标是否能从真实路径采集；\n- 是否区分平均值与尾部延迟；\n- 技术成功是否与任务质量分开；\n- 重试和降级是否计入；\n- 成本是否按成功任务衡量；\n- 目标超限是否有责任人和动作；\n- 版本或路由变化后是否重新建立基线。\n\n## FAQ\n\n### AI服务SLO必须包含质量吗？\n\n生产AI服务建议包含。否则接口返回成功但结果不可用时，SLO仍可能显示正常。\n\n### SLO目标应该越高越好吗？\n\n不是。更高目标通常意味着更多容量、冗余和运维成本，应与业务风险匹配。\n\n### 模型供应方SLA能否替代企业SLO？\n\n不能。供应方SLA通常只覆盖其服务边界，企业还需测量自身应用、网络、路由、质量和成本。\n\n### 成本为什么要进入SLO？\n\n当服务通过更昂贵的模型或频繁重试维持质量和可用性时，成本可以揭示不可持续的运行方式。\n\n## 下一步\n\n选择一个生产服务，用最近一个完整周期建立四维基线，再由业务、平台和运营团队共同确定目标与处置流程。\n","\u003Cdiv class=\"github-markdown-body\">\u003Cp>AI服务SLO不能只写可用率。生产级SLO应同时约束延迟、技术可用性、任务质量和单位成本，并按业务风险与任务类型分别设定。企业需要先定义服务对象和测量口径，再确定目标、误差预算、告警和处置机制。\u003C\u002Fp>\n\u003Ch2>SLI、SLO和SLA分别是什么\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>SLI\u003C\u002Fstrong>是实际测量指标，例如超时率或合格响应比例；\u003C\u002Fli>\n\u003Cli>\u003Cstrong>SLO\u003C\u002Fstrong>是内部期望目标，例如某周期内指标应达到的水平；\u003C\u002Fli>\n\u003Cli>\u003Cstrong>SLA\u003C\u002Fstrong>是对客户或业务方的正式承诺，可能包含责任条款。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>本文讨论SLO设计，不提供通用目标数字。目标值必须来自业务要求和真实基线。\u003C\u002Fp>\n\u003Ch2>为什么AI服务需要四维SLO\u003C\u002Fh2>\n\u003Cp>传统API主要关心是否响应和响应多快。AI服务还可能返回技术成功但业务无效的结果，也可能因模型、提示、数据或路由变化导致成本和质量波动。\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>维度\u003C\u002Fth>\n\u003Cth>需要回答的问题\u003C\u002Fth>\n\u003Cth>示例SLI\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\n\u003Ctr>\n\u003Ctd>延迟\u003C\u002Ftd>\n\u003Ctd>用户要等多久\u003C\u002Ftd>\n\u003Ctd>首Token时间、完整响应时间、超时率\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>可用性\u003C\u002Ftd>\n\u003Ctd>服务是否可成功使用\u003C\u002Ftd>\n\u003Ctd>成功请求率、错误率、限流率\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>质量\u003C\u002Ftd>\n\u003Ctd>结果是否满足任务\u003C\u002Ftd>\n\u003Ctd>任务通过率、格式遵从、人工接受率\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>成本\u003C\u002Ftd>\n\u003Ctd>完成任务消耗多少\u003C\u002Ftd>\n\u003Ctd>每成功任务成本、Token或GPU消耗\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\n\u003C\u002Ftable>\n\u003Cp>四个维度需要联合观察，但不能简单合并为一个分数。\u003C\u002Fp>\n\u003Ch2>第一步：定义服务与用户旅程\u003C\u002Fh2>\n\u003Cp>SLO对象应是稳定的模型服务或业务能力，而不是一个底层实例。明确调用方、任务、输入范围、关键路径和失败后果。同一模型用于实时客服和离线摘要时，应设置不同SLO。\u003C\u002Fp>\n\u003Ch2>第二步：选择可测量的SLI\u003C\u002Fh2>\n\u003Cp>每项SLI应明确：\u003C\u002Fp>\n\u003Cul>\n\u003Cli>数据来源和采集位置；\u003C\u002Fli>\n\u003Cli>分子、分母与排除项；\u003C\u002Fli>\n\u003Cli>聚合窗口和分位数；\u003C\u002Fli>\n\u003Cli>是否包含重试、缓存和降级；\u003C\u002Fli>\n\u003Cli>按租户、应用、版本或路由如何切分。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>质量指标应基于任务评测与抽样复核，不能仅依赖接口返回码。\u003C\u002Fp>\n\u003Ch2>第三步：建立基线再设目标\u003C\u002Fh2>\n\u003Cp>先观察真实负载，了解流量、延迟、失败、质量和成本分布。目标应能支持业务，又能让团队识别风险和做出取舍。没有基线就直接设置极高目标，往往只会产生噪声告警或昂贵冗余。\u003C\u002Fp>\n\u003Ch2>第四步：设计误差预算\u003C\u002Fh2>\n\u003Cp>误差预算用于把可靠性目标转化为可操作边界。当错误、超时或质量失败消耗过快时，可以暂停高风险变更、限制非关键流量或启用降级策略。\u003C\u002Fp>\n\u003Cp>质量和成本同样可以设置运行预算，例如评测失败比例或单位成本偏离范围。\u003C\u002Fp>\n\u003Ch2>第五步：把SLO连接到处置\u003C\u002Fh2>\n\u003Cp>SLO只有触发行动才有价值：\u003C\u002Fp>\n\u003Cul>\n\u003Cli>延迟超限：检查队列、容量、上下文和路由；\u003C\u002Fli>\n\u003Cli>可用性下降：有限重试、切换或降级；\u003C\u002Fli>\n\u003Cli>质量下降：回滚模型、提示或路由变更；\u003C\u002Fli>\n\u003Cli>成本超限：检查高价路由、重试、异常用量和资源效率。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>AGIOne可提供什么基础\u003C\u002Fh2>\n\u003Cp>AGIOne知识库确认模型服务层涉及统一API、策略路由、Token计量和运营分析，算力运行层涉及资源监控与计量，公共治理域覆盖配额、账单和运营规则。这些数据维度可以为SLO测量和处置提供基础。\u003C\u002Fp>\n\u003Ch2>SLO评审清单\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>SLO是否对应明确服务和用户；\u003C\u002Fli>\n\u003Cli>指标是否能从真实路径采集；\u003C\u002Fli>\n\u003Cli>是否区分平均值与尾部延迟；\u003C\u002Fli>\n\u003Cli>技术成功是否与任务质量分开；\u003C\u002Fli>\n\u003Cli>重试和降级是否计入；\u003C\u002Fli>\n\u003Cli>成本是否按成功任务衡量；\u003C\u002Fli>\n\u003Cli>目标超限是否有责任人和动作；\u003C\u002Fli>\n\u003Cli>版本或路由变化后是否重新建立基线。\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>FAQ\u003C\u002Fh2>\n\u003Ch3>AI服务SLO必须包含质量吗？\u003C\u002Fh3>\n\u003Cp>生产AI服务建议包含。否则接口返回成功但结果不可用时，SLO仍可能显示正常。\u003C\u002Fp>\n\u003Ch3>SLO目标应该越高越好吗？\u003C\u002Fh3>\n\u003Cp>不是。更高目标通常意味着更多容量、冗余和运维成本，应与业务风险匹配。\u003C\u002Fp>\n\u003Ch3>模型供应方SLA能否替代企业SLO？\u003C\u002Fh3>\n\u003Cp>不能。供应方SLA通常只覆盖其服务边界，企业还需测量自身应用、网络、路由、质量和成本。\u003C\u002Fp>\n\u003Ch3>成本为什么要进入SLO？\u003C\u002Fh3>\n\u003Cp>当服务通过更昂贵的模型或频繁重试维持质量和可用性时，成本可以揭示不可持续的运行方式。\u003C\u002Fp>\n\u003Ch2>下一步\u003C\u002Fh2>\n\u003Cp>选择一个生产服务，用最近一个完整周期建立四维基线，再由业务、平台和运营团队共同确定目标与处置流程。\u003C\u002Fp>\n\u003C\u002Fdiv>","markdown","AI服务SLO不能只看可用率。本文说明延迟、可用性、质量与成本四类指标，以及基线、误差预算、告警处置和评审方法。","\u002Ffile\u002F2\u002F2026\u002F8\u002F2bf239fa3c44.png?i=9232&name=ai-service-slo-latency-availability-quality-cost.png","ai-service-slo-latency-availability-quality-cost","AI服务SLO怎么定义？延迟、可用性、质量与成本","AI服务SLO,AI服务等级目标,LLM SLO","AI服务SLO怎么定义？延迟、可用性、质量与成本 - AGIOne","AI服务SLO,AI服务等级目标,LLM SLO,模型服务可用性,AI服务延迟,AI质量指标","2026-08-25","2026-08-26 17:02:02","2026-08-25 09:32:03","AGIOne","\u002Ffile\u002F2\u002F2026\u002F6\u002F291951eaf479.png?i=9162&name=AGIOne_反白.png","最佳实践",1787820909742,{"data":234,"status":22,"timestamp":248},{"previous":235,"next":242},{"id":236,"title":237,"cover":238,"slug":239,"productName":229,"blogTypeName":240,"blogTypeCode":241},190,"AGIOne 产品架构：模型服务、算力资源与运营治理如何协同","\u002Ffile\u002F2\u002F2026\u002F8\u002F0e2b35f2bd27.png?i=9233&name=agione-product-architecture.png","agione-product-architecture","产品介绍","products",{"id":243,"title":244,"cover":245,"slug":246,"productName":229,"blogTypeName":231,"blogTypeCode":247},193,"每 Token 成本怎么计算？指标、公式与常见误区","\u002Ffile\u002F2\u002F2026\u002F8\u002F188f5bf7e664.png?i=9228&name=cost-per-token-calculation.png","cost-per-token-calculation","best-practice",1787820909791,{"data":250,"status":22,"timestamp":345},[251,267,285,300,316,330],{"tenantId":5,"id":252,"title":253,"desc":254,"intro":255,"industryId":256,"slug":257,"productId":49,"languageId":6,"tags":258,"siteId":6,"cover":259,"coverBig":260,"seoTitle":261,"keywords":261,"description":262,"pdfurl":263,"isTop":21,"status":6,"publishedAt":9,"contentUpdatedAt":9,"sortIndex":9,"platform":9,"createdAt":264,"updatedAt":265,"languageName":15,"productName":229,"productIcon":230,"productCode":229,"industryName":266},59,"墨西哥医疗机构：AGIOne统一AI效能层驱动GPU利用率增长","墨西哥一家国家级医疗机构面临GPU资源碎片化、利用率不足20%的困境。通过部署AGIOne统一AI效能层，实现异构算力池化、多模型统一服务与混合云弹性调度。GPU利用率实现翻倍以上增长，新AI服务部署周期从\"月\"压缩到\"天\"，成功将AI从成本项目转化为支撑核心诊断业务的战略能力。","背景：该墨西哥医疗机构率先部署多个AI试点项目用于影像诊断、放射分析及患者服务，但各应用独占硬件导致GPU利用率低于20%，扩容依赖采购、CapEx持续飙升，AI规模化陷入瓶颈。\n\n方案：采用AGIOne构建统一AI效能层，实现四大突破：GPU资源池化整合碎片化算力、异构纳管跨型号GPU统一调度、多模型聚合实现LLM统一抽象服务、混合云弹性架构本地资源触顶时自动扩展至云端。同步引入全生命周期运营方法论，基于延迟、成本、数据敏感度三维智能路由自动决策工作负载去向。\n\n收益：GPU利用率从不足20%实现翻倍以上增长，硬件投资回报率显著改善；新AI服务上线及运营变更周期从\"月\"压缩至\"天\"；混合云弹性架构使扩容不再受制于硬件采购，业务全程不中断；集中化控制台强化数据治理与合规管控，AI从成本负担转变为支撑核心诊断业务的战略护城河。\n\n",5,"mexico_healthcare_gpu_utilization_ai","异构GPU资源池化$$GPU利用率翻倍增长,混合云弹性调度$$本地云端无缝扩展,多模型统一治理$$异构LLM聚合智能路由","\u002Ffile\u002F2\u002F2026\u002F6\u002F9b010d5fc872.png?i=7884&name=预览图.png","\u002Ffile\u002F2\u002F2026\u002F6\u002F236f21262dc6.png?i=7885&name=官网Bannner.png","医疗AI，AI效能层，GPU资源调度，模型聚合平台，混合云AI，智能路由，AGIOne，异构算力纳管，AI规模化运营，医疗影像诊断，AI生命周期管理，企业AI基础设施，智算资源调度，AI运营平台，算力池化","墨西哥国家级医疗机构在推进多个AI试点项目后，遭遇GPU资源碎片化、利用率低于20%、扩容严重依赖硬件采购等困局。该机构采用AGIOne统一AI效能层解决方案，通过资源池化、异构纳管、模型聚合与混合云弹性四大能力，构建全新的AI运营基础设施。项目落地后GPU利用率实现翻倍以上增长，新AI服务部署周期从\"月\"压缩到\"天\"，为医疗行业AI规模化运营提供了标杆实践。","\u002Ffile\u002Fcase\u002F59\u002F墨西哥医疗机构：AGIOne统一AI效能层驱动GPU利用率增长.pdf","2026-06-16 12:15:15","2026-08-03 08:54:02","卫生医疗",{"tenantId":5,"id":268,"title":269,"desc":270,"intro":271,"industryId":272,"slug":273,"productId":146,"languageId":6,"tags":274,"siteId":6,"cover":275,"coverBig":276,"seoTitle":9,"keywords":277,"description":278,"pdfurl":279,"isTop":21,"status":6,"publishedAt":9,"contentUpdatedAt":9,"sortIndex":9,"platform":9,"createdAt":280,"updatedAt":281,"languageName":15,"productName":47,"productIcon":282,"productCode":283,"industryName":284},44,"某城开集团：用云容灾成功抵御病毒勒索攻击","国内某城开企业业务系统运行于华为 HCS8.1 私有云，选用 HyperBDR 容灾工具，采用云容灾架构实现差异化容灾策略，提升业务韧性。项目实施后，企业业务连续性提升，成功应对一次病毒勒索事件。","背景：某农担金融企业 8 套业务系统运行于本地华为 HCS8.1 私有云，原备份机制有缺陷，传统容灾成本高，亟需优化。\n方案：选用万博智云 HyperBDR，采用华为公有云对象存储，依业务定容灾策略，分阶段交付测试。\n收益：业务连续性提升，成本降低，管理更高效，面对勒索病毒等风险时响应快速，恢复能力强。",22,"chengkai-group-successfully-resists--ransomware-attacks-with-cloud-disaster-recovery","混合云容灾$$HCS到华为云,定制同步策略$$结合业务定制容灾SLA,弹性架构$$容灾资源按量计费","\u002Ffile\u002F2\u002F2025\u002F4\u002Fe07eacec88c6.png?i=8747&name=城开集团.png","\u002Ffile\u002F2\u002F2025\u002F4\u002F0550fc6e1110.png?i=8665&name=地产.png","云容灾,混合云容灾,公有云容灾,对象存储容灾,抵御勒索病毒,业务连续性保护,HyperBDR","国内某城开企业 8 套业务系统运行于本地 IDC 华为 HCS8.1 私有云，原备份方式缺陷明显。企业与万博智云合作，选用 HyperBDR 容灾工具，采用云容灾架构。该方案实现差异化容灾策略，优化资源配置。项目实施后，企业业务连续性提升，成本降低，管理效率提高，应对风险能力增强。","\u002Ffile\u002Fcase\u002F44\u002F某城开集团：用云容灾成功抵御病毒勒索攻击.pdf","2025-04-29 17:31:13","2026-08-03 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台主机安全迁移，交付团队快速掌握工具。","hypermotion-case-studies-migration-south-africa-social-agency","电力不稳$$导致迁移频繁中断,大容量磁盘$$云平台无法自动创建,传统Agent模式$$影响生产业务运营","\u002Ffile\u002F2\u002F2024\u002F12\u002F947e99cd6e4f.jpg?i=252&name=duiker-island-2076042_1280.jpg","\u002Ffile\u002F2\u002F2025\u002F4\u002F80fbc5326fb8.png?i=8664&name=南非.png","南非公共部门,VMware迁移,无代理迁移,电力中断,断点续传,自动化迁移","\u002Ffile\u002Fcase\u002F40\u002F南非公共部门：3天打通迁移卡点.pdf","2024-12-11 12:07:37","2026-08-03 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