大模型运维监控LLM Observability Platform
为大模型生产环境打造的全链路可观测平台。Token 级调用追踪、回答质量评估、提示词回归测试、成本与安全审计，让每一次模型调用都看得见、管得住、可优化。Full-stack observability for LLM production: token-level tracing, answer quality evaluation, prompt regression testing, cost and security auditing.
为大模型应用量身定制的可观测体系Observability Built for LLMs
从用户提问到模型回答，五层观测维度覆盖大模型应用全链路。Five observation dimensions covering the full LLM request lifecycle.
核心能力Core Capabilities
大模型上线只是开始，持续可靠才是关键。Launching an LLM is the start — reliability is the journey.
一次提问背后的检索、路由、生成全链路可视化，慢在哪、贵在哪一目了然。Full pipeline visualization for every request.
基于 LLM-as-a-Judge 的自动打分 + 人工抽检，50+ 内置质量维度。LLM-as-a-Judge scoring with 50+ metrics.
模型升级、数据漂移导致的质量下降实时告警，防患于未然。Real-time alerts on quality degradation.
提示词与模型版本变更前自动回归，避免"改一处坏一片"。Automated regression before every change.
敏感内容、提示词注入、越狱攻击检测，对话留存满足审计合规。Content, injection and jailbreak detection.
按应用/部门/场景分摊 Token 成本，路由优化建议平均节省 35%。Cost allocation with 35% average savings.
面向千行百业的落地实践Real-world Deployments
生产级大模型应用离不开生产级监控。Production LLMs need production observability.
客服回答质量实时把关，合规审计留痕，投诉率下降 40%。Quality gating with audit trails, -40% complaints.
政策问答准确率持续监控，错误回答即时拦截纠正。Continuous accuracy monitoring with interception.
RAG 检索命中率优化，知识库短板自动发现。RAG hit-rate optimization insights.
答疑质量与学生反馈关联分析，持续改进教学模型。Quality-feedback correlation analysis.
多租户 Token 成本精确分摊，路由调优节省 35% 成本。Per-tenant cost allocation, -35% spend.
医疗建议类回答 100% 留痕审计，风险内容实时告警。Full audit for medical advice.
用数据说话Proven by Numbers
协同产品矩阵Product Matrix
被监控的模型 API 服务本体。The LLM services being monitored.
了解详情Learn more算力与设备层的运维调度。Infrastructure-level ops.
了解详情Learn more承载推理负载的算力底座。The compute foundation.
了解详情Learn more含监控体系建设的一体化交付。Delivery includes observability.
了解详情Learn more让您的大模型应用时刻保持最佳状态Keep Your LLMs at Their Best
支持对现有大模型应用免费接入试用，24 小时生成首份质量与成本诊断报告。Free onboarding trial — first quality & cost diagnostic report within 24 hours.