Welcome to Guance

Welcome to the Guance Documentation!

Guance is dedicated to providing modern monitoring and observability cloud services

Unifying monitoring needs for cloud, cloud-native, applications, and business in the AI era

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Tech Stack Integrations

Seamlessly connect various applications, databases, infrastructure, and public cloud platforms to achieve cross-platform data collection, analysis, and monitoring

Log Analysis

Provides one-stop services for log collection, parsing, indexing, storage, and retrieval, supporting full-text search, field filtering, pattern analysis, and real-time monitoring

RUM

Captures real user behavior data on web and mobile, records page performance and user interactions, supports session replay for issue localization, and correlates frontend and backend data for full-chain root cause analysis

How to access an application?

APM

Builds service topology through distributed tracing to achieve full-stack performance monitoring. When services are abnormal, quickly locate code-level bottlenecks to solve performance issues like slow queries and API timeouts

Start Trace Data Collection

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01 What is Guance?

A one-stop observability platform covering infrastructure, APM, RUM, logs, LLM monitoring, and more, meeting comprehensive monitoring needs in the cloud-native and AI era

02 Quick Start

After registering with Guance, install DataKit and access data, then configure views in visual interfaces like dashboards to directly view monitoring results

03 Start Data Collection

Enable data collection via DataKit: after one-click installation, configure the required collectors and restart the service to start receiving data

Core Features

Scenarios

As the core module of data visualization, it includes various display forms such as dashboards (comprehensive data monitoring views), explorers (custom data query interfaces), and notes (combining text and images), supporting flexible construction of business monitoring scenarios

Event Center

System records of abnormal states or important changes at specific times, automatically triggered by monitors and SLO rules, containing status levels, timestamps, sources, and detailed context information, supporting fault discovery and root cause localization

Incident

Aggregates anomalies from intelligent detection, monitor alerts, and manual creation, displays complete context information through correlation analysis, supports team collaboration for event status tracking and handling process recording, and ultimately achieves closed-loop management of abnormal events

Infrastructure

Provides unified monitoring for basic architecture components like hosts, containers, cloud resources, and network devices, collects performance metrics such as CPU, memory, disk, and network in real-time, and visualizes component relationships and health status through resource topology maps

Metrics

Time-series-based system status data, containing four elements: name, value, tags, and timestamp, supports automatic collection, storage, and multi-dimensional analysis, covering full-stack monitoring needs from infrastructure and application performance to business operations

Synthetic Tests

Simulates user requests through globally distributed monitoring nodes to detect the availability and response performance of services like websites, APIs, and ports, providing multi-region availability statistics, performance comparison analysis, and fault alerts

Security Monitoring

Continuously monitors system security status, including security event log analysis, vulnerability scan result monitoring, abnormal login detection, compliance policy checks, providing security posture assessment and risk warnings

Monitoring

Integrated monitoring system that detects data anomalies through monitors and triggers alerts, managed with alert policies and notification functions, assisted by mute and SLO for fine-grained governance

LLM Monitoring

Specialized monitoring solution for large language model applications, providing API call performance monitoring, Token usage statistics, response quality evaluation, cost analysis, and usage forecasting to ensure the stability and cost control of AI applications