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Azure Monitor Features
What are the features of Azure Monitor?
Monitoring
- Usage Monitoring
- Database Monitoring
- API Monitoring
- Real-Time Monitoring - Cloud Infrastructure Monitoring
Administration
- Activity Monitoring
- Multi-Cloud Management
- Automation
Analysis
- Reporting
- Dashboards and Visualizations
- Spend Forecasting and Optimization
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Azure Monitor Categories on G2
Filter for Features
Monitoring
Usage Monitoring | As reported in 22 Azure Monitor reviews. Tracks infrastructure resource needs and alerts administrators or automatically scales usage to minimize waste. | 87% (Based on 22 reviews) | |
Database Monitoring | Monitors performance and statistics related to memory, caches and connections. 20 reviewers of Azure Monitor have provided feedback on this feature. | 88% (Based on 20 reviews) | |
API Monitoring | Based on 20 Azure Monitor reviews. Detects anomalies in functionality, user accessibility, traffic flows, and tampering. | 84% (Based on 20 reviews) | |
Real-Time Monitoring - Cloud Infrastructure Monitoring | Constantly monitors system to detect anomalies in real time. 22 reviewers of Azure Monitor have provided feedback on this feature. | 88% (Based on 22 reviews) | |
Security and Compliance Monitoring | Enables monitoring of security and compliance standards across cloud infrastructure. | Not enough data | |
Performance Baselines | Not enough data | ||
Performance Analysis | Not enough data | ||
Performance Monitoring | Not enough data | ||
AI/ML Assistance | Not enough data | ||
Multi-System Monitoring | Not enough data |
Administration
Activity Monitoring | Actively monitor status of work stations either on-premise or remote. This feature was mentioned in 21 Azure Monitor reviews. | 90% (Based on 21 reviews) | |
Multi-Cloud Management | Allows users to track and control cloud spend across cloud services and providers. 19 reviewers of Azure Monitor have provided feedback on this feature. | 85% (Based on 19 reviews) | |
Automation | As reported in 21 Azure Monitor reviews. Efficiently scales resource usage to optimize spend whith increased or decreased resource usage requirements. | 87% (Based on 21 reviews) | |
Auto-Scaling & Resource Optimization | Automatically scales resources based on demand and optimizes for performance and cost. | Not enough data |
Analysis
Reporting | As reported in 21 Azure Monitor reviews. Creates reports outlining resource, underutilization, cost trends, and/or functional overlap. | 85% (Based on 21 reviews) | |
Dashboards and Visualizations | Presents information and analytics in a digestible, intuitive, and visually appealing way. This feature was mentioned in 22 Azure Monitor reviews. | 86% (Based on 22 reviews) | |
Spend Forecasting and Optimization | Ability to project spend based on contracts, usage trends, and predicted growth. 19 reviewers of Azure Monitor have provided feedback on this feature. | 85% (Based on 19 reviews) |
Response
Dashboards and Visualization | Not enough data | ||
Incident Alerting | Not enough data | ||
Root Cause Analysis (RCA) | Not enough data |
Performance
Real User Monitoring (RUM) | Captures and analyzes each transaction by users of a website or application in real time. | Not enough data | |
Second by Second Metrics | Provides high-frequency metrics data. | Not enough data |
Functionality
Synthetic Monitoring | Monitors and test apps to address issues before they affect end users. | Not enough data | |
Dynamic Transaction Mapping | Provides dynamic end-to-end maps of every single transaction. | Not enough data | |
Load Balancing | Automatically adjusts resources base on application usage. | Not enough data | |
Cloud Observability | Monitors cloud microservices, containers, kubernetes, and other cloud native software. | Not enough data |
Telemetry Collection & Ingestion - Observability
Multi-Telemetry Ingestion | Ingests and processes multiple telemetry types, such as logs, metrics, and traces. | Not enough data | |
OpenTelemetry Support | Supports ingestion and standardization of observability data via OpenTelemetry protocol. | Not enough data |
Visualization & Dashboards - Observability
Service Dependency Mapping | Displays relationships between services to visualize system dependencies. | Not enough data | |
Unified Dashboard | Provides a consolidated view of system-wide telemetry in a single dashboard. | Not enough data | |
Trace Visualization | Allows users to explore and visualize distributed traces and span relationships. | Not enough data |
Correlation & Root Cause Analysis - Observability
Cross-Telemetry Correlation | Correlates logs, metrics, and traces to surface performance patterns and root causes. | Not enough data | |
Root Cause Detection | Identifies likely causes of issues using system insights and correlation logic. | Not enough data | |
Intelligent Alerting | Automatically alerts users to anomalies or critical events using contextual data. | Not enough data |
Scalability & Ecosystem Integration - Observability
Kubernetes Monitoring | Provides observability into containerized workloads and Kubernetes clusters. | Not enough data | |
Hybrid/Multi-Cloud Support | Enables observability across public cloud, private cloud, and on-prem environments. | Not enough data |
AI Features - Observability
Predictive Insights | Forecasts future system issues based on historical performance trends. | Not enough data | |
AI-Generated Incident Summaries | Summarizes incident root causes and potential fixes using generative AI. | Not enough data | |
AI Anomaly Detection | Uses machine learning to detect unusual behavior across telemetry data. | Not enough data |
Agentic AI - Application Performance Monitoring (APM)
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
Agentic AI - Cloud Infrastructure Monitoring
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Natural Language Interaction | Engages in human-like conversation for task delegation | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
AI Automation - Cloud Infrastructure Monitoring
AI-Powered Anomaly Detection | Utilizes machine learning to automatically detect and alert on unusual patterns in infrastructure metrics. | Not enough data | |
AI-Driven Insight Recommendations | Provides AI-generated insights and actionable recommendations to optimize resource performance and cost. | Not enough data |
Agentic AI - Observability Software
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Natural Language Interaction | Engages in human-like conversation for task delegation | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
