NETSCOUT Network & Application Observability Features

Continuously monitors network performance across the entire span of a network.

Sends alerts via pop-up notifications, texts, emails, or calls regarding network issues or failures.

Suggests potential remedies or improvements to slowdowns, errors, or failures.

Provides monitoring capabilities for multiple networks at once.

Monitor usage and activity on a diverse array of IT systems, e.g., servers, databases, and networks.

Generate real-time high-level and/or in-depth analytics regarding monitored systems activity.

Generate insights into IT systems utilizing event metrics, logging, traces, and metadata.

Integrate AI and/or machine learning capabilities to identify and address potential and active failures and errors.

Monitors and test apps to address issues before they affect end users.

Provides dynamic end-to-end maps of every single transaction.

Monitors cloud microservices, containers, kubernetes, and other cloud native software.

Sets a standard performance baseline by which to compare live network activity.

Charts or graphs live and historical network performance for quick status checking and analysis.

Gives insights into which specific network paths are performing suboptimally.

Consolidate IT systems monitoring overviews to a single pane for quick insights.

Offer pre-built and custom reporting and dashboards for quick insights into system states.

Set expected baselines for system functions.

Create and distribute detailed alerts via email, phone, and messaging for potential and active failures and errors.

Tracks infrastructure resource needs and alerts administrators or automatically scales usage to minimize waste.

Monitors performance and statistics related to memory, caches and connections.

Detects anomalies in functionality, user accessibility, traffic flows, and tampering.

Constantly monitors system to detect anomalies in real time.

Enables monitoring of security and compliance standards across cloud infrastructure.

Actively monitor status of work stations either on-premise or remote.

Allows users to track and control cloud spend across cloud services and providers.

Automatically scales resources based on demand and optimizes for performance and cost.

Creates reports outlining resource, underutilization, cost trends, and/or functional overlap.

Presents information and analytics in a digestible, intuitive, and visually appealing way.

Provides high-frequency metrics data.

Offers full observability of all network environments, including on-premises, cloud, SD-WAN, WLAN, and edge computing.

Automatically detects and maps all network devices and architectures, reducing manual effort and improving visibility.

Provides true real-time network monitoring to detect and resolve issues instantly, rather than relying on near-time data.

Uses AI-driven analysis of historical and real-time data to forecast network issues before they impact performance.

Combines packet and flow analysis techniques to provide a comprehensive view of network traffic and performance.

Ensures all monitored network data is securely encrypted both in transit and at rest.

Supports integration with Zero Trust frameworks and identity management solutions to enhance network security.

Unifies network performance monitoring with security intelligence to identify and mitigate threats in real-time.

Uses machine learning to identify network anomalies, preventing security and performance issues before they escalate.

Ingests and processes multiple telemetry types, such as logs, metrics, and traces.

Supports ingestion and standardization of observability data via OpenTelemetry protocol.

Displays relationships between services to visualize system dependencies.

Provides a consolidated view of system-wide telemetry in a single dashboard.

Allows users to explore and visualize distributed traces and span relationships.

Correlates logs, metrics, and traces to surface performance patterns and root causes.

Identifies likely causes of issues using system insights and correlation logic.

Automatically alerts users to anomalies or critical events using contextual data.

Provides observability into containerized workloads and Kubernetes clusters.

Enables observability across public cloud, private cloud, and on-prem environments.

Forecasts future system issues based on historical performance trends.

Summarizes incident root causes and potential fixes using generative AI.

Uses machine learning to detect unusual behavior across telemetry data.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Works across multiple software systems or databases

Improves performance based on feedback and experience

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Utilizes machine learning to automatically detect and alert on unusual patterns in infrastructure metrics.

Provides AI-generated insights and actionable recommendations to optimize resource performance and cost.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives