groundcover Features

Tracks infrastructure resource needs and alerts administrators or automatically scales usage to minimize waste. This feature was mentioned in 12 groundcover reviews.

Monitors performance and statistics related to memory, caches and connections. This feature was mentioned in 12 groundcover reviews.

Detects anomalies in functionality, user accessibility, traffic flows, and tampering. This feature was mentioned in 12 groundcover reviews.

Constantly monitors system to detect anomalies in real time. This feature was mentioned in 12 groundcover reviews.

Enables monitoring of security and compliance standards across cloud infrastructure.

As reported in 10 groundcover reviews.

This feature was mentioned in 10 groundcover reviews.

Based on 10 groundcover reviews.

Actively monitor status of work stations either on-premise or remote. 12 reviewers of groundcover have provided feedback on this feature.

Allows users to track and control cloud spend across cloud services and providers. 11 reviewers of groundcover have provided feedback on this feature.

Efficiently scales resource usage to optimize spend whith increased or decreased resource usage requirements. 10 reviewers of groundcover have provided feedback on this feature.

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

Creates reports outlining resource, underutilization, cost trends, and/or functional overlap. This feature was mentioned in 11 groundcover reviews.

As reported in 12 groundcover reviews. Presents information and analytics in a digestible, intuitive, and visually appealing way.

Based on 11 groundcover reviews. Ability to project spend based on contracts, usage trends, and predicted growth.

As reported in 14 groundcover reviews. Presents information and analytics in a digestible, intuitive, and visually appealing way.

Clearly notifies users with relevant information and anomalies in a timely manner. 14 reviewers of groundcover have provided feedback on this feature.

Creates reports outlining log activity and relevant metrics. 14 reviewers of groundcover have provided feedback on this feature.

Automatically allocates resources to address log anomalies. 13 reviewers of groundcover have provided feedback on this feature.

Sets a standard performance baseline by which to compare log activity. This feature was mentioned in 13 groundcover reviews.

Constantly monitors logs to detect anomalies in real time. 13 reviewers of groundcover have provided feedback on this feature.

Captures and analyzes each transaction by users of a website or application in real time.

Provides high-frequency metrics data.

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

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

Automatically adjusts resources base on application usage.

Based on 10 groundcover reviews. Monitors cloud microservices, containers, kubernetes, and other cloud native software.

Generate real-time depth analytics utilizing event metrics, logging and metadata.

Use custom or pre-built tests for buisness rules. to ensure data quality.

Involves automation capabilities to identify and track issues, failed operations by looking at historical trends.

Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility.

Identify the different type of anomalies and receive alerts.

The data observability environment can be viewed from a single dashboard.

Provides immediate alerts for any anomalies or expected events.

Establishes lineage for the data pipeline - from data warehouse to the data user.

Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts.

Allows users to generate text based on a text prompt.

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

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

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

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