SUSE Cloud Observability Features

Tracks infrastructure resource needs and alerts administrators or automatically scales usage to minimize waste. This feature was mentioned in 11 SUSE Cloud Observability reviews.

Monitors performance and statistics related to memory, caches and connections. 11 reviewers of SUSE Cloud Observability have provided feedback on this feature.

Detects anomalies in functionality, user accessibility, traffic flows, and tampering. 12 reviewers of SUSE Cloud Observability have provided feedback on this feature.

Constantly monitors system to detect anomalies in real time. 13 reviewers of SUSE Cloud Observability have provided feedback on this feature.

Enables monitoring of security and compliance standards across cloud infrastructure.

Optimizes resource allocation.

Consistently monitors processes for applications and IT infrastructure to detect anomalies in real-time.

Sets up standard performance baseline to compare live container activities.

Traces connections between different containerized environments and detects anomalies in functionality, user accessibility, traffic flows, and tampering.

Actively monitor status of work stations either on-premise or remote. 11 reviewers of SUSE Cloud Observability have provided feedback on this feature.

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

Efficiently scales resource usage to optimize spend whith increased or decreased resource usage requirements.

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. 13 reviewers of SUSE Cloud Observability have provided feedback on this feature.

Ability to project spend based on contracts, usage trends, and predicted growth.

Allows users to search logs for troubleshooting and open-ended exploration of data.

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

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

Allows users to track log trends.

Utilizes artificial intelligence to analyze big data.

Utilizes machine learning to analyze big data.

Monitors logs and activities from a wide range of IT systems. This feature was mentioned in 10 SUSE Cloud Observability reviews.

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.

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

Directly identifies, or increases identification speed for, root causes for IT system issues. 10 reviewers of SUSE Cloud Observability have provided feedback on this feature.

Proactively identifies trends on IT systems that could lead to failures or errors.

Provides paths, suggestions, or other general assistance towards issue resolution.

Directly identifies, or increases identification speed for, root causes for container issues.

Provides paths, suggestions, or other general assistance towards issue resolution.

Proactively identifies trends on container systems that could lead to failures or errors.

As reported in 10 SUSE Cloud Observability reviews. Integrates with a variety of IT systems.

Automatically alerts necessary parties via email, text, or call when issues are identified. This feature was mentioned in 10 SUSE Cloud Observability reviews.

Generate sreports and dashboards highlighting trends and key metrics around issues and issue resolution.

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

Provides high-frequency metrics data.

Alerts over email, text, phone call, or more to multiple parties.

Provides information related to unnecessary spending and unused resources.

Gives alerts when incidents arise.

Diagnoses and resolves incidents without the need for human interaction.

Efficiently scales resource usage to optimize spend whith increased or decreased resource usage requirements.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

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

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