Splunk Observability Cloud Features

Provides users with real-time information on how individuals are using accessibility accomodation tools to interact with the site or application.

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

Allow administrators to gain valuable insights related to end-user behavior.

Monitors web-content ensuring all assets and resources are displayed as intended, alerting administrators as issues arise.

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

Provides information related to unnecessary spending and unused resources relating to their digital offerings.

Generates alerts for whenever a website or application goes down, experiences an error, makes a major change, or other events.

Provides tools to track and measure application or website performance.

Track problems and manage resolutions related to end-user experience issues.

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

Tracks infrastructure resource needs and alerts administrators or automatically scales usage to minimize waste. 13 reviewers of Splunk Observability Cloud have provided feedback on this feature.

As reported in 12 Splunk Observability Cloud reviews. Monitors performance and statistics related to memory, caches and connections.

Based on 12 Splunk Observability Cloud reviews. Detects anomalies in functionality, user accessibility, traffic flows, and tampering.

As reported in 12 Splunk Observability Cloud reviews. 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. This feature was mentioned in 13 Splunk Observability Cloud reviews.

As reported in 10 Splunk Observability Cloud reviews. 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. 13 reviewers of Splunk Observability Cloud 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 12 Splunk Observability Cloud reviews.

Presents information and analytics in a digestible, intuitive, and visually appealing way. 13 reviewers of Splunk Observability Cloud have provided feedback on this feature.

Ability to project spend based on contracts, usage trends, and predicted growth. This feature was mentioned in 11 Splunk Observability Cloud reviews.

Based on 12 Splunk Observability Cloud reviews. Presents information and analytics in a digestible, intuitive, and visually appealing way.

As reported in 12 Splunk Observability Cloud reviews. Clearly notifies users with relevant information and anomalies in a timely manner.

Creates reports outlining log activity and relevant metrics. 11 reviewers of Splunk Observability Cloud have provided feedback on this feature.

Automatically allocates resources to address log anomalies. This feature was mentioned in 12 Splunk Observability Cloud reviews.

Sets a standard performance baseline by which to compare log activity. This feature was mentioned in 12 Splunk Observability Cloud reviews.

Constantly monitors logs to detect anomalies in real time. 12 reviewers of Splunk Observability Cloud have provided feedback on this feature.

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.

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

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