Splunk Infrastructure Monitoring 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.

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.

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.

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.

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

Allows user to track log trends.

Analyze both metric and event data on the same platform regardless of source or structure

Search your logs for troubleshooting and open-ended exploration of your data

Create alerts based on search patterns, thresholds for specific log metrics, or other conditions

See your data, in real time, streaming into the system from multiple data sources

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.

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

Clearly notifies users with relevant information and anomalies in a timely manner.

Creates reports outlining performance and other relevant metrics.

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

Clearly notifies users with relevant information and anomalies in a timely manner.

Creates reports outlining log activity and relevant metrics.

Allows users to track and control resource allocation across multiple devices.

Automatically allocates resources to optimize loads across devices.

Sets a standard performance baseline by which to compare live hardware metrics.

Constantly monitors hardware to detect anomalies in real time.

Automatically allocates resources to address log anomalies.

Sets a standard performance baseline by which to compare log activity.

Constantly monitors logs to detect anomalies in real time.

Allows users to track and control resource spend and loads across multiple servers.

Automatically allocates resources to optimize loads across servers.

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

Constantly monitors server to detect anomalies in real time.

Ability of the monitoring software to immediately alert the user in case of any issue.

Deploys remedial actions proactively. If this measure fails, then the user is alerted.

Based on 10 Splunk Infrastructure Monitoring reviews. Presents information and analytics in a digestible, intuitive, and visually appealing way.

Clearly notifies team members with relevant information and anomalies in a timely manner.

Creates reports outlining load distribution, performance and other relevant metrics. This feature was mentioned in 10 Splunk Infrastructure Monitoring reviews.

Automatically collect logs from all your services, applications, and platforms

Automate the indexing of machine data that's critical to your operations

Mix and match data from apps, hosts, containers, services, networks, and more

Visualize log data on dashboards

Drill down and explore data to discover new insights

Supports public, hybrid and private cloud environments.

Software can scale as the enterprises adds more servers to their portfolio virtually.

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.

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.

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.

Continuously analyzes and adjusts network configurations to optimize performance and reliability.

Automates routine network monitoring tasks, reducing operational overhead and human error.

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

Automates corrective actions for common network issues, reducing downtime and manual intervention.

Leverages AI to anticipate hardware failures, capacity issues, and performance bottlenecks before they occur.

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

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

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

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

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