Better Stack Features

Tracks where users are clicking on a web page.

Tracks mouse movement over a web page.

See where users experience the most difficulty on a web page.

Tracks sessions where users encountered errors and bugs.

Split web traffic to a page between two different URLs.

Offers insights into relationships between user information and user behavior.

Leave notes on sessions and bugs.

Collects user information (location, IP address, email, etc.).

Search sessions by keywords or IP address.

Sets and allows adjustment of website baseline functionality and configuration.

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

Monitor multiple websites at once, organizing them with dashboards.

Generate reports manually or automatically about website performance.

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

Isolate geographic locations where website functionality is best and worst.

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.

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.

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.

Monitors systems constantly in real-time.

Alerts users of incidents and issues as soon as they arise.

Generates accurate incident reports.

Utillizes AI to monitor and report on incidents in real-time.

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.

Identify and predict anomalies in real-time with outlier detection and uncover root-causes

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

Assigns tickets to relevant team members.

Cultivates a standardized workflow for enhanced organization.

Grants transparent overviews for the lifecycle of each incident.

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

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

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

Integrates with a variety of IT systems.

Automatically alerts necessary parties via email, text, or call when issues are identified.

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

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.

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.

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

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

See log data in context with automated tagging and correlation

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

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

Provides high-frequency metrics data.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

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.

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

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

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

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

Correlates logs, metrics, traces, and alerts to isolate high-signal issues faster.

Links incidents to recent deployments, commits, configuration changes, or feature flag updates.

Detects abnormal behavior using dynamic baselines, auto-adaptive thresholds, or learned patterns.

Correlates related alerts and suppress duplicates to reduce alert fatigue and focus attention.

Routes incidents to the right responders and enrich on-call actions with service and incident context.

Tracks remaining error budget and burn rate against reliability targets.

Defines service level indicators and objectives for services, user journeys, or business-critical operations.

Model service dependencies and surface health, impact, and reliability status for stakeholders.

Assembles timelines, evidence, and summaries that guide responders through triage and diagnosis.

Captures code-level diagnostics in running systems without redeploying or restarting services.

Identifies likely causes across services and dependencies using topology, causality, or learned relationships.

Validates suspected causes with live runtime evidence instead of static inference alone.

Executes runbooks and multi-step workflows automatically in response to alerts or incidents.

Inserts approvals or decision gates before high-impact remediation steps are executed.

Embed reliability workflows into existing tools through integrations and APIs while enforcing secure access across hybrid or legacy environments.

Generates remediation guidance or code changes from incident context and observed telemetry.