LaunchDarkly Features

Configures and accurately measures an appropriate measure of engagement, such as average session length.

Accurately measures bounce rate.

Tracks user return rates and related metrics.

Tracks what proportion of users convert via a certain action.

Identifies user demographics like gender or age.

Enables the view of analytic data based on user segment.

Identifies users locations.

Enables you to define custom events for analysis.

Provides real-time reports.

Measures user acquisition analytics.

Provides a holistic summary of your data.

Displays a users progress throughout specific paths in an application.

Displays a users activity history.

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.

Ability to see analytics at the account level.

Ability to see analytics at the user level.

Ability to compare usage across segments such as role, company size, etc.

See how users move though a product via grouped page sequences

View analytics for multiple products in a single dashboard, including cross-product engagement

Breadth and functionality of integrations with third-party products.

Maintain, or facilitate maintenance of, tested and approved code in a deployment-ready state for manual pushes to production.

Integrate with other development and testing software, such as continuous integration or test automation tools.

Plug-in capable for added resources, features, and functions within the product itself.

Empowers users to smoothly deploy features in a variety of environments.

Allows teams to test features in production for select target groups without deploying.

Presents users with a low-code interface to build and maintain feature management systems.

Automatically adjusts resources base on application usage.

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

Designate the necessary tasks and workflows for a team's unique development cycle.

Generate visual dashboards and reports around development cycle progress.

Automate repository detection, version control, testing, and more.

Grants users a centralized dashboard for organized feature flag management.

Grants users granular control over feature rollouts and rollbacks.

Allows users to track the status of deployed features and application performance via automated alerts.

Give users in-app method of reporting bugs and leaving general performance feedback.

Give testers in-app method of reporting bugs and leaving general performance feedback.

Give team members method of reporting bugs and leaving comments on bug status.

Track history of bug status by application version, date, etc.

Store bug tracking data for an appropriate and useful amount of time.

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.

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

Provides high-frequency metrics data.

Provides reporting and analytics tools to quantify the results of the experiment.

Allows for the testing of multiple variables at a time during a single test.

Allows multiple experiments to be deployed simultaneously.

Allows mobile web pages to be tested during A/B experiments.

Provides users with the ability to test and optimize prompts to improve LLM output quality and efficiency.

Offers tools for users to compare multiple LLMs side-by-side based on performance, speed, and accuracy metrics.

Gives users tools to integrate LLM functionality into their existing applications through SDKs and APIs, simplifying development.

Offers users the capability to deploy models quickly to production environments with minimal effort and configuration.

Provides users with live insights into model accuracy, latency, and user interaction, helping them identify and address issues promptly.

Offers users tools to set access permissions for different roles, ensuring only authorized personnel can interact with or modify LLM resources.

Ingests and processes multiple telemetry types, such as logs, metrics, and traces.

Supports ingestion and standardization of observability data via OpenTelemetry protocol.

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

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Capability to perform complex tasks without constant human input

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

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

Works across multiple software systems or databases

Improves performance based on feedback and experience

Tracks changes to prompts over time.

Helps users run test outputs across different models.

Tracks execution time of prompts across models.

Helps in monitoring token consumption and API call frequency.

Identifies expensive prompts to help teams optimize for cost efficiency.

Helps in A/B testing of prompts across models.

Helps to plug into CI/CD for automated testing, validation, and deployment of prompts.

Helps generating reliable and consistent AI outputs.

Lets teams configure execution order and dependencies among AI components, ensuring workflows run smoothly and logically.

Streamlines the management of multiple AI models and agents by bringing them together into cohesive, automated workflows for complex business tasks.

Connects with a wide array of AI model APIs and external services, regardless of provider, within a single orchestration platform.

Enables the construction of sophisticated, multi-step AI workflows that combine specialized models for end-to-end process automation.

Integrates seamlessly with enterprise platforms such as CRMs, databases, and business applications for unified data and process flow.

Supports the setup of live data pipelines that move information instantly between AI components and enterprise systems.

Offers comprehensive dashboards to visualize KPIs and performance metrics for all orchestrated AI workflows.

Generates in-depth reports on workflow performance, resource consumption, and the overall business impact of AI orchestration.

Tracks and displays resource usage across all AI components, enabling proactive performance tuning and troubleshooting.

Dynamically manages and allocates computational resources, including GPU clusters, to maximize efficiency based on current workflow needs.

Scales AI workloads up or down automatically, adapting to fluctuations in processing demands and user traffic.

Provides insights into data flow, processing times, and success rates for each individual AI component within a workflow.

Ensures all AI workflows adhere to relevant regulatory standards and industry requirements.

Empowers organizations to enforce governance policies and compliance controls across orchestrated AI operations.

Facilitates the setup of role-based permissions and approval workflows for sensitive AI operations and deployments.

Maintains comprehensive logs and audit trails of workflow executions to support compliance and troubleshooting efforts.

Applies robust security protocols and policies to protect sensitive AI deployments and data flows.

Capability to perform complex tasks without constant human input

Improves performance based on feedback and experience

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Engages in human-like conversation for task delegation

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

View analytics for multiple products in a single dashboard, including cross-product engagement

Ability to see analytics at the user level.

Ability to see analytics at the account level.

Ability to compare usage across segments such as role, company size, etc.

See how users move though a product via grouped page sequences

Scores customers and visitors to identify at-risk or thriving accounts.

View analytics for multiple products in a single dashboard, including cross-product engagement

Anticipates needs and offers suggestions without prompting

Capability to perform complex tasks without constant human input

Works across multiple software systems or databases

Receive automated alerts and reports based on gains or losses in performance.

Breadth and functionality of integrations with third-party products.