Product Updates

LLM Observability gives teams visibility into how GenAI applications behave in production. It tracks not only performance metrics like latency and error rates, but also semantic details, including prompts, token usage, and responses. With LaunchDarkly’s LLM Observability, you can debug, monitor, and improve both the performance and quality of your AI-driven features.

Learn more about LLM Observability on the LD docs site

Experiment discussions bring contextual collaboration directly into LaunchDarkly experiments. Product, engineering, and data teams can plan, monitor, and analyze experiments with built-in comments to help connect the dots. All stakeholders can now discuss experiment setup, results, and decisions right alongside the experiment data, alleviating the need to jump between external tools.

Learn more about Experimentation on the LaunchDarkly docs site

Heatmaps surface where users engage most (or least), revealing usability issues and validating design decisions. They turn raw behavioral data into actionable insights for product, design, and growth teams—without requiring deep analytics setup.

LaunchDarkly heatmaps are a visualization layer that aggregates user interactions, like taps, clicks, or scrolls, into color-coded intensity maps over your app or website.

Get started by installing the web session replay plugin to create your first heatmap (dashboards page).

The LaunchDarkly Developer Toolbar is now available in public beta! The toolbar lets you use LaunchDarkly directly in your local development workflow—no need to switch between your IDE and the LaunchDarkly UI.

You can view feature flags in use, override flag values locally, and listen for incoming events like flag evaluations in real time. You can also identify missing flags and link directly to your LaunchDarkly project to create them.

This is a big step toward making it easier for you to develop and test with LaunchDarkly locally.

Get started with the Developer Toolbar documentation

Experimentation customers can now add metrics or attributes to an ongoing experiment—no need to restart or recreate it.

This update gives teams more flexibility and removes the pain of re-randomization when they want to track new measures or slice results by additional attributes mid-experiment.

Metrics and Attributes Anytime is available for hosted experimentation in all regions (excluding Snowflake-native experiments).

See our documentation for Metrics and Attributes in Experiments for more details