Autogenerated metrics | LaunchDarkly

Overview

This topic explains the metrics LaunchDarkly automatically generates from SDK events and how you can use them to monitor the health of your applications.

Metric events

An “event” happens when someone takes an action in your app, such as clicking on a button, or when a system takes an action, such as loading a page. Your SDKs send these metric events to LaunchDarkly, where, for certain event kinds, LaunchDarkly can automatically create metrics from those events. You can use these metrics with experiments and guarded rollouts to track how your flag changes affect your customers’ behavior.

LaunchDarkly autogenerates metrics from events that are sent:

from AI SDKs used in conjunction with AI Configs
during document load, for browser apps, if you are using the Observability plugin
as part of OpenTelemetry traces using the OpenTelemetry feature for server-side SDKs

Autogenerated metrics are marked on the Metrics list with an autogenerated tag. You can view the events that autogenerated these metrics from the Metrics list by clicking View, then Events.

To learn more, read Metric events and Metric analysis.

Randomization units for autogenerated metrics

LaunchDarkly sets the randomization unit for autogenerated metrics to your account’s default context kind for experiments. For most accounts, the default context kind for experiments is user. However, you may have updated your default context kind to account, device, or some other context kind you use in experiments most often. To learn how to change the default context kind for experiments, read Map randomization units to context kinds.

All autogenerated metrics are designed to work with a randomization unit of either user or request. Depending on your account’s default context kind for experiments, you may need to manually update the randomization unit for autogenerated metrics as needed. The recommended randomization units for each autogenerated metric are listed in the tables below. To learn how to manually update the randomization unit for a metric, read Edit metrics.

To learn more, read Randomization units.

Metrics autogenerated from AI SDK events

An AI Config is a resource that you create in LaunchDarkly and then use to customize, test, and roll out new large language models (LLMs) within your generative AI applications. As soon as you start using AI Configs in your application, you can track how your AI model generation is performing, and your AI SDKs begin sending events to LaunchDarkly.

AI SDK events are prefixed with $ld:ai and LaunchDarkly automatically generates metrics from these events.

Some events generate multiple metrics that measure different aspects of the same event. For example, the $ld:ai:feedback:user:positive event generates a metric that measures the average number of positive feedback events per user, and a metric that measures the percentage of users that generated positive feedback.

The following expandable sections explain the metrics that LaunchDarkly autogenerates from AI SDK events:

Positive AI feedback count $ld:ai:feedback:user:positive

Metric kind: Custom conversion count

Randomization unit: User

Definition:

Measurement method: Count
Unit aggregation method: Sum
Analysis method: Average
Success criterion: Higher is better
Units without events: Include units that did not send any events and set their value to 0

Description: Average number of positive feedback events per context

Example usage: Running an experiment to find out which variation causes more users to click “thumbs up”

Positive AI feedback rate $ld:ai:feedback:user:positive

Metric kind: Custom conversion binary

Randomization unit: Request

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Higher is better
Units without events: Include units that did not send any events and set their value to 0

Description: Percentage of contexts that generated positive AI feedback

Example usage: Running a guarded rollout to make sure there is a positive feedback ratio throughout the rollout

Negative AI feedback count $ld:ai:feedback:user:negative

Metric kind: Custom conversion count

Randomization unit: User

Definition:

Measurement method: Count
Unit aggregation method: Sum
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Average number of negative feedback events per context

Example usage: Running an experiment to find out which variation causes more users to click “thumbs down”

Negative AI feedback rate $ld:ai:feedback:user:negative

Metric kind: Custom conversion binary

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Percentage of contexts that generated negative AI feedback

Example usage: Running an experiment to find out which variation causes more users to click “thumbs down”

Average input tokens per AI completion $ld:ai:tokens:input

Metric kind: Numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: For example, for a chatbot, this might indicate user engagement

Example usage: Running an experiment to find out which variation results in fewer input tokens, reducing cost

Average output tokens per AI completion $ld:ai:tokens:output

Metric kind: Numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Indicator of cost, when charged by token usage

Example usage: Running an experiment to find out which variation results in fewer output tokens, reducing cost

Average total tokens per AI completion $ld:ai:tokens:total

Metric kind: Numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Indicator of cost, when charged by token usage

Example usage: Running an experiment to find out which variation results in fewer total tokens, reducing cost

Average AI completion time $ld:ai:duration:total

Metric kind: Numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Time required for LLM to finish a completion

Example usage: Running an experiment to find out which variation results in faster user completion, improving engagement

AI completion success count $ld:ai:generation:success

Metric kind: Custom conversion count

Randomization unit: User

Definition:

Measurement method: Count
Unit aggregation method: Sum
Analysis method: Average
Success criterion: Higher is better
Units without events: Include units that did not send any events and set their value to 0

Description: Counter for successful LLM completion requests

Example usage: Running an experiment to find out which variation results in more user completion requests (“chattiness”), improving engagement

AI completion error count $ld:ai:generation:error

Metric kind: Custom conversion count

Randomization unit: Request

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Counter for erroneous LLM completion requests

Example usage: Running a guarded rollout to make sure the change doesn’t result in a higher error rate

AI completion error count $ld:ai:generation:error

Metric kind: Custom

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Counter for erroneous LLM completion requests

Example usage: Running a guarded rollout to make sure the change doesn’t result in a higher error rate

AI completion error count $ld:ai:generation:error

Metric kind: Custom

Randomization unit: User

Definition:

Measurement method: Count
Unit aggregation method: Sum
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Counter for erroneous LLM completion requests

Example usage: Running a guarded rollout to make sure the change doesn’t result in a higher number of errors

Average time to first token for AI requests $ld:ai:tokens:ttf

Metric kind: Numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Time required for LLM to generate first token

Example usage: Running a guarded rollout to make sure the change doesn’t result in longer token generation times

As an example, the autogenerated metric in the first expandable section above tracks the average number of positive feedback ratings per user.

Here is what the metric setup looks like in the LaunchDarkly user interface:

Metrics autogenerated from observability events

The LaunchDarkly observability plugins provide error monitoring and metric collection for errors, web vitals, and document loading in your browser application. The functionality is in separate plugins, which you enable in the initialization options for the LaunchDarkly SDK. The observability plugins collect and send data to LaunchDarkly, where you can review metrics, events, and errors from your application.

The observability events are prefixed with $ld:telemetry and LaunchDarkly automatically generates metrics from these events.

These expandable sections explain the metrics that LaunchDarkly autogenerates from events recorded by the observability plugins for LaunchDarkly browser SDKs:

Average Cumulative Layout Shift (CLS) per context (LaunchDarkly) $ld:telemetry:metric:cls

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.

Example usage: Observing the latency of interactions an end user makes with your application

P95 Cumulative Layout Shift (CLS) per context (LaunchDarkly) $ld:telemetry:metric:cls

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.

Example usage: Observing the latency of interactions an end user makes with your application

P99 Cumulative Layout Shift (CLS) per context (LaunchDarkly) $ld:telemetry:metric:cls

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile largest burst per context of layout shift scores for every unexpected layout shift that occurs during the entire lifecycle of a page.

Example usage: Observing the latency of interactions an end user makes with your application

Average Document Load Latency per context (LaunchDarkly) $ld:telemetry:metric:document_load

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average DOM load duration in milliseconds per context

Example usage: Observing the latency of interactions an end user makes with your application

P95 Document Load Latency per context (LaunchDarkly) $ld:telemetry:metric:document_load

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile DOM load duration in milliseconds per context

Example usage: Observing the latency of interactions an end user makes with your application

P99 Document Load Latency per context (LaunchDarkly) $ld:telemetry:metric:document_load

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile DOM load duration in milliseconds per context

Example usage: Observing the latency of interactions an end user makes with your application

Percentage of users with errors (LaunchDarkly) $ld:telemetry:error

Metric kind: Custom conversion binary

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Measures the percentage of contexts that encountered an error at least once. This metric is autogenerated by an initial $ld:telemetry:session:init event and populated by subsequent $ld:telemetry:error events. This means you can use the metric even if your app has not yet generated any errors.

Example usage: Running a guarded rollout to make sure the error change doesn’t result in a higher error rate

Average First Contentful Paint (FCP) per context (LaunchDarkly) $ld:telemetry:metric:fcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.

Example usage: Observing the latency of interactions an end user makes with your application

P95 First Contentful Paint (FCP) per context (LaunchDarkly) $ld:telemetry:metric:fcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.

Example usage: Observing the latency of interactions an end user makes with your application

P99 First Contentful Paint (FCP) per context (LaunchDarkly) $ld:telemetry:metric:fcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile time in milliseconds per context between first navigation to a page and when any part of the page’s content is rendered.

Example usage: Observing the latency of interactions an end user makes with your application

Average First Input Delay (FID) per context (LaunchDarkly) $ld:telemetry:metric:fid

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average time in milliseconds per context between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.

Example usage: Observing the latency of interactions an end user makes with your application

P95 First Input Delay (FID) per context (LaunchDarkly) $ld:telemetry:metric:fid

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile time, in milliseconds per context, between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.

Example usage: Observing the latency of interactions an end user makes with your application

P99 First Input Delay (FID) per context (LaunchDarkly) $ld:telemetry:metric:fid

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile time, in milliseconds per context, between a user’s first interaction (click, tap, or key press) and the time when the browser starts processing event handlers in response to that interaction.

Example usage: Observing the latency of interactions an end user makes with your application

Average Interaction to Next Paint (INP) per context (LaunchDarkly) $ld:telemetry:metric:inp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.

Example usage: Observing the latency of interactions an end user makes with your application

P95 Interaction to Next Paint (INP) per context (LaunchDarkly) $ld:telemetry:metric:inp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.

Example usage: Observing the latency of interactions an end user makes with your application

P99 Interaction to Next Paint (INP) per context (LaunchDarkly) $ld:telemetry:metric:inp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile response time in milliseconds per context of all click, tap, and keyboard interactions during the lifespan of a visit to a page.

Example usage: Observing the latency of interactions an end user makes with your application

Average Largest Contentful Paint (LCP) per context (LaunchDarkly) $ld:telemetry:metric:lcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a page

Example usage: Observing the latency of interactions an end user makes with your application

P95 Largest Contentful Paint (LCP) per context (LaunchDarkly) $ld:telemetry:metric:lcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a page

Example usage: Observing the latency of interactions an end user makes with your application

P99 Largest Contentful Paint (LCP) per context (LaunchDarkly) $ld:telemetry:metric:lcp

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile time in milliseconds per context to render the largest image, text block, or video visible when first navigating to a page

Example usage: Observing the latency of interactions an end user makes with your application

Average Time to First Byte (TTFB) per context (LaunchDarkly) $ld:telemetry:metric:ttfb

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the average time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.

Example usage: Observing the latency of interactions an end user makes with your application

P95 Time to First Byte (TTFB) per context (LaunchDarkly) $ld:telemetry:metric:ttfb

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 95th percentile time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.

Example usage: Observing the latency of interactions an end user makes with your application

P99 Time to First Byte (TTFB) per context (LaunchDarkly) $ld:telemetry:metric:ttfb

Metric kind: Custom numeric

Randomization unit: User

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events from the analysis

Description: Measures the 99th percentile time in milliseconds per context between the request for a resource and when the first byte of a response begins to arrive.

Example usage: Observing the latency of interactions an end user makes with your application

Metrics autogenerated from server-side SDKs using OpenTelemetry

LaunchDarkly’s SDKs support instrumentation for OpenTelemetry traces. Traces provide an overview of how your application handles requests. For example, traces may show that a particular feature flag was evaluated for a particular context as part of a given HTTP request. To learn more, read OpenTelemetry and Sending OpenTelemetry traces to LaunchDarkly.

OpenTelemetry events are prefixed with otel and LaunchDarkly automatically generates metrics from these events.

These expandable sections explain the metrics that LaunchDarkly autogenerates from OpenTelemetry traces:

User HTTP error rate (OpenTelemetry) otel.http.error

Metric kind: Custom conversion binary

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Measures the percentage of users that encountered an error inside HTTP spans at least once, as reported by OpenTelemetry. Useful when running a guarded rollout.

User HTTP 5XX response rate (OpenTelemetry) otel.http.5XX

Metric kind: Custom conversion binary

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Measures the percentage of users that encountered an HTTP 5XX response at least once, as reported by OpenTelemetry. Useful when running a guarded rollout.

User non-HTTP exception rate (OpenTelemetry) otel.exception

Metric kind: Custom conversion binary

Randomization unit: User

Definition:

Measurement method: Occurrence
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Include units that did not send any events and set their value to 0

Description: Measures the percentage of users that encountered an exception outside of HTTP spans at least once, as reported by OpenTelemetry. Useful when running a guarded rollout.

Average request latency (OpenTelemetry) otel.http.latency

Metric kind: Custom numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: Average
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Measures the average request latency, as reported by OpenTelemetry. Useful when running a guarded rollout. For best results, use a ‘request’ randomization unit and send ‘request’ contexts.

P95 request latency (OpenTelemetry) otel.http.latency

Metric kind: Custom numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P95
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Measures the 95th percentile request latency, as reported by OpenTelemetry. For many applications, this represents the experience for most requests. You can adjust the percentile to fit your application’s needs. Useful when running a guarded rollout. For best results, use a ‘request’ randomization unit and send ‘request’ contexts.

P99 request latency (OpenTelemetry) otel.http.latency

Metric kind: Custom numeric

Randomization unit: Request

Definition:

Measurement method: Value/size
Unit aggregation method: Average
Analysis method: P99
Success criterion: Lower is better
Units without events: Exclude units that did not send any events

Description: Measures the 99th percentile request latency, as reported by OpenTelemetry. For many applications, this represents the worst-case experiences. You can adjust the percentile to fit your application’s needs. Useful when running a guarded rollout. For best results, use a ‘request’ randomization unit and send ‘request’ contexts.