IN THE FIELD GUIDE

launchdarkly

launchdarkly.com

LaunchDarkly is a runtime control platform that enables teams to manage code and AI agents in production with features like feature flags, progressive delivery, automated rollback, and runtime control. It supports continuous experimentation, self-healing systems, and optimization of AI performance and cost without redeploying code.

THE PRODUCT, BEYOND THE PITCH

Automatically researched · Not editorially reviewed · Sources checked Sep 14, 2026

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A good fit for

Not confirmed yet.

Know the limitations

Not confirmed yet.

What you can do

Progressively release AI-built code and roll back instantly based on real-time impact to ship with confidence.
Control and govern AI agents in production by monitoring behavior, detecting drift, and taking action without redeploying.

Features

Feature flags for controlling code in production with targeting, progressive rollouts, and automated rollbacks.

Integrations

Integrates with CI/CD and observability tools including GitHub, GitLab, Vercel, Datadog, Elastic, Cloudflare, Segment, Sentry, VS Code, Cursor, and Claude.

Platforms & data export

Supports data export of AI traces and metrics for observability.

THE COST FOR YOUR TEAM

Go beyond the starting price.

Published plan prices for your team size and usage. Results update as you type. Taxes, currency conversion and unlisted add-ons are excluded, and anything the source did not state is called out rather than guessed.

PeopleBillingCurrencyPrices are shown as published. No exchange rates are applied.Monthly usage per toolApplies to each tool’s own unit. Leave at 0 for subscription cost only.

Known monthly subtotal

$0.00/month

1 of 1 tools could not be priced with these inputs, so this is not the full cost.

Plan costs based on your requirements
Tool / plan	Monthly	Per year	What this assumes
launchdarkly	No pricing recorded yet. Check the official site, or ask the owner to add it.

A practical workflow

Create a config and add AgentControl to your agent to manage prompts, models, and tools from a UI without redeploying.
Route users or segments to different AI models by configuring targeting without any code changes.
Run offline evaluations against labeled datasets and review detailed results alongside aggregate scores.

Based on the sources below. Editorial review does not imply hands-on product testing.

Alternatives to explore

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Candidates based on category and primary feature. Check feature and pricing differences before switching.

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What changed

Changes to the facts recorded here, not a live scan of every vendor update. Save this tool to follow updates in your account.

9/14/2026
Updated: best for, export formats, features, integrations, platforms, summary, use cases, walkthrough
See recorded changes
bestFor
Before: ["Development teams building and deploying AI agents and code who need runtime control and safe progressive delivery.","Teams requiring integration of runtime control with existing CI/CD and observability tools."]
After: []
exportFormats
Before: ["Warehouse-native integration with Snowflake, Redshift, Databricks, BigQuery and more for metrics and experimentation data."]
After: ["Supports data export of AI traces and metrics for observability."]
features
Before: ["Feature flags for controlling code behavior in production.","Progressive rollouts to gradually release changes and reduce risk.","Automated rollback to instantly revert changes based on real-time impact.","AgentControl to configure, evaluate, roll out, and observe AI agents in production.","Experimentation capabilities including A/B/n testing and multi-armed bandits for optimizing code and agents."]
After: ["Feature flags for controlling code in production with targeting, progressive rollouts, and automated rollbacks."]
integrations
Before: ["Integrates with GitHub, GitLab, Vercel, Datadog, Elastic, Cloudflare, Segment, Sentry, VS Code, Cursor, and Claude."]
After: ["Integrates with CI/CD and observability tools including GitHub, GitLab, Vercel, Datadog, Elastic, Cloudflare, Segment, Sentry, VS Code, Cursor, and Claude."]
platforms
Before: ["Supports 25+ native SDKs across frontend, backend, mobile, AI, and more for runtime control."]
After: []
summary
Before: "LaunchDarkly is a runtime control platform that enables teams to manage code and AI agents in production with features like feature flags, progressive delivery, automated rollback, and runtime control. It supports continuous experimentation, self-healing systems, and real-time monitoring without redeploying code."
After: "LaunchDarkly is a runtime control platform that enables teams to manage code and AI agents in production with features like feature flags, progressive delivery, automated rollback, and runtime control. It supports continuous experimentation, self-healing systems, and optimization of AI performance and cost without redeploying code."
useCases
Before: ["Progressively releasing AI-built code and rolling back instantly based on real-time impact.","Controlling and governing AI agents in production by monitoring behavior and steering responses in real time."]
After: ["Progressively release AI-built code and roll back instantly based on real-time impact to ship with confidence.","Control and govern AI agents in production by monitoring behavior, detecting drift, and taking action without redeploying."]
walkthrough
Before: ["Create and deploy an agent, then use AgentControl to monitor and change its behavior in production without shipping code.","Update prompts, models, and tools without redeploying by editing variations that take effect immediately.","Route users or segments to different models without code changes using targeting configurations.","Run offline evaluations against labeled datasets and review detailed results to compare agent performance."]
After: ["Create a config and add AgentControl to your agent to manage prompts, models, and tools from a UI without redeploying.","Route users or segments to different AI models by configuring targeting without any code changes.","Run offline evaluations against labeled datasets and review detailed results alongside aggregate scores."]

Sources & research

How we research, calculate costs, and distinguish sponsorship