Feature Flags · AI Coding · Enterprise Grade

Feature flags built for the AI era

AI ships code faster than teams can validate it. Release confidently, detect issues instantly, and automatically fix problems in production with feature flags optimized for scale and performance.

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Trusted by engineering teams at

Ship features at the speed AI allows—with control

Without runtime control you ship and hope: there's little to no control once changes hit production

LaunchDarkly lets you ship features with control, detect change level issues immediately, and remediate automatically without manual intervention.

Ship with control

Create a feature and tie it to key system and business metrics. Define thresholds for performance, errors, or user impact.

Progressively rollout

Roll out changes gradually while the system continuously monitors performance and acts automatically at the first sign of issues.

Contain and correct automatically

Remediation is instant, automated, and non-disruptive.

Release and observe in production

Don’t wait to find out what is happening: the system observes and handles incidents in real time—no one needs to jump in.

Guardrail metrics on every feature

Define thresholds per flag. LaunchDarkly monitors in real time and rolls back automatically when thresholds are breached.

Root cause attribution in seconds

No manual triage. Just the answer.

Help prevent incidents before they impact users

Restore the last known good variation in seconds, identify what works best, and edit at runtime.

The fastest feedback loop runs in production, not staging

Expose variations to real users and track performance against your defined metrics. Understand how each variation performs under actual usage and automatically promote the winner.

Experiments attached to the flags you already release with

The same flag that controls your rollout becomes the experiment.

Test AI prompts and model variants

Compare models, measure real-world performance and cost, and route traffic to the best option—automatically, in production. All without redeploying.

Continuous optimization

Automatically promote winning variations, adjust exposure, and iterate.

When investigating outages, we noted that 45% required a significant volume of traffic or involved a specific data spread that could not be replicated [in] pre-production. By tightening our use of LaunchDarkly feature flags and paying closer attention to signals related to error rate, infrastructure performance, and response time, we were able to prevent multiple outages and reduce resolution time by 50% when a flag was used compared to a classic deployment.

AlayaCare Engineering Team

Healthcare software platform · Production at scale

Your release process wasn't built for the pace AI codes at

See how engineering teams use feature flags to control what ships, observe production, and iterate on AI features.

Get a Demo

Feature Flags · AI Coding · Enterprise Grade

Feature flags built for the AI era

AI ships code faster than teams can validate it. Release confidently, detect issues instantly, and automatically fix problems in production with feature flags optimized for scale and performance.

Book your personalized demo

Trusted by engineering teams at

Ship features at the speed AI allows—with control

Ship with control

Create a feature and tie it to key system and business metrics. Define thresholds for performance, errors, or user impact.

Progressively rollout

Roll out changes gradually while the system continuously monitors performance and acts automatically at the first sign of issues.

Contain and correct automatically

Remediation is instant, automated, and non-disruptive.

Release and observe in production

Don’t wait to find out what is happening: the system observes and handles incidents in real time—no one needs to jump in.

Guardrail metrics on every feature

Define thresholds per flag. LaunchDarkly monitors in real time and rolls back automatically when thresholds are breached.

Root cause attribution in seconds

No manual triage. Just the answer.

Help prevent incidents before they impact users

Restore the last known good variation in seconds, identify what works best, and edit at runtime.

The fastest feedback loop runs in production, not staging

Expose variations to real users and track performance against your defined metrics. Understand how each variation performs under actual usage and automatically promote the winner.

Experiments attached to the flags you already release with

The same flag that controls your rollout becomes the experiment.

Test AI prompts and model variants

Compare models, measure real-world performance and cost, and route traffic to the best option—automatically, in production. All without redeploying.

Continuous optimization

Automatically promote winning variations, adjust exposure, and iterate.

AlayaCare Engineering Team

Healthcare software platform · Production at scale

Your release process wasn't built for the pace AI codes at

See how engineering teams use feature flags to control what ships, observe production, and iterate on AI features.

Get a Demo

Feature flags built for the AI era

Book your personalized demo

Trusted by engineering teams at

Ship features at the speed AI allows—with control

Ship with control

Progressively rollout

Contain and correct automatically

Release and observe in production

Guardrail metrics on every feature

Root cause attribution in seconds

Help prevent incidents before they impact users

The fastest feedback loop runs in production, not staging

Experiments attached to the flags you already release with

Test AI prompts and model variants

Continuous optimization

Your release process wasn't built for the pace AI codes at

Feature flags built for the AI era

Book your personalized demo

Trusted by engineering teams at

Ship features at the speed AI allows—with control

Ship with control

Progressively rollout

Contain and correct automatically

Release and observe in production

Guardrail metrics on every feature

Root cause attribution in seconds

Help prevent incidents before they impact users

The fastest feedback loop runs in production, not staging

Experiments attached to the flags you already release with

Test AI prompts and model variants

Continuous optimization

Your release process wasn't built for the pace AI codes at

Your release process wasn't built for the pace AI codes at

Your release process wasn't built for the pace AI codes at