LaunchDarkly + AWS

Your path to cloud confidence

Migrate, innovate, and accelerate.

Deliver extraordinary customer experiences.

De-risk, modernize, and move faster on AWS with LaunchDarkly. Unify feature flags, release observability, experimentation and AI configuration management to safely ship new software and AI apps.

Innovation in every industry

Software

Velocity with safety

Decouple deploy from release on AWS, allowing continuous integration and deployment while gating new features with flags. This reduces downtime risk and enables mid-deployment rollbacks without redeploys.

Cross-service coordination

Use LaunchDarkly targeting to coordinate releases across dozens of microservices running in AWS Lambda or ECS, ensuring dependent services update in lockstep rather than risking cascading failures.

Experimentation at scale

Embed experimentation into the delivery pipeline by attaching experiments to any feature flag. This enables rapid iteration on pricing flows, collaboration features, or UI changes, with statistically rigorous results trusted by both engineering and product teams.

Fraud detection & risk models

Deploy new fraud detection models on AWS using LaunchDarkly feature flags to gate releases by customer segment. Teams can validate accuracy and performance in production before broad rollout, reducing false positives and customer friction.

Regulatory compliance

Use Guarded Releases with automated rollback and CloudTrail Lake audit logs to ensure every update to trading platforms or payment systems can be traced, audited, and reversed instantly if thresholds are breached.

Customer engagement experiments

Financial institutions can run A/B tests on digital banking features (like mobile onboarding flows or savings recommendations) with full isolation between test and control groups, measuring engagement while preserving compliance boundaries.

Clinical decision support

Gradually roll out new diagnostic algorithms or patient monitoring dashboards to a subset of clinicians. If issues arise (e.g., model drift, latency spikes), roll back instantly to the prior version without interrupting care delivery.

HIPAA-ready governance

Enforce strict access controls for feature management with SSO/MFA, ensure PHI-sensitive features are only available in compliant regions, and provide audit trails to satisfy HIPAA and FedRAMP requirements.

Iterative healthcare tools

Test new note-taking or telehealth tools with small patient cohorts, collecting usability and reliability metrics before scaling system-wide, reducing the risk of introducing instability into critical care workflows.

Content moderation

LaunchDarkly flags allow teams to trial new moderation models (e.g., toxicity detection in chat) on AWS in real time, targeting by geography or user tier to comply with local policies and measure impact before full rollout.

Personalized recommendations

Streaming and gaming companies can test algorithm changes on a subset of users, monitoring engagement and churn before extending to their global user base. Real-time rollback prevents widespread quality drops.

A/B testing at scale

Media platforms can run parallel experiments on ad placement, subscription offers, or UI changes, measuring effects on revenue per user, session length, and retention—all without pausing deployments or requiring parallel code branches.

Personalized shopping journeys

Retailers can release new recommendation algorithms or targeted promotions to defined cohorts, tracking conversion lift in real time and rolling back if results degrade.

Demand forecasting

New inventory prediction models can be incrementally rolled out by region or store type, validated against live sales data, and optimized iteratively without exposing all customers to risk.

Optimized conversions

LaunchDarkly flags allow checkout flow updates or upsell features to be toggled in production. Teams can measure drop-off rates and revenue impact immediately, iterating rapidly without redeploys.

Read story
Poka goes “flag-first” to transform its release processes and AI innovation
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If prompts were only on the backend, only the backend people could modify them. But since they're a flag in LaunchDarkly, the product managers, front-end developers, or even the designers might have access to modifying them if they want to test something out.
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Read story
How Naviance unlocked Its monolith to migrate with confidence
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When our team first decided to do this, it immediately dawned on me how valuable this could be because what this does is it allows us to have very controlled data migrations and to apply the concepts of LaunchDarkly—especially the concept of testing in production—with feature flag controls to our data migration.
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Watch the talk
Nestlé Purina streamlines and de-risks releases through progressive delivery.
2x
faster release cycles
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Releases are safe and boring with LaunchDarkly. That’s exactly what we wanted, and that’s exactly what we got.
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Deploy faster

Migrate and modernize faster

LaunchDarkly lets you roll out back-end components in a gradual, controlled manner—enabling speed through safety.

Ebook: 3 ways to end the misery of tech migrations

Break migrations into manageable pieces

Use migration-specific feature flags to progressively introduce new cloud infrastructure, databases, APIs, and other services in small increments.

Go beyond infrastructure routing

Control migrations at the application layer to flexibly target using any parameter. Roll out specific back-end components to specific audiences.

Monitor migration metrics

Closely monitor performance, consistency, and business metrics along each step of a migration. Improve visibility and increase the likelihood of success.

Accelerate AI development

Safely control and accelerate the deployment of AI applications on AWS by using LaunchDarkly and Amazon Bedrock to rapidly iterate on new models and prompts, instantly roll back issues, and customize and optimize experiences across audiences.

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