LaunchDarkly announced multiple platform innovations to help engineering and product teams deliver with both high velocity and lower risk.
The latest capabilities at LaunchDarkly give teams the tools they need to innovate boldly—without exposing customers or businesses to unnecessary risk. By bringing observability, AI controls, and analytics directly into the release process, LaunchDarkly is enabling engineering and product teams to ship with confidence, respond to application issues, and continuously improve the user experience.
“Software used to evolve quarterly. Today, it changes by the hour. And with AI systems adapting in production, often unpredictably, release management at feature level granularity has become mission-critical,” said Dan Rogers, CEO of LaunchDarkly. “Teams need the ability to ship with precision, respond in real time, and continuously optimize what’s live. That’s what LaunchDarkly delivers: a safer, smarter way to build and release software in an AI-powered world.”
Platform Updates Introduced at Galaxy ’25:
Guarded Releases – Observability at the Point of Release: Guarded Releases pair progressive rollouts with real-time monitoring, automated rollback, and feature-level observability. Teams can now identify regressions instantly and correlate them directly to specific changes, preventing incidents before they impact users. With the recent integration of Highlight.io, LaunchDarkly extends observability to include telemetry data like metrics, logs and traces at the point of release.
AI Configs – Runtime Control Plane for Model and Prompt Management: AI Configs give teams a centralized control plane to manage prompt and model configurations for AI-powered applications. Teams can safely iterate in production, monitor key metrics like cost and latency, and deploy fallback strategies when things go wrong without any code changes. This reduces risk while accelerating the development of AI features.
Warehouse-Native Experimentation & Product Analytics: LaunchDarkly now gives teams real-time insights into user behavior and feature engagement, powered directly by their data warehouse. With warehouse-native experimentation and product analytics, teams can quickly understand what’s working, what’s not, and how every feature impacts business outcomes. The recent integration of Houseware strengthens these capabilities by making it easier to run experiments, analyze results, and iterate faster, all within the existing data ecosystem.
“Generative AI is fundamentally changing the relationship between the code we build, the code we deploy, and the code we maintain in production. Experimentation, understanding user behaviour, is now a necessity, not a luxury,” said James Governor, RedMonk co-founder. “LaunchDarkly is building observability into its core offerings, deepening its focus on analytics, and doubling down on release management to create an integrated platform for progressive delivery in the AI era.”
Guarded Releases, AI Configs, and Warehouse-Native Experimentation & Product Analytics are generally available today. Advanced observability features within Guarded Releases, including error monitoring, session replay, and telemetry integrations, are available in early access.
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