June 2025 Summaries

Imagine a scenario where a critical API route change triggers cascading failures across a platform, causing customer complaints and systems downtime. However, in this same scenario, with LaunchDarkly's runtime controls, the issue can be resolved instantly, restoring service within 45 seconds. This is not fantasy, but a reality that organizations like LaunchDarkly have experienced. The current approach of deploying and releasing software as inseparable processes has hidden costs, such as code freezes during critical periods, deployment anxiety, domain expert dependency, all-or-nothing releases, and missed opportunities. A fundamental shift in how modern software is delivered can be achieved by moving beyond configuration-as-code to runtime control. This involves implementing features like kill switches, progressive delivery, and empowering domain experts with self-service provisioning templates. By making a small commitment to learning about LaunchDarkly's solution for one recurring incident, individuals can spark a larger transformation, shifting from reactive to proactive software delivery, where code freezes become unnecessary, deployments happen daily without fear, domain experts control their own destiny, engineers focus on building rather than firefighting, and incidents are resolved in seconds.

### Software development is not just about shipping code, but also about solving user problems. A product feedback loop is a systematic process for collecting, analyzing, implementing, and following up on user feedback to continuously improve the software. It's different from traditional "ship and forget" approaches, as it creates an ongoing conversation between the code and its users. The best product feedback loops blend quantitative data with qualitative insights, helping teams understand what's happening and why, to enable more targeted solutions. Implementing a product feedback loop can reduce development cycles and iteration time, catch issues before they become widespread, validate if features actually solve user problems, build better software with data, measure impact through quantitative metrics, and optimize the process for reliability, efficiency, and actionable insights.

LaunchDarkly is showcasing its integration with Snowflake Summit 2025, allowing teams to build, test, and ship features in one unified workflow. The platform provides experimentation, product analytics, and feature management capabilities that empower engineering, product, and data teams to unlock real-time insights, reduce risk, and make smarter product decisions. LaunchDarkly's Experimentation enables engineers to ship experiments as part of the development process, while providing product teams with immediate, trustworthy insights. With native integration into Snowflake, users can run warehouse-native experiments and analyze results directly alongside their core business data, export raw experiment data for deeper analysis, and ship full-stack experiments across web, mobile, backend, and AI models. LaunchDarkly's Product Analytics brings clarity and speed to how product teams measure impact by providing real-time visibility into feature adoption, usage patterns, and business outcomes. The platform also enables teams to manage and test AI features efficiently with AI Configs, which treats AI configurations like code by applying versions, running tests, and updating model details at runtime. LaunchDarkly is helping teams build better software with every release, with its integration with Snowflake Summit 2025 providing a shared workflow for continuous improvement of AI-driven features.

With GenAI, teams manage AI prompts and models without relying on code pushes or redeploys. This is where AI Configs comes in, a solution that provides control, speed, and confidence for managing AI workflows. AI Configs allows teams to experiment with different model variations in production without redeploying code, run A/B tests, track key metrics, and govern access to model configurations. It's designed for building safer and more scalable AI applications, making it suitable for chatbots, recommendation systems, summarization engines, and other intelligent user experiences. With AI Configs, teams can iterate without redeploys, target specific contexts, monitor with confidence, experiment like pros, and govern AI Configs with guardrails. The solution is built to help manage AI like product infrastructure, reducing waste by providing fine-grained control over model deployment, access, and cost.