AI-through-API has become a standard component in software applications, with companies like OpenAI, Anthropic, and Notion using platforms like Statsig to enhance product development. The deployment of AI features requires robust release management systems to ensure efficient and safe code releases, especially given the complex interaction effects and non-deterministic results often associated with AI products. Feature management allows engineers to test features in production safely, while experimentation is crucial for optimizing AI models, prompts, and other application components. Online experimentation offers a more dynamic approach than traditional offline testing, allowing companies to test different parameters and configurations to improve performance, latency, and cost. The use of Statsig’s Layers facilitates simultaneous experiments without cross-contamination of results, enhancing the ability to gather insights from user interactions. AI companies are now tracking a range of performance, latency, and cost metrics to monitor progress and drive product improvements, with experimentation and analytics playing a pivotal role in minimizing risks and maximizing the success of AI initiatives.