When evaluating observability platforms for large language models (LLMs), architectural approaches significantly influence long-term success, as seen in the comparison between Braintrust and Helicone. Braintrust is positioned as a comprehensive AI observability platform that integrates evaluation with the development process, enabling teams to understand and improve AI behavior in production seamlessly. It offers SDK-based tracing for complete application visibility, optional proxy features for model access, and built-in evaluation infrastructure, promoting cross-functional collaboration and systematic improvement. Helicone, on the other hand, is an open-source platform with a proxy-based architecture that provides basic visibility into LLM calls but couples observability with request routing, potentially adding latency and dependency risks. While Helicone's simplicity suits early experimentation, Braintrust's robust features support production AI products by closing the feedback loop and enhancing collaboration, making it a suitable choice for teams focused on quality improvements without the burden of building extensive evaluation infrastructure.