As the adoption of Large Language Models (LLMs) expands, the need for effective observability tools becomes crucial, with Helicone and Arize Phoenix emerging as two prominent platforms in this space. Helicone is designed for developers and analysts, offering features such as self-hosting, user tracking, and flexible pricing, making it accessible to smaller teams and individual developers. It emphasizes ease of integration and comprehensive feature sets that include caching and cost analysis. In contrast, Arize Phoenix targets data scientists and ML engineers with its robust evaluation capabilities and open-source nature, although it lacks some developer-friendly features and self-hosting options. While Helicone is favored for its versatility and user-friendly approach, especially for those seeking an all-in-one solution, Arize Phoenix excels in evaluation and monitoring for complex AI projects. Ultimately, the choice between the two depends on specific needs, team size, and the complexity of LLM applications, with Helicone generally being more accessible for a broader range of users.