Build Versus Buy Tradeoffs for AI Observability

This technical report discusses the tradeoffs between building and buying observability solutions for machine learning (ML) and large language models (LLMs) in the context of generative AI. It outlines the considerations that teams face when deciding on their observability infrastructure, emphasizing the importance of return on investment (ROI) and the impact on model accuracy and business metrics. The report details essential elements for a modern AI observability platform, including capabilities needed for LLM observability, performance monitoring, and troubleshooting. It also highlights the common trend among enterprises to opt for buying solutions due to the opportunity costs associated with building in-house. The document presents various perspectives and approaches to aid organizations in making informed decisions regarding their observability needs, ultimately aiming to enhance the efficiency and effectiveness of AI deployments.