LLM call observability: Tracing every request, response, and token in production

LLM call observability is a critical process in monitoring the detailed interactions between applications and language models, allowing for comprehensive tracking of requests, responses, and associated metadata for each API call. Unlike traditional APM tools that only capture HTTP-level signals, LLM call observability focuses on in-depth data such as the full request and response payloads, performance metrics, and cost analysis, which are pivotal for debugging and ensuring quality outputs. This observability is essential for various production LLM workloads, including chatbots and summarization, as it provides visibility into what the model received, returned, and the performance of each call. Tools like Braintrust offer robust solutions by integrating LLM call observability with evaluation and release decision workflows, supporting teams in debugging, detecting drift, and managing regression evaluations effectively. Additionally, Braintrust's platform connects call observability directly to CI quality gates and production-to-test-case workflows, facilitating continuous improvement and quality assurance in AI systems.