The Traceloop Blog

This article explains how teams can move from reacting to unpredictable LLM bills to proactively controlling costs by tracking token usage at a granular, per-user level. The key is attaching metadata—such as user_id or feature_name—to every LLM request so costs can be attributed to specific users, features, or teams. Since manually tagging across multiple services is unscalable, organizations increasingly use LLM proxies or OpenTelemetry-based observability frameworks to centralize and automate this data collection. With full traces tying user actions to token spend, teams can visualize which users or features drive costs, investigate anomalies, set alerts, and enforce budgets. Platforms like Traceloop provide this out-of-the-box, turning opaque LLM spend into a transparent and controllable part of the engineering and FinOps workflow.

This article explains why debugging LLMs in production is so challenging due to their non-deterministic behavior and complex pipelines, and outlines how modern teams overcome this with deep observability and reproducible debugging. It emphasizes the need for end-to-end tracing—capturing every prompt, retrieval step, API call, and intermediate output under a unique request ID—to understand where failures originate, especially in architectures like RAG. With full trace context, specialized LLM observability platforms can then “replay” production failures as repeatable test cases, allowing engineers to reliably reproduce issues, iterate on fixes, and validate improvements. Ultimately, robust tracing plus one-click reproduction transforms unpredictable LLM anomalies into systematic, solvable problems.

Understanding how your large language model performs requires more than basic monitoring. This article explains how OpenTelemetry and Traceloop’s OpenLLMetry provide complete visibility into LLM operations by capturing token usage, latency, and cost within unified traces. It outlines how developers can instrument their applications, visualize metrics in real time, and use Traceloop’s OpenTelemetry-native platform to simplify observability and performance optimization.

Debugging RAG pipelines without proper visibility can feel like guesswork. This post explains how end-to-end tracing turns that process into data-driven debugging, revealing exactly what happens at every stage of a RAG workflow, from query to retrieval to generation. It highlights why LLM observability platforms matter and how Traceloop delivers complete, out-of-the-box trace visibility that helps teams pinpoint errors, reduce latency, and optimize RAG performance.