Llm Monitoring

An agent trajectory monitor watches the full sequence of steps, tool calls, decisions, and token spend an AI agent makes in real time — not just the final answer. Because AI agents consume 5-30x more tokens per task than standard chatbots, real-time trajectory monitoring has moved from a nice-to-have to a budget requirement. It lets you see whether your agent is doing the right work or just getting the right answer by chance. ...

As AI applications move from prototype to production, the gap between what your LLM is doing and what you can actually observe grows dangerously wide. OpenObserve is an open-source, Apache 2.0-licensed observability platform built in Rust that unifies logs, metrics, and traces under a single roof — making it a compelling choice for teams who need full visibility into their AI stack without handing over their data or their budget. In this guide, you’ll get a complete walkthrough of OpenObserve’s LLM monitoring capabilities: from initial setup to cost dashboards, integrations, alerting, and a clear comparison against the major commercial alternatives. ...