In this article, you will learn about seven leading LLM observability tools that help AI engineers monitor, evaluate, and debug large language model applications running in production. Topics we will cover include: What LLM observability is and why it matters for production AI systems. The core capabilities of each tool, including tracing, evaluation, cost tracking, and prompt management. How to choose the right tool based on your stack, team size, and immediate priorities. LLM Observability Tools for Reliable AI Applications Introduction Large language models (LLMs) now power everything from customer service bots to autonomous coding agents. Getting them to work in a demo is one thing, but keeping them working reliably at scale is another. Responses can degrade in quality over time, costs can spike without warning, and a bad prompt change can affect many users before anyone notices. LLM observability tools give you visibility into what your models are actually doing in production.…