Lunary

It tracks prompt inputs, model responses, latency, token usage, cost, and user feedback providing the operational visibility for AI applications that application performance monitoring tools provide for traditional software.

The platform's debugging tools allow teams to trace specific interactions end-to-end, understanding exactly what prompts were sent to which models, what the responses were, and where in the application logic any failures or degradations occurred.

This traceability is essential for diagnosing production issues in AI applications, where the stochastic nature of model outputs makes debugging fundamentally different from traditional software.

AI engineering teams building and operating LLM-powered products, ML engineers managing model deployments, and product teams monitoring AI feature quality use Lunary to maintain operational confidence in their AI applications.

As LLM applications move from prototype to production, the observability gap the lack of visibility into how the AI is actually behaving at scale becomes the primary barrier to confident operation. Lunary closes that gap.