Comparing LLM Observability Tools: LangSmith, LangFuse, Lunary, and Helicone
Observability for LLM applications is critical. Whether you’re troubleshooting unexpected model outputs, tracking token usage and costs, or fine-tuning your prompt strategies, having the right observability tool can make all the difference. In this post, we compare four popular platforms— LangSmith , LangFuse , Lunary , and Helicone —to help you determine which fits your needs best. Why LLM Observability Matters LLM observability goes beyond classic infrastructure monitoring. With LLM apps, you need: Detailed tracing of prompt-to-response flows Evaluation metrics to monitor model performance and output quality Cost tracking for usage-heavy deployments Robust integration with your existing workflows (e.g., LangChain or other frameworks) As models become more complex and integrated into mission-critical applications, understanding these dimensions is essential for debugging, compliance, and performance optimization. Tool Ov...