Langfuse vs Arize Phoenix vs LangSmith: Which LLM Observability Tool Isn’t Useless?

Description

Langfuse vs Arize Phoenix vs LangSmith: Which LLM Observability Tool Isn’t Useless? 🤖📊 In this video, we compare Langfuse, Arize Phoenix, and LangSmith — three major players in the world of LLM observability and evaluation tools. Whether you're building with OpenAI, Anthropic, or custom fine-tunes, knowing which platform actually helps you track, debug, and improve your AI workflows is crucial. We’ll break down how each handles traceability, model metrics, prompt testing, latency, and error analysis — showing how these tools fit real-world AI development, from startups to enterprise labs. Pros & Cons: Find out which one nails transparency and trace detail (Langfuse), which excels at visual analytics and root-cause detection (Arize Phoenix), and which offers tight integration with LangChain pipelines (LangSmith) — plus where each may struggle in usability, pricing, or deployment setup. By the end, you’ll know which observability tool actually saves your AI projects — and which one’s just marketing fluff. 👇 Don’t forget to like, comment, and subscribe! Tell us which LLM tool you’re using to monitor your AI stack! #Langfuse #ArizePhoenix #LangSmith #LLMObservability #AItools #AIevaluation Business Inquries Only: theguideinquiries@gmail.com Disclaimer: All Content Is Used For Educational Purposes Only, This video is for informational and entertainment purposes only and does not constitute financial advice. I am not a financial advisor. The content is based on personal opinion and experience. Always do your own research before using any financial platform, product, or service. Your decisions are your responsibility, and I am not liable for any losses incurred.