"Klarna deployed a multi-agent system using LangSmith for step-by-step trace visibility and test-driven development. The result: 80% faster resolution times and a 70% automation rate across customer service workflows."
Why teams choose LangSmith as their Langfuse alternative
Langfuse tracing and Langfuse docs are solid starting points. But teams evaluating Langfuse review their options when agents reach production scale—and three gaps consistently emerge.
Langfuse evals lack deterministic online checks
Langfuse evaluation docs confirm online deterministic checks are on the roadmap but not in GA. LangSmith runs both LLM-as-a-judge and rule-based evals on live production traffic today. If Langfuse LLM-as-a-judge coverage gaps are slowing your team, LangSmith has the full evaluation toolkit ready now.
Langfuse tracing has no queue routing logic
Langfuse's Annotation Queues lack routing rules or priority-based distribution. As review volume scales, manual queue management becomes a bottleneck—a gap that teams switching from Langfuse vs Braintrust or Langfuse vs Arize consistently cite. LangSmith routes high-risk outputs first and load-balances work automatically.
Langfuse self hosted lacks managed agent runtime
Whether you need Langfuse self hosted or Langfuse enterprise pricing on cloud, neither option includes infrastructure for running stateful AI agents in production. LangSmith Deployment provides managed runtime with task queues, human-in-the-loop pauses, and horizontal scaling—what Langfuse vs LangGraph comparisons miss.
"Podium's team used LangSmith's evaluation workflows to reach a 98.6% F1 score on their AI agent. They reduced engineering intervention by 90% through systematic evals and dataset curation powered by production traces."
Podium Engineering Team
Podium
