Pricing and capability claims verified through .
LangSmith vs Arize
Choose LangSmith when you build on LangChain/LangGraph and need tracing plus evals. Choose Arize for broader production AI observability beyond that ecosystem.
Moneda may earn a commission from some product links. Partner economics do not affect our recommendations. This page is research-based — conclusions come from product evidence and documentation, not a claim that we personally purchased every product listed.
Winner for most teams
Choose LangSmith when your stack is LangChain/LangGraph and you need tracing and evaluation workflows. Choose Arize when you need broader production AI observability across models and LLMs.
Try LangSmithBest alternative
Arize
Teams running production AI systems that need model and LLM observability
Try ArizeHow we evaluated this
Evaluation note: We assessed Trace debugging, Eval workflows, Cross-stack monitoring needs against vendor docs, product pages, and workflow fit — not synthetic benchmark scores. 5 primary sources checked. led for most teams in our editorial call.
Which of these are you?
Your pick
LangSmith
Native tracing and eval workflows for that ecosystem.
Tradeoff
Arize can be better when the task is mostly platform teams standardizing multi-stack observability.
Highlighted = better for LangChain/LangGraph teams
Spec at a glance
Free tier · Plus and enterprise plans
Custom pricing
✅ Included · ❌ Missing · ⚠️ Partial or add-on. Shaded cell = stronger option for your segment.
Don't pick LangSmith if
Your work is mostly platform teams standardizing multi-stack observability — Arize wins that workflow.
Don't pick Arize if
Your work is mostly langchain/langgraph teams — LangSmith wins that workflow.
What changed, and why
Each entry is generated from an approved recompute of this decision — not written after the fact.
integrity-whylabs-langsmith-vs-arize
What we evaluated
Evaluation criteria: Trace debugging, Eval workflows, Cross-stack monitoring needs. Conclusions are based on primary documentation, product evidence, and workflow fit — not hands-on benchmark runs unless noted. 5 primary sources checked.