Comparison
Arize Phoenix vs LangSmith
Arize Phoenix and LangSmith are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Arize Phoenix
When you want open-source LLMOps tooling that works in notebooks, the IDE, and production with the same instrumentation.
A team instruments their RAG pipeline with the Phoenix tracer, then runs the built-in faithfulness eval on yesterday's traffic to find sessions where the model contradicted the docs.
When you would reach for LangSmith
LangSmith comes up when the question is fundamentally about infrastructure.
A LangChain app with one line of setup: every chain run shows up in the LangSmith trace UI with input, output, intermediate steps, and per-step costs.
Frequently asked
What is the difference between Arize Phoenix and LangSmith?
Arize Phoenix: Arize Phoenix is an open-source LLM observability and evaluation tool. It ingests OpenTelemetry traces, renders them in a debug UI, and provides built-in LLM-as-judge evaluators for hallucination, relevance, and toxicity. LangSmith: LangSmith is LangChain's commercial LLM observability and evaluation platform. It captures traces (LangChain-native and OTel), runs evaluations, manages prompt versions, and supports dataset curation.
When should I use Arize Phoenix vs LangSmith?
When you want open-source LLMOps tooling that works in notebooks, the IDE, and production with the same instrumentation. LangSmith applies when you are focused on infrastructure.
Are Arize Phoenix and LangSmith the same thing?
No. Arize Phoenix is infrastructure; LangSmith is infrastructure. They are related but address different parts of the AI stack.