Arize AIEnterprise
An ML and LLM observability platform for monitoring model and agent performance, detecting drift, and tracing failures in production.
Analytics comparison
Arize AI vs LangSmith
Pricing, pros, cons, and ideal use cases — side by side.
LangSmithFreemium
An observability and evaluation platform for LLM and agent applications — tracing, datasets, automated evals, and production monitoring in one place.
At a glance
| Arize AI | LangSmith | |
|---|---|---|
| Pricing | EnterpriseOpen-source Phoenix is free. The Arize platform is sold via paid and enterprise plans. | FreemiumFree developer tier. Paid Plus and Enterprise plans add seats, longer trace retention, and deployment options. |
| Category | Analytics | Analytics |
| Ideal for | Enterprises running AI in productionML and platform teamsRegulated industries needing auditable monitoring | Teams running agents in productionEnterprises needing eval and monitoringEngineering orgs standardizing AI observability |
Pros & cons
Arize AI
Pros
- Purpose-built for AI observability at scale
- Drift and performance-degradation detection
- Open-source Phoenix as a lighter on-ramp
- Strong fit for regulated environments
Cons
- Enterprise platform pricing is a commitment
- Most valuable once you have real production volume
- Setup is an engineering project
LangSmith
Pros
- End-to-end tracing for complex agent runs
- Datasets and automated evals in CI
- Production monitoring for quality and cost
- Framework-agnostic
Cons
- Most seamless within the LangChain ecosystem
- Trace volume can drive cost at scale
- Eval design is still real work
Which should you choose?
LangSmith is the lighter-weight option (Freemium), while Arize AI sits higher on the pricing ladder (Enterprise). Arize AI is built around enterprises running ai in production; LangSmith leans more toward teams running agents in production. Shortlist the one whose strengths line up with your biggest constraint.