---
title: "Open Source LangSmith Alternative: Arize Phoenix vs. LangSmith"
---

## What is the difference between Arize Phoenix and LangSmith?

LangSmith is another LLM Observability and Evaluation platform that serves as an alternative to Arize Phoenix. Both platforms support the baseline tracing, evaluation, prompt management, and experimentation features, but there are a few key differences to be aware of:\\

1. LangSmith is **closed source**, while Phoenix is open source
2. LangSmith is part of the broader LangChain ecosystem, though it does support applications that don’t use LangChain. **Phoenix is fully framework-agnostic**.
3. **Self-hosting is a paid feature within LangSmith**, vs free for Phoenix.
4. **Phoenix is backed by Arize AI**. Phoenix users always have the option to graduate into Arize AX, with additional features, a customer success org, infosec team, and dedicated support. Meanwhile, Phoenix is able to focus entirely on providing the best fully open-source solution in the ecosystem.

## Open vs. Closed Source

The first and most fundamental difference: **LangSmith is closed source**, while **Phoenix is fully open source**.

This means Phoenix users have complete control over how the platform is used, modified, and integrated. Whether you're running in a corporate environment with custom compliance requirements or you're building novel agent workflows, open-source tooling allows for a degree of flexibility and transparency that closed platforms simply can’t match.

LangSmith users, on the other hand, are dependent on a vendor roadmap and pricing model, with limited ability to inspect or modify the underlying system.

## Ecosystem Lock-In vs. Ecosystem-Agnostic


**LangSmith is tightly integrated with the LangChain ecosystem**, and while it technically supports non-LangChain applications, the experience is optimized for LangChain-native workflows.

**Phoenix is designed from the ground up to be framework-agnostic**. It supports popular orchestration tools like LangChain, LlamaIndex, CrewAI, SmolAgents, and custom agents, thanks to its OpenInference instrumentation layer. This makes Phoenix a better choice for teams exploring multiple agent/orchestration frameworks—or who simply want to avoid vendor lock-in.

## Self-Hosting: Free vs. Paid


If self-hosting is a requirement—for reasons ranging from data privacy to performance—**Phoenix offers it out-of-the-box, for free**. You can launch the entire platform with a single Docker container, no license keys or paywalls required.

**LangSmith, by contrast, requires a paid plan to access self-hosting options**. This can be a barrier for teams evaluating tools or early in their journey, especially those that want to maintain control over their data from day one.

## Backed by Arize AI


Phoenix is backed by [Arize AI](https://arize.com), the leading and best-funded AI Observability provider in the ecosystem.

Arize Phoenix is intended to be a complete LLM observability solution, however for users who do not want to self-host, or who need additional features like Custom Dashboards, Copilot, Dedicated Support, or HIPAA compliance, there is a seamless **upgrade path to Arize AX**.

The success of Arize means that Phoenix does not need to be heavily commercialized. It can focus entirely on providing the best open-source solution for LLM Observability & Evaluation.

## Feature Comparison


| Feature                 | Arize Phoenix | Arize AX    | LangSmith    |
| :----------------------- | :------------- | :----------- | :------------ |
| Open Source             | ✅             |  |              |
| Tracing                 | ✅             | ✅           | ✅            |
| Auto-Instrumentation    | ✅             | ✅           |   |
| Offline Evals           | ✅             | ✅           | ✅            |
| Online Evals            |    | ✅           | ✅            |
| Experimentation         | ✅             | ✅           | ✅            |
| Prompt Management       | ✅             | ✅           | ✅            |
| Prompt Playground       | ✅             | ✅           | ✅            |
| Run Prompts on Datasets | ✅             | ✅           | ✅|
| Built-in Evaluators     | ✅             | ✅           | ✅            |
| Agent Evaluations       | ✅             | ✅           | ✅            |
| Human Annotations       | ✅             | ✅           | ✅            |
| Custom Dashboards       |    | ✅           |   |
| Workspaces              |    | ✅           |   |
| Semantic Querying       |    | ✅           |   |
| Copilot Assistant       |    | ✅           |   |

## Final Thoughts

LangSmith is a strong option for teams all-in on the LangChain ecosystem and comfortable with a closed-source platform. But for those who value openness, framework flexibility, and low-friction adoption, Arize Phoenix stands out as the more accessible and extensible observability solution.
