---
title: "Langfuse alternative? Arize Phoenix vs Langfuse: Key differences"
---


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

Langfuse has an initially similar feature set to Arize Phoenix. Both tools support tracing, evaluation, experimentation, and prompt management, both in development and production. But on closer inspection there are a few notable differences:\\

1. While it is open-source, **Langfuse locks certain key features** like Prompt Playground and LLM-as-a-Judge evals behind a paywall. These same features are free in Phoenix.
2. **Phoenix is significantly easier to self-host than Langfuse**. Langfuse requires you to separately setup and link Clickhouse, Redis, and S3. Phoenix can be hosted out-of-the-box as a single docker container.
3. **Langfuse relies on outside instrumentation libraries to generate traces**. Arize maintains its own layer that operates in concert with OpenTelemetry for instrumentation.
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.

## Feature Access

Langfuse is open-source, but several critical features are gated behind its paid offering when self-hosting. For example:

* Prompt Playground
* LLM-as-a-Judge evaluations
* Prompt experiments
* Annotation queues

These features can be crucial for building and refining LLM systems, especially in early prototyping stages. In contrast, **Arize Phoenix offers these capabilities fully open-source**.

## Ease of Self-Hosting

Self-hosting Langfuse requires setting up and maintaining:

* A **ClickHouse** database for analytics
* **Redis** for caching and background jobs
* **S3**-compatible storage for logs and artifacts

Arize Phoenix, on the other hand, can be launched with a single Docker container. No need to stitch together external services—Phoenix is designed to be drop-in simple for both experimentation and production monitoring. This “batteries-included” philosophy makes it faster to adopt and easier to maintain.

## Instrumentation Approach

Langfuse does not provide its own instrumentation layer—instead, it relies on developers to integrate third-party libraries to generate and send trace data.

Phoenix takes a different approach: it includes and maintains its own OpenTelemetry-compatible instrumentation layer, [**OpenInference**](https://github.com/Arize-ai/openinference).

In fact, Langfuse supports OpenInference tracing as one of its options. This means that using Langfuse requires at least one additional dependency on an instrumentation provider.

## 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    | Langfuse    |
| :----------------------- | :------------- | :----------- | :----------- |
| 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

If you're choosing between Langfuse and Arize Phoenix, the right tool will depend on your needs. Langfuse has a polished UI and solid community momentum, but imposes friction around hosting and feature access. Arize Phoenix offers a more open, developer-friendly experience—especially for those who want a single-container solution with built-in instrumentation and evaluation tools.

