Arize Phoenix Alternative? Langfuse vs. Phoenix LLM Observability

This article compares Arize Phoenix and Langfuse, two open source LLM observability platforms.

High Level Comparison

Arize Phoenix and Langfuse are both open-source tools for LLM observability, analytics, evaluation, testing, and annotation. However, they differ in focus, adoption, and suitability for different stages of LLM application development.

Langfuse focuses on being best in class for core LLM engineering features (tracing, evaluations, prompt management, APIs), prompt management, and usage analytics. Arize Phoenix focuses on the experimental and development stages of LLM apps and is particularly strong for RAG use cases.

• Self-Hosting: Langfuse is very easy to self-host and offers extensive self-hosting documentation for data security or compliance requirements.

• Integration with Arize: Arize Phoenix is a good solution if your company already uses Arize AI’s platform. Phoenix enables a smooth data transfer between the two tools. However, it lacks prompt management and LLM usage monitoring features, which may limit its effectiveness in production environments.

• Adoption and Production Readiness: Langfuse has a larger open source adoption compared to Arize Phoenix and is considered battle-tested for production use cases. This makes Langfuse a good choice for companies seeking a reliable tool for live production environments.

Download and Usage Statistics

Langfuse is the most popular open source LLM observability platform. You can find a comparison of GitHub stars and PyPI downloads vs. Arize Phoenix below. We are transparent about our usage statistics.

Community Engagement

OverviewGitHub Star History

	⭐️ GitHub stars	Last commit	GitHub Discussions	GitHub Issues
🪢 Langfuse
Phoenix / Arize

Numbers refresh automatically via shields.io

Downloads

	pypi downloads	npm downloads	docker pulls
🪢 Langfuse
Phoenix / Arize		N/A

Numbers refresh automatically via shields.io

Arize Phoenix

What is Arize Phoenix?

Arize Phoenix is an open-source observability tool designed for experimentation, evaluation, and troubleshooting of LLM apps. Built by Arize AI, Phoenix enables AI engineers and data scientists to visualize data, evaluate performance, track down issues, and export data for improvements.

What is Arize Phoenix used for?

• Integration with Arize AI: Share data when discovering insights to the Arize platform so the data science team can perform further investigation or kickoff retraining workflows.
• Development and Experimentation: Arize Phoenix is focused on the experimental and development stages of LLM applications, providing tools for model evaluation and troubleshooting.

Langfuse

Example trace in our public demo

What is Langfuse?

Langfuse is an LLM observability platform that provides a comprehensive tracing and logging solution for LLM applications. Langfuse helps teams to understand and debug complex LLM applications and evaluate and iterate on them in production.

What is Langfuse used for?

• Holistic Tracing and Debugging: Effective tracking of both LLM and non-LLM actions, delivering complete context for applications.
• Production Environments: Best in class for core LLM engineering features, emphasizing production-grade monitoring, debugging, and performance analytics.
• Prompt Management: Provides robust prompt management solutions through client SDKs, ensuring minimal impact on application latency and uptime during prompt retrieval.
• Integration Options: Supports asynchronous logging and tracing SDKs with integrations for frameworks like LangChain, LlamaIndex, OpenAI SDK, and others.
• Deep Evaluation: Facilitates user feedback collection, manual reviews, annotation queues, LLM-as-a-Judge automated annotations, and custom evaluation functions.
• Self-Hosting: Extensive self-hosting documentation for data security or compliance requirements.

Core Feature Comparison

This table compares the core features of LLM observability tools: Logging model calls, managing and testing prompts in production, and evaluating model outputs.

Conclusion

Langfuse is a great choice for most production use cases, particularly when comprehensive tracing, prompt management, deep evaluation capabilities, and robust usage monitoring are critical. Its ability to provide detailed insights into both LLM and non-LLM activities, along with support for asynchronous logging and various framework integrations, makes it ideal for complex applications requiring thorough observability.

Arize Phoenix is a strong option if your company already uses Arize AI’s enterprise platform and is focused on the experimental and development stages of LLM applications. It offers tools for evaluation and troubleshooting . However, its lack of prompt management and comprehensive LLM usage monitoring features may limit its effectiveness in production environments, making it less suitable for teams requiring these capabilities.

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