AI tool comparison

Langfuse vs Arize Phoenix

A data-driven comparison of Langfuse (LLM Observability) and Arize Phoenix (AI Observability) — features, GitHub momentum, and which tool fits your workflow.

Langfuse

LLM Observability

Private

Arize Phoenix

AI Observability

Private

Feature comparison

Feature
Langfuse
Arize Phoenix
Focus
LLM-specific observability
Full ML + LLM observability
Tracing
LLM trace trees
Spans + embeddings
Evaluations
Built-in framework
Built-in + experiment tracking
Self-hosted
Yes
Yes (open source)
Embedding analysis
No
Yes (drift, retrieval)
Pricing
Free + usage-based
Free (open source)

Track your AI tool usage with Rize

Both platforms help you understand your AI systems. Rize helps you understand how much time your team spends operating them.

Frequently asked questions

What is the difference between Langfuse and Arize Phoenix?

Langfuse is a LLM Observability while Arize Phoenix is a AI Observability. Key differences include: Focus (Langfuse: LLM-specific observability, Arize Phoenix: Full ML + LLM observability); Tracing (Langfuse: LLM trace trees, Arize Phoenix: Spans + embeddings); Evaluations (Langfuse: Built-in framework, Arize Phoenix: Built-in + experiment tracking).

Which is more popular, Langfuse or Arize Phoenix?

Both tools are actively developed open-source projects. Check the live GitHub stats above for the latest popularity data.

Can Rize track time spent in Langfuse and Arize Phoenix?

Yes. Rize automatically detects and tracks time spent in both Langfuse and Arize Phoenix. It runs in the background and categorizes your AI tool usage by project and client — no manual timers needed.

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