Arize Phoenix vs Langfuse: both are coding tools on Scriptiii AI Tools. Arize Phoenix pricing: Free. Free to use. (reported by the maker) Langfuse pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
Do Arize Phoenix and Langfuse list the same price?
Both listings report Free. Free to use. Pricing is reported by the maker and is not independently verified.
Which has more upvotes, Arize Phoenix or Langfuse?
Both have 0 upvotes.
What does each maker say Arize Phoenix and Langfuse are for?
Arize Phoenix: AI observability and evaluation tool for tracing and evaluating LLM applications. Langfuse: Open-source LLM engineering platform for tracing, evaluation and prompt management.
Arize Phoenix is Arize's open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. For managed production workflows, Arize also offers Arize AX. Phoenix provides:
Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation.
PXI (Phoenix Intelligence) - An AI engineering agent built into Phoenix for debugging traces, iterating on prompts, and navigating the product.
Remote MCP Server - Connect Claude Code, Cursor, and other MCP clients directly to your Phoenix instance's /mcp endpoint to query traces, datasets, experiments, and more.
Phoenix runs practically anywhere, including your local machine, a containerized deployment, or in the cloud. See Self-Hosting for a walkthrough of each option, or jump straight into the Tracing Quickstart.
[!NOTE]
Phoenix is open-source and self-hosted. For a managed platform, Arize offers Arize AX — the same OpenTelemetry/OpenInference instrumentation works with both. See the full comparison.
The fastest way to send traces is to let your coding agent (Claude Code, Codex, Cursor, and others) instrument your app. From your project directory, run:
npx @arizeai/phoenix-cli setup
## or, with Phoenix installed: px setup
Setup detects your framework and LLM provider, installs the right OpenInference instrumentation, and wires up trace export. Prefer to wire it up in code? See the tracing documentation.
Deploy
Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes via the Helm chart.
[!NOTE]
The Google Cloud button builds Phoenix from source in Cloud Shell rather than deploying the prebuilt Docker Hub image. The Azure template serves plain HTTP (Azure Container Instances does not terminate TLS) — front it with a TLS proxy such as an Application Gateway before production use.
Packages
The arize-phoenix package includes the entire Phoenix platform. However, if you have deployed the Phoenix platform, there are lightweight Python sub-packages and TypeScript packages that can be used in conjunction with the platform.
CLI for fetching traces, datasets, and experiments for use with Claude Code, Cursor, and other coding agents
Tracing Integrations
Phoenix is built on top of OpenTelemetry and is vendor, language, and framework agnostic. For details about tracing integrations and example applications, see the OpenInference project and the integrations documentation.
People and coding agents such as Claude Code, Codex, and Cursor can both work with Phoenix. To give an agent access to your traces, datasets, and experiments, connect it in one of these ways:
Claude Code or Codex. Install the Arize Phoenix plugin. It registers the Phoenix MCP server and installs the Phoenix skills.
Any other agent. Add the Remote MCP Server at your instance's /mcp endpoint. It serves your Phoenix data and the Phoenix skills, which match your server version.
Optional CLI. The Phoenix CLI, npx @arizeai/phoenix-cli, runs Phoenix commands in a terminal, and px setup instruments your app through your coding agent.
By default, Phoenix collects basic web analytics (e.g., page views, UI interactions) to help us understand how Phoenix is used and improve the product. None of your trace data, evaluation results, or any sensitive information is ever collected.
You can opt-out of telemetry by setting the environment variable: PHOENIX_TELEMETRY_ENABLED=false
Community
Join our community to connect with thousands of AI builders.
Interested in contributing to Langfuse full-time? Langfuse is growing fast (we doubled the team in the last 6 months). Since January 2026 we're part of ClickHouse. We hire remotely, with offices in Berlin, San Francisco, Amsterdam, and New York. See open roles →
✨ Core Features
AI Observability: Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions. Built on OpenTelemetry. Try the interactive demo to see this in action.
Prompt Management helps you centrally manage, version control, and collaboratively iterate on your prompts. Thanks to strong caching on server and client side, you can iterate on prompts without adding latency to your application. The LLM Playground lets you test prompts and model configurations; when you see a bad result in tracing, you can jump straight to the playground to iterate on it.
Skill Management (beta) brings versioning and labels to agent skills: a SKILL.md with task instructions plus optional supporting files. Promote or roll back versions without changing application code, and link skill versions to the traces they produced.
Evaluations are key to the AI agent development workflow, and Langfuse adapts to your needs. It supports LLM-as-a-judge, code evaluators, user feedback collection, manual labeling, and custom evaluation pipelines via APIs/SDKs. Use Datasets and Experiments to build test sets and benchmarks for pre-deployment testing and continuous improvement.
AI Gateway (beta) gives your organization one endpoint for calls to your model providers. Keep provider credentials in one place, hand out per-user or service account gateway keys, approve models centrally, track AI spend per user, key, and model, and record every call as a Langfuse trace.
: Langfuse is frequently used to power bespoke AI engineering workflows while using the building blocks provided by Langfuse via the API. An OpenAPI spec, typed SDKs for Python and JS/TS, the , and the for coding agents are available.
📦 Deploy Langfuse
Langfuse Cloud
Managed deployment by the Langfuse team, generous free tier, no credit card required.
Docker Compose: Run Langfuse on your own machine or a single virtual machine in 5 minutes. The guide also covers configuring Docker log rotation so container logs don't fill up the disk.
# Get a copy of the latest Langfuse repository
git clone --depth=1 https://github.com/langfuse/langfuse.git
cd langfuse
# Run the langfuse docker compose
docker compose up
Kubernetes (Helm): Run Langfuse on a Kubernetes cluster using Helm. This is the preferred production deployment.
[!TIP] Self-hosting Langfuse? Subscribe to the self-hosting update list to get an email about important features and new releases for open source Langfuse — self-hosting updates only, no marketing.
🔌 Integrations
Langfuse is built on OpenTelemetry, so any OpenTelemetry-instrumented application, framework, or collector can send traces to Langfuse.
Instrument your app and start ingesting traces to Langfuse, thereby tracking LLM calls and other relevant logic in your app such as retrieval, embedding, or agent actions. Inspect and debug complex logs and user sessions.
🤖 Agentic installation (recommended)
Install the Langfuse Agent Skill to let your coding agent access all Langfuse features, then ask it to add tracing to your application:
Install the Langfuse Agent Skill from github.com/langfuse/skills
and use it to add tracing to this application with Langfuse
following best practices.
You can also install the skill with the skills CLI (npx skills add langfuse/skills --skill "langfuse") or the Langfuse Cursor plugin. If your agent supports MCP, add the Langfuse Docs MCP server (https://langfuse.com/api/mcp, no authentication) so it can look up current documentation while it works. See the get started guide for details.
Create new API credentials in the project settings
2️⃣ Log your first LLM call
The @observe() decorator makes it easy to trace any Python LLM application. In this quickstart we also use the Langfuse OpenAI integration to automatically capture all model parameters.
[!TIP]
Not using OpenAI? Visit our documentation to learn how to log other models and frameworks.
pip install langfuse openai
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASE_URL="https://cloud.langfuse.com" # 🇪🇺 EU region
## LANGFUSE_BASE_URL="https://us.cloud.langfuse.com" # 🇺🇸 US region
## LANGFUSE_BASE_URL="https://jp.cloud.langfuse.com" # 🇯🇵 Japan region
## LANGFUSE_BASE_URL="https://hipaa.cloud.langfuse.com" # ⚕️ HIPAA region
from langfuse import observe
from langfuse.openai import openai # OpenAI integration
@observe()
def story():
return openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "What is Langfuse?"}],
).choices[0].message.content
@observe()
def main():
return story()
main()
3️⃣ See traces in Langfuse
See your language model calls and other application logic in Langfuse.
Our documentation is the best place to start looking for answers. It is comprehensive, and we invest significant time into maintaining it. You can also suggest edits to the docs via GitHub.
Langfuse FAQs where the most common questions are answered.
Use "Ask AI" to get instant answers to your questions.
Support Channels:
Ask any question in our public Q&A on GitHub Discussions. Please include as much detail as possible (e.g. code snippets, screenshots, background information) to help us understand your question.
Open a PR - see CONTRIBUTING.md for details on how to setup a development environment.
🥇 License
This repository is MIT licensed, except for the ee folders. See LICENSE and docs for more details.
Dependencies
We deploy this code base in Docker containers. See web/Dockerfile and worker/Dockerfile for how the images are built, including the base images they use.
⭐️ Star History
❤️ Open Source Projects Using Langfuse
Top open-source projects that use the Langfuse Python or JS/TS SDK, ranked by stars (Source):
We take data security and privacy seriously. Please refer to our Security and Privacy page for more information.
Telemetry
By default, Langfuse automatically reports basic usage statistics of self-hosted instances to a centralized server (PostHog).
This helps us to:
Understand how Langfuse is used and improve the most relevant features.
Track overall usage for internal and external (e.g. fundraising) reporting.
The telemetry does not include raw traces, prompts, observations, scores, or dataset contents. We document the exact fields that are collected, where they are sent, and the implementation reference in our telemetry docs.
For Langfuse OSS, you can opt out by setting TELEMETRY_ENABLED=false.
