Langfuse Has a Free LLM Observability Platform — Debug and Monitor AI Applications

Langfuse is an open-source LLM observability platform — trace, evaluate, and monitor your AI applications.

What You Get for Free (Free Tier / Self-hosted)

• Tracing — see every LLM call, prompt, and response
• Cost tracking — track token usage and costs per request
• Latency — monitor response times across models
• Evaluations — score outputs with custom metrics
• Prompt management — version and A/B test prompts
• User feedback — collect thumbs up/down on responses
• Datasets — create evaluation datasets from production data
• Integrations — OpenAI, Anthropic, LangChain, LlamaIndex

Quick Start

pip install langfuse

from langfuse import Langfuse
from openai import OpenAI

langfuse = Langfuse()
client = OpenAI()

trace = langfuse.trace(name="chat")
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)
trace.generation(
    name="chat-completion",
    model="gpt-4",
    input=[{"role": "user", "content": "Hello"}],
    output=response.choices[0].message.content,
    usage={"input": response.usage.prompt_tokens, "output": response.usage.completion_tokens}
)

Why Developers Switch from Logging to Console

console.log doesn't scale for AI debugging:

• Full trace — see prompt → response → score in one view
• Cost visibility — know exactly how much each user costs
• Prompt versioning — compare prompt v1 vs v2 performance
• Evaluation — automated quality scoring, not manual review

An AI chatbot had random hallucinations but no way to debug them. After Langfuse: identified that 90% of bad outputs came from one prompt template, fixed it in 10 minutes, hallucination rate dropped from 12% to 2%.

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