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Langfuse
LLM observability and prompt management — trace every LLM call, score outputs, A/B test prompts, open-source self-hostable.
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About Langfuse
Langfuse is an open-source LLM engineering platform — tracing, evaluation, prompt management, and dataset creation in one tool. The honest answer to "why is my chatbot giving bad answers?" is Langfuse traces.
**What Langfuse solves:**
When an LLM application misbehaves in production, debugging requires: seeing the exact prompt sent, the model''s raw response, every retrieval step in a RAG pipeline, and the final output shown to the user. Langfuse traces the complete chain from user input to final response.
**Langfuse vs LangSmith:**
| Feature | Langfuse | LangSmith |
|---------|---------|----------|
| Open-source | Yes (MIT) | No |
| Self-hostable | Yes | No |
| Free tier | 50K traces/month | Limited |
| Prompt management | Yes | Yes |
| Evaluation datasets | Yes | Yes |
| Framework agnostic | Yes | LangChain-native |
| Pricing (paid) | $59/month | $39/month (limited) |
Langfuse is framework-agnostic — integrates with LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, and any HTTP client via the API. LangSmith is most natural for LangChain-based applications.
**Tracing a RAG pipeline:**
```python
from langfuse.decorators import observe
@observe()
def retrieve_documents(query: str) -> list:
# Vector search
return results
@observe()
def generate_answer(query: str, docs: list) -> str:
# LLM call
return answer
```
Every decorated function appears in Langfuse as a span with input, output, latency, and token costs. The trace shows exactly which documents were retrieved and which became the final answer.
**Prompt management:**
Edit prompts in the Langfuse UI, version them, and deploy new versions without a code deployment. A/B test prompt variants and score outputs to find which prompt produces better answers.
Key Features
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Tracing: decorator-based or SDK instrumentation — captures every LLM call with full input/output
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Scores: attach human or LLM-as-judge scores to traces — track quality metrics over time
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Prompt management: version-controlled prompts with production deployment and A/B testing
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Datasets: curate traces into evaluation sets — replay against new prompts to measure improvement
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Cost tracking: per-model token usage and costs across all providers in one dashboard
Pros
- ✓Open-source MIT license — self-host for free, complete data control, no vendor lock-in
- ✓Framework agnostic: works with LangChain, LlamaIndex, OpenAI SDK, or any HTTP call
- ✓Traces every LLM call: exact prompt, response, latency, token cost, and scores in a searchable UI
- ✓Prompt versioning: edit, version, and A/B test prompts from the UI without code deployments
- ✓50K traces/month free on Cloud — sufficient for development and low-traffic production
Cons
- ✗Smaller community than LangSmith — fewer tutorials and community integrations
- ✗UI can be overwhelming when first setting up evaluation and scoring pipelines
- ✗Self-hosting requires Docker Compose setup — not as simple as a SaaS signup for first-timers
Who is using Langfuse?
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AI engineers debugging why their RAG pipeline returns wrong answers
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Teams who need to track LLM costs and latency across multiple models and providers
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Product teams running A/B tests on prompt variants to improve response quality
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Companies with data sovereignty requirements who need to self-host their AI observability
Use Cases
- →Tracing a RAG pipeline to see exactly which documents were retrieved for a failing query
- →Monitoring LLM API costs per user per day to identify unexpectedly expensive usage patterns
- →A/B testing two system prompts to see which produces higher human evaluation scores
- →Building an evaluation dataset of good and bad LLM outputs to measure prompt improvements
Pricing
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Self-hosted : $0/mo — Full features, MIT license, Docker Compose, Community support
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Cloud Free : $0/mo — 50K traces/month, All features, Community support
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Cloud Pro : $59/mo — 1M traces/month, Team collaboration, Priority support, SLA
Pricing details may not be up to date. For the most accurate and current pricing, refer to the official website.
What Makes Langfuse Unique?
The open-source LLM observability platform with MIT license self-hosting — framework-agnostic tracing that shows the exact prompt, retrieval steps, and response for every LLM call, with prompt versioning and A/B testing from a UI without code deployments.
How We Rated It
Feature comparison from Langfuse and LangSmith documentation July 2025. Trace completeness evaluated on a 5-step RAG pipeline with LangChain and direct OpenAI SDK. Self-hosting setup time measured on a fresh Ubuntu 22.04 VPS with Docker Compose.
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Accuracy and Reliability 4.5/5
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Ease of Use 4.4/5
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Functionality and Features 4.6/5
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Performance and Speed 4.6/5
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Customer Support 4.3/5
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Value for Money 4.7/5
AI summary
LLM observability and prompt management — trace every LLM call, score outputs, A/B test prompts, open-source self-hostable.