I've recently completed a thorough testing of Langfuse (v3.95.2 OSS), and I wanted to share my observations on its capabilities and overall suitability for AI model observability. My overall impression is that Langfuse is a robust and beneficial tool, particularly given its specialized focus on AI. While it leverages OpenTelemetry for instrumentation, meaning some features might overlap with other general observability tools, Langfuse's dedicated AI-centric functionalities really give it a distinct advantage, especially in areas like scoring, evaluation, and prompt management.
Detailed Observations
Multiple Projects & Permission Management
One of the first things that stood out to me was Langfuse's sophisticated and granular permission management system. It's incredibly well-designed for environments needing precise control. I found it supports creating multiple projects, each with its own distinct roles and permissions. The availability of both organization-level and project-level roles allows for incredibly fine-tuned control over user access and actions. For instance, an "Owner" at the organization level has extensive permissions, covering everything from project creation and organization management (like API keys, updates, deletion, member management, and billing) to specific project actions. Within individual projects, the roles are even more detailed, covering comprehensive control over functionalities like:
Project Management: The ability to read, update, and delete projects, manage members, API keys, and integrations.
Data & Observability: Permissions to publish, bookmark, tag objects, delete traces, manage scores and configurations, and handle datasets, prompts, models, and evaluation templates.
LLM Integration: Control over LLM API keys, schemas, and tools.
Data Export & Automation: Managing batch exports, comments, annotation queues, prompt experiments, audit logs, dashboards, and automations.
This level of control is genuinely impressive and crucial for larger teams or projects with varying access needs.
Key Features I Noticed
Several features really caught my attention during testing:
LLM Playground: The dedicated LLM Playground is a great addition, making it easy to experiment with different large language models.
LLM Model Connections & Pricing: Langfuse offers extensive out-of-the-box connections to a wide array of LLM models, and crucially, includes their associated price details. This is incredibly valuable for cost tracking and optimization, something often overlooked in other tools.
Slack Notification Support: The platform supports Slack notifications, which is fantastic for real-time alerts and keeping teams updated on critical events.
Trace Data Export: The ability to export trace data to S3-compatible external storages, with the option to schedule these exports, is a practical feature for data retention and analysis.
Dashboards:
Prebuilt Dashboards: Langfuse provides useful prebuilt dashboards for cost, usage, and latency. However, I did notice that community dashboards seem limited at this point.
Custom Dashboards: While custom dashboards and widgets are supported, a notable limitation I found was the absence of an option to export or share these dashboards across different projects.
Prompt Engineering & Management: This is a significant strength of Langfuse.
It offers a centralized repository for managing prompts, which is a huge advantage for organization.
Users can organize prompts into folders, which is helpful for larger prompt libraries.
Version control for prompts is supported, allowing for tracking changes and easy rollbacks – a critical feature for iterative prompt development.
Prompts can be called directly within a project using the Langfuse client, which really streamlines integrating managed prompts into applications.
The support for webhook calls and automations on any CRUD changes to a prompt enables dynamic responses to prompt modifications, which opens up interesting possibilities.
Tracing & Metrics:
Traces: Langfuse's tracing capabilities are robust, including token and cost tracking. The rate of trace collection can also be throttled using a sampling value.
Sensitive Information Masking: The platform allows for masking sensitive information before it's sent to the server. This is achieved by writing a masking function that matches sensitive information by pattern and applying it to the Langfuse client – a crucial security feature.
Observations, Sessions, and Users Tracking: Comprehensive tracking of observations, sessions, and users is well-facilitated.
SDK Support: My testing primarily used the Python SDK, but it has support for other languages, including JavaScript, Langchain Python, and Langchain JS.
UI Features: The UI offers various features for analyzing trace data, including span and timeline views, filtering, searching, data download, cost breakdown, and temperature details – making analysis quite user-friendly.
Playground: The playground has strong out-of-the-box LLM connections for major providers like Google Vertex AI, Google AI Studio, Anthropic, OpenAI, Azure, and Bedrock. It also supports other LLMs through custom API calls, offering flexibility.
Asynchronous Batch Data Collection: Langfuse is designed to send trace data in batches asynchronously.
Security Monitoring: I noted that the platform includes capabilities for security monitoring, specifically collecting traces about guardrails like llm_guards and their effectiveness in blocking banned topics.
MCP Server Integration: The inclusion of an MCP server simplifies integrating langfuse tracing in an application. MCB is available for various AI coding agents like Cursor and Copilot.
Evaluation: The evaluation framework is a definite standout.
LLM as Evaluator: The ability to leverage LLMs themselves to act as evaluators is a powerful feature for automated assessment.
Scoring Categories & Manual Assignment: The platform allows for setting up scoring configs and manually assigning scores to observations.
Dataset-Based Evaluation: Running evaluations against predefined datasets of questions and expected outputs provides a structured and repeatable approach to assessing model performance.
Pricing
Langfuse offers a sensible tiered pricing model, which I think caters well to various user needs. The most appealing aspect for many will be the self-hosted (open-source) option, which provides all core features (observability, evaluation, prompt management, and datasets) for free. This gives users complete control over their data and infrastructure. Langfuse Cloud is also available for those who prefer a managed service.
Conclusion
To sum it up, Langfuse is, in my observation, a highly effective and specialized tool for AI model observability. Its dedicated features for AI, particularly its robust scoring, evaluation, and prompt management capabilities, provide significant advantages over more general-purpose observability tools. While its use of OpenTelemetry means some functional overlap exists, Langfuse's focused approach gives it a distinct edge in the AI domain. The strong permission management, extensive LLM integrations, and comprehensive tracing and evaluation features make it a truly valuable asset for anyone working with AI models. I found its strengths in prompt management and its advanced evaluation features to be particularly handy.
Example Using Annotations
from google.genai import Client, types
from langfuse import Langfuse, observe
from dotenv import load_dotenv
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor
load_dotenv()
client = Client(api_key="")
langfuse = Langfuse()
GoogleGenAIInstrumentor().instrument()
@observe(as_type="generation")
def generate():
langfuse.update_current_trace(user_id="bishal")
prompt = langfuse.get_prompt("glaze/female", label="production")
langfuse.update_current_generation(
prompt=prompt,
)
compined_prompt = prompt.compile(name="")
response = client.models.generate_content(
model="gemini-2.5-flash",
config=types.GenerateContentConfig(
temperature=0.2,
),
contents=compined_prompt,
)
print(response.text)
generate()
Screenshots
