What does the self-hosted version of Langfuse actually cover in production?

Langfuse is open source and free to download. Self-hosting v3 in production requires PostgreSQL, ClickHouse, Redis, S3 and Kubernetes: non-trivial infrastructure to operate. The entry tier of the managed version is capped on volume, number of users and retention period. The Idun open source platform integrates Langfuse out of the box, with no separate stack.

Does the Idun open source platform replace Langfuse or complement it?

The Idun open source platform enables Langfuse natively without replacing it. You get Langfuse observability without deploying it separately. On top of traces, the platform adds real-time guardrails, memory management, session management and governance for the agents you connect to it. The two work together with a single command.

How long does it take to integrate Langfuse into an existing project with the Idun open source platform?

Run pip install idun-agent-engine, then enter your Langfuse API key: traces appear in your dashboard. For an existing LangGraph or Google ADK project, allow 15 to 30 minutes for the full integration. Documentation is available at docs.idun-group.com.

What does the Idun open source platform add beyond self-hosting Langfuse on its own?

Beyond Langfuse observability: sub-10ms guardrails (PII, prompt injection), native memory and session persistence (SQLite/PostgreSQL) and sovereign deployment (on-premise, cloud or hybrid). Langfuse covers observation, tracing and full evaluation of outputs, from machine learning models to LLM agents. The Idun open source platform covers the full production readiness of your pipelines.

Is the Idun open source platform compatible with our current tools (N8N, LangChain, OpenAI)?

LangGraph and Google ADK are supported natively. LangChain integration is in progress. The OpenAI SDK is compatible through OpenTelemetry. For teams on N8N, Idun Group offers a migration service to LangGraph, with Langfuse observability live from the first day in production.

How does Idun Group handle security and GDPR / AI Act compliance?

The Idun open source platform detects and blocks PII before it is sent to the model (sub-10ms) and generates auditable traces. On-premise deployment is available for organisations with data sovereignty requirements. On the European timeline, the Article 50 transparency obligations have applied since 2 August 2026, and compliance for Annex III high-risk systems has been postponed to 2 December 2027.

What is the minimum commitment to work with Idun Group?

The 30-minute discovery call is the natural starting point. What we sell is a fixed-price engagement whose scope is set during the discovery phase, delivered by a dedicated team that knows the LLMOps ecosystem. No pricing is published: any quote follows the analysis and is presented in a meeting.

Langfuse: LLM observability and engineering for your AI agents with Idun Engine

TL;DR: Langfuse traces every call your LLM agents make in production. Self-hosting v3, however, requires PostgreSQL + ClickHouse + Redis + S3 + Kubernetes. The Idun open source platform integrates Langfuse out of the box with a single pip install command, then adds sub-10ms guardrails, session memory and the AI Act compliance that observability alone does not cover.

Langfuse is an open source LLM observability platform that traces every call your AI agents make in production. With the Idun open source platform, Langfuse is switched on with a single Python pip install command, with no separate infrastructure to deploy. We add real-time guardrails, data and memory management, and the regulatory governance that observability alone does not cover, including for your agentic data science workflows. Reference: at LVMH, design and operation of a generative AI agent platform in production. Full documentation, and use cases validated in a 30-minute discovery call.

Langfuse: the open source LLM engineering platform

Trace, analyse and iterate on your LLM applications

Langfuse is an open source LLM engineering platform that lets teams monitor LLMs in production. It observes and captures traces of every API call to the model: latency, inference cost, tokens consumed per response and errors returned. These metrics let you debug issues and monitor performance before they affect your end users. Prompt management, automatic evaluation and evaluation data management round out Langfuse. With more than 80 native OpenTelemetry integrations (langfuse.com/docs, 2026), Langfuse covers the full range of modern LLM applications and engineering needs, from machine learning models to LangGraph agents with their prompt templates in production.

Langfuse, OpenTelemetry and agent frameworks

OpenTelemetry is becoming the de facto standard for observability of agentic systems in production, and Langfuse supports it natively. As an open source LLM engineering platform, Langfuse connects to every major framework: LangGraph, Google ADK, LangChain, the OpenAI SDK and LiteLLM. This convergence on an open standard means your existing Python stack gets instrumented without a rewrite. Langfuse helps teams monitor their AI agents and large language model applications, however diverse their technical ecosystem.

Langfuse joins ClickHouse: what changes for your stack

ClickHouse acquired Langfuse on 16 January 2026. The MIT licence is maintained and self-hosting remains possible in the short term. What the acquisition changes: Langfuse's cloud roadmap and observability priorities are now aligned with those of a company whose business model differs from yours. The question of long-term strategic dependency deserves an answer before any structural investment in your LLMOps stack.

The Idun open source platform: Langfuse built in, production-ready in one command

One install, full observability: `pip install idun-agent-engine`

pip install idun-agent-engine, and Langfuse is live. The Idun open source platform integrates Langfuse out of the box, without deploying the separate stack that a self-hosted Langfuse v3 requires in production: PostgreSQL + ClickHouse + Redis + S3 + Kubernetes (Glassbrain.dev, 2026). You write your first agent in Python, manage your prompt templates from the dashboard, and traces appear with no extra API key to configure. Your application code stays unchanged. That is what it concretely means to use Langfuse in production without friction: observability live in 15 minutes, production-ready in a few hours.