Langfuse.
Langfuse is an open-source observability and analytics platform for language model applications, covering tracing, cost tracking, evaluation and prompt management, with self-hosted and managed deployment.
Where Langfuse fits on a long engagement.
Self-hosting is the property that decides this choice for many organisations. Traces contain user input, and sending that to a third-party observability service is a data protection question that some clients simply cannot answer affirmatively. Running it inside your own estate removes the question.
Cost tracking per trace is more useful than it sounds. Token spend attributed to a specific feature, user or tenant makes it possible to find the expensive paths, and that attribution is difficult to reconstruct from a provider invoice after the fact.
What an assigned team does with Langfuse.
Prompt management inside the observability platform closes a loop worth closing. Prompts versioned in the same place traces are recorded means a change can be correlated with the behaviour that followed it.
Wiring that together properly is part of the platform work scoped alongside application capacity under devops as a service.
What we use Langfuse for.
- Observability without sending data out Self-hosted tracing where user input cannot go to a third party.
- Cost attributed to features Token spend per path, so the expensive parts are identifiable.
- Prompt versions correlated with behaviour A change and its effect visible in the same system.
How Langfuse capacity is assigned.
Open-source observability is assigned inside AI capacity, with self-hosting chosen where trace content cannot leave the estate.
Tell us what your roadmap needs Langfuse for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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