Langfuse specialists for your dedicated team.

Where Langfuse fits in a long-term product team.

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 your dedicated 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 a dedicated cloud and DevOps team does alongside the application developers.

Common Langfuse use cases.

• 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.

Adding Langfuse skills to your team.

Open-source observability is assigned inside AI capacity, with self-hosting chosen where trace content cannot leave the estate.

Langfuse specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.