Building an AI demo application is easy, but making it work reliably is hard. Open-ended user inputs, model reasoning, and agentic tool use require a new workflow to iteratively measure, evaluate, and improve these systems as a team.

Langfuse helps developers solve that problem. Its open-source LLM engineering platform gives teams the tools to trace, evaluate, and improve performance, whether they’re debugging prompts, testing model responses, or analyzing billions of interactions.

For companies working with sensitive or large-scale data, part of Langfuse’s appeal lies in its flexibility: it can be self-hosted or used as a managed cloud service. his flexibility helped Langfuse gain early traction with large enterprises—but it also created a scaling challenge. By mid-2024, the simple Postgres-based architecture that powered both their cloud and self-hosted offerings was under pressure. The platform was handling billions of rows, fielding complex queries across multiple UIs, and struggling to keep up with rapidly scaling customers generating massive amounts of data. Something had to change.

At a March 2025 ClickHouse meetup in San Francisco, Langfuse co-founder Clemens Rawert shared how the team re-architected their platform with ClickHouse as the “centerpiece” of their data operations. He also explained how they rolled out that change to thousands of self-hosted users, turning a major infrastructure change into a win for the entire community. — Read More