"By bringing Langfuse in-house, ClickHouse can offer customers a native way to collect, store, and analyze large volumes of LLM telemetry alongside operational and business data, helping teams debug models faster, control costs, and run more reliable AI workloads without relying on a separate observability tool, Tyagi added."
"'Most companies are not failing to build AI features, rather they're failing to explain them, trust them, or afford them. Models drift, costs spike, and business users can't tell if decisions are defensible. ClickHouse is going after that precise blind spot,' Gogia said."
"'With Langfuse in-house, it now has the tooling to convert every LLM call into a structured, queryable record,' Gogia noted."
ClickHouse integrated Langfuse to provide a native pipeline for collecting, storing, and analyzing LLM telemetry alongside operational and business data. The capability converts individual LLM calls into structured, queryable records, enabling faster debugging of models, tighter cost control, and more reliable AI workloads without depending on a separate observability tool. The integration targets enterprise pain points around explainability, trust, and affordability, addressing model drift, rising inference costs, and lack of defensibility in automated decisions. The tooling allows teams and business users to correlate telemetry with business signals and operations to surface issues and justify AI-driven outcomes.
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