FestivalIn personFree

VIP Salon: Production AI with ClickHouse and Langfuse

SEP9
18:00 · Asia / Singapore

About this event

About the event
Building production AI products means two hard problems at once: a data layer fast enough to power real-time analytics, and LLM calls observable enough to debug at scale. Most teams stitch together a separate analytics database, a warehouse, an observability stack, a vector store, and a tracing tool — and the seams are where production AI breaks.
Join ClickHouse and the Langfuse team for an evening on the Agentic Data Stack — one columnar engine for analytics, observability, Postgres, and agents. The payoff: a unified stack and lower total cost of running AI in production.
What we'll cover
The Agentic Data Stack — ClickHouse’s vision for a unified foundation for production AI: real-time analytics, observability (ClickStack), Postgres (ClickHouse Postgres, in public beta), and agents (managed agentic analytics powered by Claude) — all on one columnar engine. One stack, one billing line, one telemetry surface; less data duplication, fewer pipelines to operate.
Langfuse — open-source LLM observability, now part of the ClickHouse family. Trace every LLM call, tool invocation, retrieval step, and agent action with cost, latency, and quality analytics — on a ClickHouse-native data model that scales to hundreds of millions of observations.
You'll walk away with
A clear mental model for the Agentic Data Stack: how ClickHouse, ClickStack, ClickHouse Postgres, and ClickHouse Agents combine into one engine — replacing the fragmented analytics / warehouse / observability / vector patchwork with a single, unifying data layer.
How to instrument an LLM application with Langfuse in under 30 minutes — open-source, OpenTelemetry-based, with 100+ framework integrations (LangChain, LlamaIndex, LiteLLM, OpenAI, and more) and Python and JS/TS SDKs.
A reference architecture for production AI on the Agentic Data Stack: real-time analytics + LLM observability on the same columnar engine — unifying the stack while cutting total cost.
Practical patterns for monitoring cost, la

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