Langfuse has teamed up with ClickHouse to deliver an open‑source LLM observability stack that lets developers monitor AI agents in near‑real‑time. By replacing the old PostgreSQL backend with ClickHouse Cloud, the platform now handles billions of traces per month while cutting query latency from minutes to seconds. This upgrade gives you instant insight into complex LLM workflows.
Why Switch to ClickHouse?
Traditional relational databases start to choke when trace volumes explode, leading to sluggish dashboards and delayed debugging. ClickHouse’s columnar engine is built for high‑throughput ingestion, so it can store massive telemetry streams without sacrificing speed. The result is a system that stays responsive even as your LLM applications scale.
Key Benefits for LLM Engineers
Near‑Real‑Time Query Performance
Queries that once took minutes now return in seconds, letting you spot bottlenecks while a model is still running. This immediacy shortens the feedback loop and helps you iterate faster.
Scalable Trace Storage
Because ClickHouse stores data in wide tables, memory usage drops dramatically—often by a factor of three—while still supporting billions of records each month. You’ll notice lower storage costs without compromising detail.
Open‑Source Flexibility
The stack is fully open source, so you can self‑host it, tweak the schema, or add custom modules that fit your specific agent architecture. There’s no vendor lock‑in, and you retain full control over your data.
How the Stack Works
Every LLM interaction—tool calls, intermediate prompts, state changes—is streamed into ClickHouse’s columnar store. From there, you can run ad‑hoc SQL‑style queries across millions of events in seconds, turning raw telemetry into actionable insights without writing custom pipelines.
Practical Impact for Developers
- Faster debugging cycles let you fix agent logic daily instead of weekly.
- Reduced storage footprint cuts infrastructure spend.
- Self‑hosting capability ensures compliance with internal security policies.
Getting Started
To deploy the stack, spin up a ClickHouse Cloud instance, connect Langfuse’s tracing SDK, and configure your LLM agents to emit trace events. Once the pipeline is live, you’ll have a unified dashboard that visualizes end‑to‑end execution paths. If you’ve already built custom agents, you can simply point them at the new endpoint and start collecting data immediately.
Future Outlook
Real‑time observability is becoming a core requirement for any production LLM product. As agents grow more autonomous, the ability to monitor every decision point will differentiate successful deployments from flaky experiments. With the Langfuse‑ClickHouse stack, you’re positioned to meet that demand today and scale effortlessly tomorrow.
