langfuse-performance-tuning

name	langfuse-performance-tuning
description	Optimize Langfuse tracing performance for high-throughput applications. Use when experiencing latency issues, optimizing trace overhead, or scaling Langfuse for production workloads. Trigger with phrases like "langfuse performance", "optimize langfuse", "langfuse latency", "langfuse overhead", "langfuse slow".
allowed-tools	Read, Write, Edit
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","langfuse","performance","scaling","tracing"]
compatibility	Designed for Claude Code

Langfuse Performance Tuning

Overview

Optimize Langfuse tracing for minimal overhead and maximum throughput: benchmark measurement, batch tuning, non-blocking patterns, payload optimization, sampling, and memory management.

Prerequisites

• Existing Langfuse integration
• Performance baseline to compare against
• Understanding of async patterns

Performance Targets

Metric	Target	Critical
Trace creation overhead	< 1ms	< 5ms
Flush latency (batch)	< 100ms	< 500ms
Memory per active trace	< 1KB	< 5KB
CPU overhead	< 1%	< 5%

Instructions

Step 1: Benchmark Current Performance

// scripts/benchmark-langfuse.ts
import { performance } from "perf_hooks";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

async function benchmark() {
  const sdk = new NodeSDK({
    spanProcessors: [new LangfuseSpanProcessor()],
  });
  sdk.start();

  const iterations = 1000;

  // Measure trace creation
  const : [] = [];
   ( i = ; i < iterations; i++) {
     start = performance.();
     (,  () => {
      ({ : { i }, : { :  } });
    });
    timings.(performance.() - start);
  }

   sorted = timings.( a - b);
  .();
  .();
  .();
  .();
  .();
  .();

   flushStart = performance.();
   sdk.();
  .();
}

();