langfuse-cost-tuning

# Langfuse Cost Tuning ## Overview Track, analyze, and optimize LLM costs using Langfuse's built-in token/cost tracking, the Metrics API for programmatic cost analysis, model routing for cost reduction, and automated budget alerts. ## Prerequisites - Langfuse tracing with token usage captured (via `observeOpenAI` or manual `usage` fields) - For Metrics API: `@langfuse/client` installed - Understanding of LLM pricing models ## How Langfuse Tracks Costs Langfuse automatically calculates costs for supported models (OpenAI, Anthropic, Google) when token usage is captured. For custom models, you can configure pricing in the Langfuse UI under **Settings > Model Definitions**. Cost tracking works on observations of type **generation** and **embedding**. The `observeOpenAI` wrapper captures usage automatically; for manual tracing, include `usage` in your observation updates. ## Instructions ### Step 1: Ensure Token Usage is Captured ```typescript // Automatic: observeOpenAI captures everything import { observeOpenAI } from "@langfuse/openai"; const openai = observeOpenAI(new OpenAI()); // Tokens, model, latency, and cost are all auto-tracked // Manual: include usage in generation observations import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing"; await startActiveObservation( { name: "llm-call", asType: "generation" }, async () => { updateActiveObservation({ model: "gpt-4o" }); // Model required for cost calc const response = await...