# LLM Observability with LangFuse, OpenRouter, and Encore.ts

DevFeed: [LLM Observability with LangFuse, OpenRouter, and Encore.ts](<https://devfeed.tech/articles/llm-observability-with-langfuse-openrouter-and-encore-ts-17816.md>)

Original publisher: [Read original article](<https://encore.dev/blog/langfuse-tutorial>)

Author: Ivan Cernja

Published: 2025-11-27T00:00:00Z

Content type: tutorial

Language: en

Sources: [Encore Updates](<https://devfeed.tech/sources/encore-updates.md>)

Topics: [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [API](<https://devfeed.tech/topics/api.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [llm](<https://devfeed.tech/tags/llm.md>), [observability](<https://devfeed.tech/tags/observability.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

## AI overview

This tutorial shows how to build and deploy a chat backend with LLM observability using LangFuse, OpenRouter, and Encore.ts. It covers tracing prompts and completions, tracking token usage, costs, latency, and user feedback, and using that data to compare models and optimize an application.

## Source excerpt

Building AI apps with built-in observability. Track every call, optimize performance.