LiteLLM + Langfuse: My Stack for AI Cost & Quality Control

This post is being written. The outline and key topics are below — full content coming soon.

Why this matters

Once you start using LLMs in production, two things happen fast:

1. The bill gets unpredictable. You don't know which features are expensive until you're staring at a surprise invoice.
2. Quality drifts without you knowing. Model updates, prompt changes, or edge cases cause silent regressions.

LiteLLM + Langfuse solves both.

LiteLLM — one API, many models

LiteLLM gives you a single API interface for OpenAI, Anthropic Claude, Google Gemini, Mistral, and 100+ others. You call one endpoint, and LiteLLM handles:

• Provider routing
• Fallbacks (if GPT-4 is down, fall back to Claude)
• Rate limiting
• Per-model cost tracking

Langfuse — observability for every LLM call

Langfuse traces every prompt and completion with:

• Full input/output logging
• Token usage and cost per call
• Latency breakdowns
• Per-user and per-feature grouping
• Eval scores (you can attach human or automated quality scores)

Key topics

1. Setting up LiteLLM as a self-hosted proxy
2. Connecting Langfuse for automatic tracing
3. Tagging traces by feature, user, and environment
4. Setting cost alerts and budgets per model
5. Running evals on production traffic
6. Dashboard setup for non-technical stakeholders

Coming soon

Full setup guide with Docker Compose, environment config, and real cost dashboards.