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LiteLLM as a Gateway and Cost Control

When an app can call multiple models, you need a common gateway: keys, limits, budgets, fallbacks, cache, and traces. That's the role of an LLM gateway.

  • Understand LiteLLM as a proxy between your app and multiple models.
  • Design keys per user, team, or environment.
  • Control cost, fallbacks, and cache before surprises happen.

Conceptual setup

Terminal
model_list:
  - model_name: local-qwen
    litellm_params:
      model: openai/Qwen/Qwen3-8B
      api_base: http://127.0.0.1:8000/v1
      api_key: local

  - model_name: backup-cloud
    litellm_params:
      model: openai/gpt-4.1-mini
      api_key: os.environ/OPENAI_API_KEY

What to measure per key

  • Input and output tokens.
  • Estimated or actual cost.
  • Model used and fallback applied.
  • Latency and errors.
  • User, team, or tenant.
Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
01

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

You do not need every course: choose the component your system needs and return as it grows.

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