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Put a thin interface between your app and the model. Flip an env var to compare GPT, Claude via OpenRouter, Gemini, or Ollama. Keep running when a provider rate-limits or goes down.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
Engineers are learning here from
Abstract OpenAI, OpenRouter, Gemini, and Ollama behind one chat() function with automatic fallback so you flip an env var to compare any two models without rewriting your app.
Swap OpenAI, OpenRouter, Gemini, and Ollama behind one chat() function with automatic fallback.
What you'll ship
What you'll learn
Curriculum
Why a provider abstraction
Feel the lock-in problem, then design a single chat() contract that makes provider swaps cheap.
OpenAI-compatible providers
Wrap OpenAI, OpenRouter, Fireworks, and Gemini behind the same chat() function using their OpenAI-compatible endpoints.
Local and self-hosted
Point the same chat() function at Ollama, LM Studio, and vLLM without touching application code.
Fallback and retries
Build a fallback chain, add exponential backoff with tenacity, and route cost-aware across cheap and premium models.
Production polish
Add structured logging, env-driven model swaps in CI, and graduate the provider pattern from workshop to production.
Who it's for
who are tired of rewriting the service layer every time a new model lands
who want a hedge against a single vendor pricing change or outage
who need to offer cloud and on-prem models behind the same internal API
FAQ
No. You need one cloud key (OpenRouter is easiest, and it fronts dozens of models) plus Ollama running locally. Every example is shaped so you can swap the provider later.
You can, and many teams do. This course teaches the pattern so you understand what those libraries abstract, can debug them when they break, and can build a lighter custom version for products with tight latency and cost constraints.
For most apps, yes. But a provider abstraction is still valuable because OpenRouter itself can go down, prices can change, and some customers require direct OpenAI or on-prem Ollama. One interface keeps your options open.
Yes. The chat function supports a stream flag, and the fallback logic wraps streaming responses so the learner can swap between providers in either mode.
Pricing
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One-time purchase. Lifetime access to every lesson, exercise, and update.
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Still deciding? Ask Param a question
The fastest way to future-proof an LLM app is a thin interface and a good fallback.
Building multi-provider LLM apps with OpenRouter
$29 one-time