What happens if Claude disappears tomorrow? You’d better build a portable AI stack with Claude, GPT, and local models, so no single provider can control your workflow.. Llms (Large Language Models), Ai Fire 101, 🔥 Ai Fire Academy.
TL;DR: Relying on a single AI provider is a hidden business risk, and it’s one that sneaks up on you. A safer setup uses multiple cloud models, one local model for sensitive work, and a routing file so each task goes to the right model automatically.
The core idea is simple: assign models by job, not by brand. Use stronger models for planning and review, cheaper ones for execution, and a local model for anything client-sensitive.
Your project folder stays at the center of everything. Skills, rules, prompts, and job descriptions all live there, so if you ever need to swap a provider, you’re changing one line in a routing file instead of rebuilding your entire workflow from scratch.
What you’ll learn:
Why single-vendor dependence is a real business problem, and how to spot if you already have it
How to assign models by job rather than defaulting to your strongest tool
Which harnesses handle which kinds of work
How to write a routing file that keeps everything predictable
What this stack actually costs, and the honest trade-offs
Table of Contents
Introduction
You’re paying $200/month for Claude (or just $20), building everything around it, and then one day you start wondering:
What happens if Claude disappears tomorrow?
Haha, seriously. Why let one AI provider control your entire workflow?
So I started building something different: a multi-model AI team that routes each job to the cheapest or strongest model automatically, based on cost and capability.
Claude, GPT, local models, multiple cloud models, self-hosted AI, and Claude Code alternatives. The goal? A portable AI stack that you control.
If the answer is “a lot,” you already have a dependency problem. Let’s fix that.
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