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Getting Off the Cloud AI Treadmill

How to reduce dependency on paid AI without losing what makes it useful

Cloud AI creates a dependency loop. Your productivity gets better. Your bill gets bigger. Your workflow becomes tied to services you don't control. Usage limits. Price increases. Service changes. Outages during deadline week.

The answer isn't going fully local (yet). The answer is being strategic about what actually needs cloud capabilities and what doesn't.

The 80/20 Split

Most AI tasks don't need GPT-4. They need "good enough, fast, and available."

Local handles 80%

  • Code completion
  • Basic refactoring
  • Documentation
  • Simple Q&A
  • Context switching help
  • Routine summaries

Cloud for 20%

  • Complex architecture decisions
  • Novel problems requiring broad context
  • Specialized domain knowledge
  • Large-scale analysis
  • Creative brainstorming

The key insight: most routine tasks don't need frontier models. A fast local model at infinite availability beats a better model you're rationing.

How to Start

Hardware reality check

  • Minimum: 16GB RAM, modern CPU - runs small models
  • Useful: 32GB RAM, GPU with 8GB+ VRAM - runs 7B models well
  • Comfortable: 64GB RAM, RTX 4090 or M-series Mac - runs 70B models

I'm on 8GB. It works. Not comfortable, but it works.

Model recommendations

  • Code: Llama 3.1 8B, DeepSeek Coder, Codestral
  • General: Llama 3.1 70B if you have the RAM, Mixtral 8x7B if not
  • Starting point: Ollama makes this easy. Pull models, run them, done.

The transition path

Week 1: Install Ollama. Try local for code completion only.
Week 2-4: Add documentation and simple Q&A.
Month 2+: Gradually shift routine tasks. Track what still needs cloud.
Ongoing: Measure cost savings. Adjust the split based on actual performance.

What You Get

  • Cost control: Predictable hardware costs vs. variable bills
  • Privacy: Sensitive stuff never leaves your machine
  • Availability: Works offline. Works during outages.
  • Customization: Fine-tune for your specific use cases
  • Independence: Less pressure from pricing changes

What Might Go Wrong

Capability gaps: Local models are worse at complex reasoning. That's why you keep cloud for the 20%. Don't try to force local where it doesn't work.

Hardware cost: The break-even math depends on how much you're spending on cloud. If it's $20/month, hardware upgrades might not make sense. If it's $200/month, they probably do.

Setup friction: It's not hard, but it's not zero either. Start with Ollama because it handles most of the complexity.

The goal: Flip from 80% cloud / 20% local to 80% local / 20% cloud. Takes 6-12 months if you're measuring and adjusting.

Not because local is ideologically pure. Because strategic independence is worth the investment.