I keep running into the same problem with Claude Code Pro ($20/month): I burn through the usage limits faster than I expect. The obvious solution is upgrading to the $200/month plan, but that feels excessive for how I actually use it.

So I started exploring alternatives.

What I’ve realised is that the best approach isn’t replacing Claude Code entirely - it’s building a hybrid AI developer stack where different models handle different types of work.

Think of it like a compute tiering strategy.

The Hybrid AI Stack

Instead of relying on a single frontier model, I’m moving towards something like this:

Task Model Type Cost
Quick edits, small scripts Local model Free
General coding tasks Cheap cloud models Pennies
Architecture / complex work Claude / frontier models Occasional

In practice this means most requests never touch the expensive models.

Running Models Locally

For basic coding tasks, local models are already surprisingly capable.

Tools to run them:

  • Ollama
  • LM Studio

Then connect them to an IDE agent such as:

  • Continue.dev
  • Cline
  • Aider

Some good coding models:

Model Size Notes
DeepSeek Coder 6B / 33B Strong coding performance
Qwen2.5 Coder 7B / 32B Good reasoning + tool use
Codestral ~22B Solid refactoring
Llama-3.1 8B Fast and lightweight

Example:

brew install ollama
ollama run deepseek-coder:6.7b

Local models are great for:

  • writing functions
  • fixing bugs
  • editing files
  • explaining code
  • generating tests

They struggle more with large multi-file reasoning, which is where frontier models still shine.

Cheap Cloud Models

Instead of paying subscription pricing, you can also call open models via inference providers.

Some good ones:

  • Groq
  • Together AI
  • DeepInfra
  • Fireworks AI

Typical pricing is tiny:

Model Approx Price
Llama-3 70B ~$0.20 / million tokens
DeepSeek V3 ~$0.30 / million tokens
Qwen coder ~$0.10 / million tokens

For normal coding tasks this means pennies per session.

Free Coding Assistants

There are also tools offering free or semi-free coding models:

Tool Notes
Cursor small models + limited premium usage
Codeium unlimited completions
Tabnine free tier available
JetBrains AI local + quota models

Some allow plugging in your own local models, which removes limits entirely.

Using Claude Code Less (But More Strategically)

The key insight for me was this:

Frontier models like Claude are still the best for:

  • architecture decisions
  • large refactors
  • complex reasoning
  • multi-file edits

But they’re overkill for everyday coding tasks.

So instead of using Claude for everything, it becomes the top tier tool in the stack.

The Setup I’m Experimenting With

Something roughly like this:

VS Code
 ├─ Continue.dev
 ├─ Ollama (local models)
 │   ├─ qwen2.5-coder
 │   └─ deepseek-coder
 │
 └─ API fallback
     ├─ Groq
     └─ Claude Code

Usage ends up looking like:

  • 80% local models
  • 15% cheap APIs
  • 5% frontier models

Which dramatically reduces usage limits.

Hardware Option

If you code constantly, running bigger models locally becomes viable.

Typical developer setups:

Hardware Cost
Used RTX 3090 workstation ~$1.5–2k
RTX 4090 workstation ~$3–4k

A 24GB GPU can run models in the 30B parameter range, which are good enough for most coding tasks.

What’s Happening in the AI Coding Space

One thing is clear: coding models are rapidly commoditising.

Frontier models still lead in reasoning and agent reliability, but open models are already competitive for:

  • writing functions
  • generating boilerplate
  • fixing bugs
  • explaining code

The gap is shrinking fast.

My Takeaway

Instead of paying for bigger and bigger subscriptions, it makes more sense to build a layered AI workflow:

  • local models for everyday work
  • cheap cloud inference for heavier tasks
  • frontier models only when needed

You keep the power of Claude, but avoid hitting limits constantly.

And realistically, this is probably how most developer AI stacks will look going forward.