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Niuniu Ox
Niuniu Ox

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I Replaced My $99/Month AI Coding Assistant with This Free Stack — It Writes Better Code

I Replaced My $99/Month AI Coding Assistant with This Free Stack — It Writes Better Code

Three months ago I was paying for GitHub Copilot. Then I added Cursor. Then Codeium. My AI coding bill hit $99/month. Last week I cancelled everything.

The Stack That Replaced It

Tool What It Does Cost
MonkeyCode Code generation, refactoring, tests $0 (open source)
Continue IDE autocomplete, chat $0 (open source)
Ollama + DeepSeek-Coder Local LLM inference $0 (runs on my machine)
Tabby Self-hosted Copilot alternative $0 (open source)

Total: $0/month. No API keys. No rate limits. No code leaving my machine.

Setup (15 Minutes)

1. Ollama + DeepSeek-Coder (Local Model) 

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull the model (6.7B params, 4GB RAM)
ollama pull deepseek-coder:6.7b

# Test
ollama run deepseek-coder:6.7b "Write a Python function to merge two sorted lists"
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Why DeepSeek-Coder: Beats GPT-4 on HumanEval (79.3% vs 67.0%) and it's free.

2. Continue (VS Code Extension) 

// ~/.continue/config.json
{
  "models": [
    {
      "title": "DeepSeek Local",
      "provider": "ollama",
      "model": "deepseek-coder:6.7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Tabby",
    "provider": "ollama",
    "model": "deepseek-coder:6.7b"
  }
}
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What you get:

  • Ctrl+I — inline edit with natural language
  • Ctrl+Shift+L — chat with context
  • Tab autocomplete — same as Copilot, but local

3. MonkeyCode (Agent Mode) 

pip install monkeycode
monkeycode --model deepseek-coder:6.7b --local
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Example session:

> Add error handling to this function and write tests

[MonkeyCode reads your file, writes the code, runs pytest, shows results]

✓ Added try/except with logging
✓ Created 3 test cases (edge cases: empty input, None, invalid type)
✓ All tests pass
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4. Tabby (Self-Hosted Server) 

# Docker
docker run -it --gpus all -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve --model deepseek-coder:6.7b
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VS Code extension: Connects to localhost:8080, gives you Copilot-style completions.

The Comparison

Feature Copilot ($10) Cursor ($20) My Stack ($0)
Autocomplete ✅ ✅ ✅ (Tabby)
Chat ❌ ✅ ✅ (Continue)
Agent mode ❌ ✅ ✅ (MonkeyCode)
Local/private ❌ ❌ ✅
Works offline ❌ ❌ ✅
Custom models ❌ ❌ ✅

Real Code Comparison

Task: Write a rate limiter with Redis

Copilot:

# Basic sliding window, no error handling
def rate_limit(key, limit, window):
    current = redis.get(key) or 0
    if current >= limit:
        return False
    redis.incr(key)
    redis.expire(key, window)
    return True
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MonkeyCode + DeepSeek:

import time
import redis
from typing import Optional

class RateLimiter:
    def __init__(self, redis_client: redis.Redis, limit: int, window: int):
        self.redis = redis_client
        self.limit = limit
        self.window = window

    def is_allowed(self, key: str) -> bool:
        """Token bucket with Redis-backed sliding window."""
        now = time.time()
        pipeline = self.redis.pipeline()

        # Clean old entries
        pipeline.zremrangebyscore(f"rate:{key}", 0, now - self.window)
        # Count current
        pipeline.zcard(f"rate:{key}")
        # Add new entry
        pipeline.zadd(f"rate:{key}", {str(now): now})
        pipeline.expire(f"rate:{key}", self.window)

        results = pipeline.execute()
        return results[1] < self.limit

    def retry_after(self, key: str) -> Optional[float]:
        """Seconds until next allowed request."""
        oldest = self.redis.zrange(f"rate:{key}", 0, 0, withscores=True)
        if not oldest:
            return None
        return max(0, oldest[0][1] + self.window - time.time())
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Difference: Production-ready vs "works on my machine"

The Catch

Hardware: You need 8GB+ RAM for 6.7B models. 16GB for 13B. No GPU required (CPU inference is ~20 tokens/sec, usable).

Speed: Local is slower than API. 2-3 seconds for a full function vs 500ms. For autocomplete, you don't notice. For agent mode, you go make coffee.

Quality: DeepSeek-Coder is 90% as good as GPT-4 for code. For the 10% it struggles with, I use the free tier of Claude (no credit card).

My New Workflow

  1. Tabby — always-on autocomplete (local, instant)
  2. Continue — quick edits and questions (local, fast)
  3. MonkeyCode — complex features, tests, refactoring (local, thorough)
  4. Claude free tier — when I need GPT-4-level reasoning (cloud, free)

Have you tried running local LLMs for coding? What's your setup — and what made you switch back to paid (or stay free)?

Full setup guide: Free Dev Resources

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