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niuniu
niuniu

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I Self-Hosted My AI Coding Stack with Ollama, Continue, and Tabby — Total Cost $0/Month

Three months ago I cancelled every AI coding subscription. Not as a protest — as an experiment. Could a fully local stack actually replace the $10-20/month tools for daily work?

The stack: Ollama (model runtime) + Continue (VS Code/JetBrains assistant) + Tabby (self-hosted completion server). Total monthly cost: $0. Total setup time: one evening.

The Numbers

Paid stack (before) Local stack (now)
Cost $10-20/mo per seat $0
Code leaves my machine Yes Never
Works offline No Yes (planes, trains, dead wifi)
Autocomplete latency 300-600ms (network) 80-150ms (local)
Chat quality (coding) Frontier models 7B-class local models
Hardware needed None GPU or Apple Silicon, 16GB+ RAM

Honest scorecard: I win on cost, privacy, and latency. I lose on raw chat intelligence for hard architectural questions. That's the real trade — anyone who says otherwise is selling something.

What Each Piece Does

Ollama — the engine. One command per model:

ollama pull qwen2.5-coder:7b      # my daily driver for autocomplete + chat
ollama pull deepseek-coder-v2:16k # heavier lifting, needs more VRAM
ollama serve                      # localhost:11434, OpenAI-compatible API
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Continue — the chat/edit layer in your editor. Point it at Ollama in ~/.continue/config.json:

{
  "models": [
    {
      "title": "Local Coder",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Tabby",
    "provider": "openai",
    "apiBase": "http://localhost:8080/v1",
    "model": "tabby"
  }
}
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Tabby — the autocomplete server. This is the Copilot replacement. It does sub-150ms fill-in-the-middle completions, self-hosted:

docker run -d --gpus all -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve --model Qwen2.5-Coder-1.5B --device cuda
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(Apple Silicon: ollama alone handles autocomplete fine through Continue — Tabby is optional.)

3 Months of Honest Data

  • Acceptance rate on completions: 31% (vs ~38% I measured with the paid tool — real but smaller gap than expected)
  • Chat sessions/day: ~15, mostly "explain this function" and "write the test for this" — 7B models handle these fine
  • Where local still fails: multi-file refactors, obscure framework errors, anything needing 2025+ knowledge the model didn't train on
  • Money saved: ~$60/seat over 3 months, and my code has never touched an external API

The controversial part: for 80% of daily coding assistance, you don't need a frontier model. You need fast, private, and good enough. The last 20% — genuinely hard problems — is where I still open a browser.

I keep the whole stack glued together with MonkeyCode — open-source, free quota, and it talks to the same local Ollama endpoint: https://ly.cyberserval.tech/iIETXiF

Have you tried going fully local? What broke first — the models, or your patience?

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