# Run AI Code Reviews for the Cost of a $5 VPS — No Per-Seat SaaS Required

> Source: <https://dev.to/tom_seidel_d47bb467039516/run-ai-code-reviews-for-the-cost-of-a-5-vps-no-per-seat-saas-required-54jp>
> Published: 2026-09-10 13:25:25+00:00

A self-hosted AI workflow automation bot that reviews pull requests, generates

tests, and keeps docs in sync — powered by a 7B model you already own, on

hardware you already run.

**TL;DR:** AI code review SaaS pricing scales per developer, per month, and your

source code goes to someone else's cloud. The setup in this post runs the same

class of workflow entirely self-hosted: one Docker container, one Ollama

service, and a ~4.7 GB open-weight coder model. Total incremental cost: a small

VPS or a machine you already have. The project behind this post,

[AI-Git-Bot](https://github.com/tmseidel/ai-git-bot), has been

[self-pulled from Docker Hub over 16,000 times](https://hub.docker.com/r/tmseidel/ai-git-bot)

and is MIT licensed.

By now most of us have the invoice: Copilot at $10–39/seat/month, CodeRabbit and

similar review SaaS at per-PR or per-seat tiers, enterprise tiers on top.

Multiply that by a 15-person team and you're at several thousand dollars a

month — before a single review is useful.

Two things about that model bother me:

You don't have to accept either of those. Local open-weight models have been

genuinely good at code review for over a year now, and the missing piece was

never the model — it was the **workflow glue**: turning "a PR was opened" into

"reviewed, findings posted, tests generated, docs updated, automatically, on

every platform we use."

That's the gap AI-Git-Bot fills.

AI-Git-Bot is a self-hosted bot that lives inside your Git platform — Gitea,

GitHub/GitHub Enterprise, GitLab, or Bitbucket Cloud — and reacts to events you

already emit:

| Workflow | Trigger | Result | 
|---|---|---|
| PR review | PR opened / re-requested | Summary + inline findings on the diff | 
| Interactive Q&A | `@bot` mention in a PR comment | Context-aware answer in-thread | 
| Unit test generation | PR opened | Regression tests committed to the branch | 
| E2E / Full-Stack QA | PR opened | Playwright suite run against a preview, results posted | 
| Issue triage & routing | Issue opened/assigned | One assignee chosen, reason posted | 
| Issue → Pull request | Issue assigned to the coding agent | Implementation PR opened | 
| README / docs sync | PR opened | Docs updated to match code | 
| i18n coverage | PR opened | Missing translations drafted across locale files | 

No browser extension, no Slack bot to babysit, no new process. Developers just

see the bot's comments where they already look.

The whole thing is two containers.

```
services:
  app:
    image: tmseidel/ai-git-bot:latest
    ports:
      - "8080:8080"
    environment:
      SPRING_PROFILES_ACTIVE: docker
      DATABASE_URL: jdbc:postgresql://db:5432/giteabot
      DATABASE_USERNAME: giteabot
      DATABASE_PASSWORD: change-me
      APP_ENCRYPTION_KEY: your-secure-encryption-key-here
    depends_on:
      db:
        condition: service_healthy
    restart: unless-stopped

  db:
    image: postgres:17-alpine
    environment:
      POSTGRES_DB: giteabot
      POSTGRES_USER: giteabot
      POSTGRES_PASSWORD: change-me
    restart: unless-stopped

  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    restart: unless-stopped

  ollama-pull:
    image: ollama/ollama:latest
    entrypoint: ["sh", "-c", "sleep 5 && ollama pull qwen2.5-coder:7b"]
    environment:
      OLLAMA_HOST: http://ollama:11434
    depends_on:
      - ollama

volumes:
  ollama_data:
```

That's it. The image is published as a **multi-arch manifest** (`linux/amd64`

and `linux/arm64`), so the same compose file runs on an x86 VPS, an Apple

Silicon laptop, a Graviton instance, or a 64-bit Raspberry Pi — which matters a

lot if "low budget" includes "the hardware is a Pi in a closet."

Then point it at your model. In the web UI: **AI Integrations → New
Integration → provider: `ollama` → API URL: `http://ollama:11434` → model:
`qwen2.5-coder:7b`**. No API keys. No per-token billing. Nothing to export.

The project ships a ready-to-use compose for exactly this (`systemtest/`), and

the docs are direct about what local models can and can't do:

| Workload | 7B class | 14–32B class | 
|---|---|---|
| **PR reviews** (natural-language output) | ✅ works well | ✅ | 
| **Issue-based agents** (require strict JSON) | ❌ unreliable | ⚠️ 32B+ is the sweet spot | 

So the honest, low-budget recipe is:

`qwen2.5-coder:7b` (~4.7 GB)
or `codellama:7b` run comfortably in 8 GB of RAM, no GPU required, and
reviews come back in a reasonable time on a small VPS.`qwen2.5-coder:32b` or `deepseek-coder:33b` on a bigger box — or just use a
cloud provider for that one workflow. The bot mixes providers per-bot, so
you can run reviews on Ollama and agent work on Claude if you want.
That's the whole "low budget" pitch: **you pay for the workflow, not per seat, and the marginal cost of adding a developer is zero.**

`APP_ENCRYPTION_KEY` gives you AES-256-GCM at rest for secrets.

```
docker run -p 8080:8080 tmseidel/ai-git-bot:latest
```

Open `http://localhost:8080`, create your admin account, wire one AI

integration (Ollama works), one Git integration, one bot, and the webhook.

That's the whole setup — it's the path I use for the demo videos in the

[README](https://github.com/tmseidel/ai-git-bot).

If you get it running on a budget box (especially a Pi or an arm64 VPS), drop

a comment or open an issue — real-world hardware reports are the best way to

keep the docs honest.