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

A developer released AI-Git-Bot, an MIT-licensed, self-hosted workflow automation bot that reviews pull requests, generates unit and Playwright tests, triages issues, and syncs documentation using a locally run 7B open-weight coder model via Ollama. The project, published as a multi-arch Docker image and self-pulled over 16,000 times on Docker Hub, is positioned as an alternative to per-seat AI code review SaaS such as Copilot and CodeRabbit, running on a small VPS or existing hardware with no API keys or per-token billing.

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.