title: “Self-Hosted AI Coding Workflow: The Complete $6/Month St… description: “Technical guide and comparison.” date: 2026-05-21T00:00:00+08:00 lastmod: 2026-05-21T00:00:00+08:00 tech_stack: - Docker

Python
TypeScript
PostgreSQL application_domain: Collections source_version: ’' licensing_model: Open Source license_type: MIT file_size: ’' file_md5: ’' download_url: ’' backup_url: ’' last_maintained: “2026-05-21” draft: false categories: [“collections”] tags: [“self-hosted”, “ai coding”, “stack”, “workflow”, “collection”] aliases:
/posts/self-hosted-ai-coding-workflow/-

If you’ve been paying $20/mo for Cursor + $80/mo for Claude Code Pro + $19/mo for Copilot + $50/mo for Replit credits + $120/mo for OpenAI API top-ups, you’re at $289/month of AI coding spend. After 12 months that’s $3,468 — for tools you don’t own, can’t audit, and can have rate-limited or shut off without notice.

This collection assembles the 7-component self-hosted alternative that runs on a $6/month VPS and matches 90%+ of the SaaS feature set. We’ve published a deep dive on each component over the past 90 days. This page is the complete stack assembly — what to install, in what order, with which configs, plus the upgrade path when you outgrow the $6 tier.

TL;DR — The Stack at a Glance

Total monthly cost (4GB VPS + storage): $6 at the entry tier. Up to ~$30/month at the “team of 5” tier with PostgreSQL + Redis + replicated LiteLLM proxy.

1. Why This Stack Exists in 2026

Three things changed between 2024 and 2026 that made self-hosted AI coding finally viable: 1. Open-weight models caught up: DeepSeek-V4, Qwen 3 Coder, GLM-4.6 Coder are within 5% of Claude/GPT-5 on coding benchmarks while being free or near-free 2. MCP standardized tool integration: instead of every editor reinventing how to call file/git/search tools, the protocol is now a USB-C port — see MCP Server Registry guide for the 19,700+ servers now available 3. Local LLM runners are production-grade: Ollama, vLLM, and llama.cpp run at acceptable speed on consumer hardware

The math worked in 2024 too, but the experience was painful. In 2026, the gap closed.

2. Architecture Overview

                  ┌─────────────────────────────┐
                  │   Your machine / VPS ($6)   │
                  │                             │
   You type  →    │  OpenCode (editor/agent)    │
                  │           │                 │
                  │           ▼                 │
                  │  CC Switch (1-click CLI)    │
                  │           │                 │
                  │           ▼                 │
                  │  LiteLLM Gateway :4000      │
                  │           │                 │
                  │     ┌─────┴─────┐           │
                  │     │           │           │
                  │     ▼           ▼           │
                  │  9Router    Direct          │
                  │  (RTK comp) (premium)       │
                  │     │           │           │
                  └─────┼───────────┼───────────┘
                        │           │
              ┌─────────┴───┐  ┌────┴──────┐
              ▼             ▼  ▼           ▼
          Ollama       DeepSeek API   Claude API
          (local)      (free coding)  (premium)

      MCP servers (filesystem + git + memory + tavily-search)
      mounted on OpenCode via claude_desktop_config.json

The pattern: OpenCode is the editor brain, LiteLLM is the traffic cop, 9Router compresses, MCP servers expose the world. Swap any component without touching the others.

3. Component 1 — OpenCode (Editor / Agent)

The role: This is what you actually type into. Replaces Cursor + Claude Code Pro.

Why this pick: Open-source agent that speaks MCP natively. On the same refactor task (400-line React component), OpenCode + DeepSeek-V4 = 18 sec, $0.007. Claude Code (Sonnet) = 12 sec, $0.14. Twenty-times cheaper, 5% slower.

Quick install: `````bash npm install -g @opencode-ai/opencode opencode –version # 1.x


Point it at your LiteLLM gateway (next component) via config and you're done.

**Full setup** including provider routing rules, MCP server connection, and editor integration: see our [OpenCode complete guide](/resources/llm-frameworks/opencode-open-source-claude-code-alternative-2026/).

## 4. Component 2 — Ollama (Local LLM Runner)

**The role**: Run smaller models entirely on your own hardware. 100% offline option for sensitive code.

**Why this pick**: 137k stars. Single-binary install. Llama 3.2 3B runs at 22 tok/sec on a 5-year-old M1 MacBook with 8GB RAM. Qwen 3 Coder 14B runs comfortably on a 16GB M-series Mac or any 32GB Linux box.

**Quick install**: `````bash
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen3-coder:14b
ollama serve  # exposes :11434 OpenAI-compatible API

LiteLLM picks up Ollama as a provider automatically.

Full setup with model selection by hardware tier and the quantization tradeoffs that matter: see our Ollama production guide.

5. Component 3 — LiteLLM (Unified Gateway)

The role: One OpenAI-compatible API in front of every model — Ollama (local), DeepSeek (cheap), Claude (premium), Gemini (free tier). Your editor only talks to LiteLLM; LiteLLM routes based on your config.

Why this pick: 47.8k stars, the most-starred LLM gateway. 8ms P95 latency at 1k RPS. Free if self-hosted. Compared in detail in our Portkey vs LiteLLM vs OpenRouter 2026 guide.

Quick deploy on a 4GB VPS (we recommend for sub-30ms latency to mainland China users, or for everywhere else): `````bash docker run -d –name litellm -p 4000:4000
-e LITELLM_MASTER_KEY=sk-your-secret
-e OLLAMA_API_BASE=http://host.docker.internal:11434
-e DEEPSEEK_API_KEY=$DEEPSEEK_KEY
-e ANTHROPIC_API_KEY=$CLAUDE_KEY
ghcr.io/berriai/litellm:main-stable


**Full setup** with virtual keys, spend tracking, fallback rules: see our [LiteLLM production gateway 2026](/resources/llm-frameworks/litellm/).

## 6. Component 4 — 9Router (Token-Saving Proxy)

**The role**: Sit between LiteLLM and your "premium" providers (Claude / GPT-5). Compress repeated content (file headers, system prompts, codebase context) before sending. **Cuts billable tokens 20-40% for coding agents.**

**Why this matters**: Coding agents are pathological token consumers — they send the entire codebase context every turn. At $3/M input tokens on Claude Sonnet, this adds up fast. 9Router's RTK (Repetition-Token Compression) is the only proxy designed specifically for this workload.

**Quick install**: `````bash
docker run -d --name 9router -p 9999:9999 \
  -e PROVIDERS=anthropic,openai,gemini,deepseek \
  ghcr.io/rtk-ai/9router:latest

Point LiteLLM’s premium provider endpoints at localhost:9999 instead of direct.

Full setup: 9Router smart proxy guide.

7. Component 5 — Memory Layer (mem0 + AgentMemory MCP)

The role: Persistent semantic memory across coding sessions. “Remember that we use Tailwind v4 and the auth lives in src/lib/auth.ts” — and the agent actually remembers next Monday.

Why this pick: mem0 is the open-source semantic memory layer with 30k+ stars. AgentMemory is the MCP server that exposes it to any MCP host (OpenCode / Claude Desktop / Cursor).

Quick install: `````bash npm install -g @mem0/mem0-mcp

Add to OpenCode’s MCP config: # { “agentmemory”: { “command”: “mem0-mcp”, “args”: [] } }


**Full setup** with the embedding model picks and vector DB choices: see our [AgentMemory MCP guide](/resources/llm-frameworks/agentmemory-mcp-persistent-memory-2026/).

## 8. Component 6 — Code Context MCP (filesystem + git + tavily-search)

**The role**: Give the agent eyes and hands. Read your project files, inspect your git history, search the web — all via the MCP protocol.

**The minimum set**: - ````modelcontextprotocol/server-filesystem```` (Anthropic reference)
- ````modelcontextprotocol/server-git```` (Anthropic reference)
- ````tavily-mcp```` (LLM-formatted web search results)

**Quick install** (all 3 added to OpenCode's ``claude_desktop_config.json``): `````json
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user/projects"]
    },
    "git": {
      "command": "uvx",
      "args": ["mcp-server-git", "--repository", "/home/user/projects/your-repo"]
    },
    "tavily": {
      "command": "npx",
      "args": ["-y", "@tavily/mcp"],
      "env": {"TAVILY_API_KEY": "tvly-xxx"}
    }
  }
}

Tavily has a generous free tier (1,000 searches/mo) so this stays within the $6 budget.

Full menu of 19,700+ available MCP servers and how to pick more: see our MCP Server Registry comprehensive guide 2026.

9. Component 7 — CC Switch (Multi-CLI Orchestrator)

The role: When you want to use Claude Code natively (not via OpenCode) for a specific task — or jump to Codex for Rust speed — CC Switch is the 1-click swap. Single config, all your AI CLIs share MCP servers.

Why this pick: A 75k-star Rust + Tauri desktop app that means you stop maintaining 5 separate ~/.claude_desktop_config.json files for 5 different CLIs.

Quick install: Download from farion1231/cc-switch releases. Configure each CLI with one click.

Full guide: CC Switch unified AI CLI control center.

10. Assembly Order — Day 1 Setup (90 minutes)

If you’re starting from scratch, do it in this order: 1. Spin up infrastructure (15 min) — Order a , install Docker, open ports 4000 (LiteLLM) + 9999 (9Router) + 11434 (Ollama) 2. Ollama first (10 min) — Install + pull qwen3-coder:14b (~9 GB). Confirm curl localhost:11434/api/tags works 3. LiteLLM second (15 min) — Docker run with the env vars from sec. 5. Confirm curl localhost:4000/v1/models -H "Authorization: Bearer sk-your-secret" lists Ollama models 4. 9Router third (10 min) — Optional but recommended. Add to LiteLLM’s premium provider config 5. OpenCode fourth (15 min) — Install locally, point at LiteLLM at https://your-vps:4000/v1, test a basic prompt 6. MCP servers fifth (15 min) — filesystem + git + tavily added to OpenCode config. Test by asking “list the files in this repo” 7. mem0 + AgentMemory sixth (10 min) — ````npm i -g mem0-mcp```, add to config, test by saying “remember we use Tailwind v4” 8. CC Switch last (optional) — Only if you want native Claude Code / Codex side-by-side

You now have a $6/month AI coding stack matching 90% of the $289/month SaaS bundle.

11. Monthly Cost Breakdown

| | VPS (4 GB → 16 GB) | $6 | $24 | | Ollama models | $0 (you own the disk) | $0 | | LiteLLM | $0 (self-hosted) | $0 | | 9Router | $0 (self-hosted) | $0 | | Tavily search | $0 (1k req free tier) | $5 (paid tier) | | mem0 / AgentMemory | $0 (self-hosted) | $0 | | DeepSeek API (cheap inference for hard tasks) | ~$2-5 | ~$10-15 | | Claude API (rare, premium fallback) | $1-3 | $5-10 | | Total | **$6-14** | **$30-50** |

Compare against $289/month for Cursor + Claude Code Pro + Copilot + Replit + OpenAI top-ups.

12. Upgrade Path

When your stack outgrows the $6 tier (more than 1 dev, more than 1 project, persistent state matters): - Add Postgres for LiteLLM spend tracking + virtual keys per project ( $15/mo)

Add Redis for LiteLLM caching (1 GB managed Redis $10/mo)
Move LiteLLM behind a load balancer with 3 replicas — see Portkey vs LiteLLM 2026 guide sec. 4 for the Kubernetes pattern
Add Grafana + Loki for full observability — log every prompt, every fallback, every cost spike
Add team-level RBAC via LiteLLM enterprise tier (custom pricing)

The point: you started with $6/mo and own the entire stack. Every upgrade is a deliberate cost decision tied to a specific need — not a SaaS lock-in trap.

TL;DR — The 7-Component Recipe

OpenCode — the editor brain
Ollama — local fallback
LiteLLM — unified gateway
9Router — token compression
mem0 + AgentMemory — persistent memory
MCP servers (filesystem + git + tavily) — context + tools
CC Switch — multi-CLI orchestration

Total: $6/month. Total: 90 minutes to assemble. Total: zero vendor lock-in.

If you"re spending $200+/mo on AI coding SaaS, this stack pays for itself in week 1. Spin up a , follow sec. 10, and report back next week.

Bookmark this page — we update component picks quarterly as new open-source releases land. Last updated: 2026-05-21.

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Frequently Asked Questions (FAQ)

问：AI Agent和传统自动化有什么区别？

AI Agent具有自主决策能力，能够根据环境变化调整策略，而传统自动化只能执行预设规则。

问：如何选择合适的AI Agent框架？

考虑因素包括：部署难度、社区活跃度、扩展性、成本。Claude Code适合开发者，AutoGen适合复杂多智能体场景。

问：AI Agent的安全性如何保证？

实施权限最小化、输入验证、审计日志、以及定期安全评估。

问：AI Agent的学习成本有多高？

入门级使用3-5天，高级配置需要2-4周，取决于团队技术基础。

问：能否自定义AI Agent的行为？

是的，通过提示工程、工具定义、记忆系统、以及行为约束来定制。

TL;DR — The Stack at a Glance#

1. Why This Stack Exists in 2026#

2. Architecture Overview#

3. Component 1 — OpenCode (Editor / Agent)#

5. Component 3 — LiteLLM (Unified Gateway)#

7. Component 5 — Memory Layer (mem0 + AgentMemory MCP)#

Add to OpenCode’s MCP config: # { “agentmemory”: { “command”: “mem0-mcp”, “args”: [] } }#

9. Component 7 — CC Switch (Multi-CLI Orchestrator)#

10. Assembly Order — Day 1 Setup (90 minutes)#

11. Monthly Cost Breakdown#

12. Upgrade Path#

TL;DR — The 7-Component Recipe#

Related Articles#

Frequently Asked Questions (FAQ)#