Building Your Own Private 'GitHub Copilot' with VPS and Continue.dev: Absolute Source Code Security for Enterprises

Introduction: The Hidden Cost of AI Productivity

AI-powered coding assistants like GitHub Copilot, Tabnine, and OpenAI's ChatGPT have revolutionized software development. They accelerate coding speeds, debug complex functions in seconds, and automate repetitive boilerplate tasks. However, for enterprises, financial institutions, and proprietary software vendors, this productivity boost comes with a massive caveat: data privacy risks.

When developers use commercial AI extensions, snippets of code—sometimes containing proprietary algorithms, API keys, or regulatory-bound logic—are transmitted to external servers. Even with business tiers promising no data retention, many organizations find the compliance risk unacceptable. Fortunately, you do not have to choose between AI innovation and total data sovereignty. By leveraging a private Virtual Private Server (VPS) and the open-source Continue.dev IDE extension, you can deploy your own private GitHub Copilot alternative. This guide walks you through the comprehensive technical architecture to achieve absolute source code security.

The Architecture of a Private AI Coding Assistant

To build a self-hosted alternative that matches the seamless experience of commercial tools, we need three core components working in harmony:

• The Infrastructure (VPS): A dedicated cloud server with adequate hardware (ideally GPU-accelerated, though CPU-only options exist for lightweight models) acting as your private AI backend.
• The Inference Engine (Ollama/vLLM): The software running on your server that hosts and executes open-source Large Language Models (LLMs) locally.
• The Interface (Continue.dev): An open-source IDE extension for VS Code and JetBrains that connects your editor directly to your private backend, replacing the GitHub Copilot client.

Key Advantage: In this setup, every single keystroke, code completion request, and chat prompt stays entirely within your managed infrastructure. Zero data leaks to external third parties.

Step 1: Selecting and Preparing Your VPS Infrastructure

The performance of your private AI assistant depends heavily on your hosting environment. LLMs require significant computational resources, primarily memory (VRAM for GPUs or RAM for CPUs).

Hardware Recommendations

Depending on your team size and budget, choose one of the following setups:

1. The Budget/Testing Setup (CPU-only): A standard VPS with at least 8 Cores CPU and 16GB RAM. This is suitable for smaller models like Llama-3-8B or Qwen2.5-Coder-7B running at a slower token-per-second rate.
2. The Enterprise Setup (GPU-accelerated): A GPU cloud provider (such as RunPod, Lambda Labs, or specialized AWS/GCP instances) featuring an NVIDIA RTX 3090/4090, A10G, or A100 GPU. This delivers instantaneous code completions indistinguishable from commercial alternatives.

Once your Ubuntu 22.04 or 24.04 LTS VPS is initialized, update the system packages and install the foundational dependencies via your terminal:

sudo apt update && sudo apt upgrade -y
sudo apt install curl git docker.io docker-compose -y

Step 2: Deploying the AI Inference Engine (Ollama)

Ollama is an exceptionally efficient framework for running open-source LLMs locally. It packages model weights, configurations, and dependencies into a clean CLI tool.

Installation

Run the official installation script on your VPS:

curl -fsSL [https://ollama.com/install.sh](https://ollama.com/install.sh) | sh

If you are utilizing a GPU-enabled VPS, ensure the NVIDIA Container Toolkit is installed so Ollama can leverage hardware acceleration.

Downloading Specialized Coding Models

For an optimal development experience, we will pull two distinct models: one optimized for interactive chat/refactoring, and a highly specialized model optimized for fast, multi-line tab completions.

# Pull the chat and reasoning model
ollama run qwen2.5-coder:7b

# Pull the lightning-fast code completion model
ollama run deepseek-coder:1.3b-base

Qwen2.5-Coder (7B or 32B) currently leads the industry benchmarks for open-source coding intelligence, matching or exceeding GPT-4o in specific software engineering metrics.

Step 3: Securing Your Backend and Exposing the API

By default, Ollama binds to localhost:11434. Because your IDE will communicate with the VPS over the internet, you must expose this endpoint securely. Never leave an unauthenticated LLM port open to the public web.

Configuring Reverse Proxy with Nginx and Let's Encrypt

Install Nginx to handle incoming traffic and route it safely via SSL:

sudo apt install nginx -y

Create a secure configuration block allowing only authorized access, or enforce strict API token validation using an API gateway. For maximum corporate security, it is highly recommended to restrict access via a WireGuard VPN or Tailscale overlay network instead of exposing ports to the public internet. If you use Tailscale, your VPS receives a secure internal IP address accessible only to your authenticated devices, eliminating the need for public firewall exceptions.

Step 4: Configuring Continue.dev in Your IDE

With your private server safely broadcasting its secure API, it is time to connect your workspace. Open Visual Studio Code or your preferred JetBrains IDE.

1. Navigate to the Extensions Marketplace and search for Continue. Install the official extension by Continue Dev, Inc.
2. Once installed, click the Continue icon in your sidebar to open its interface.
3. Click the gear icon at the bottom right of the Continue panel to open your global configuration file: config.json.

Replace the contents of your config.json file with the following structured layout, swapping out the placeholder URL with your actual secure VPS or Tailscale endpoint:

{
  "models": [
    {
      "title": "Qwen2.5 Coder 7B (Private)",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "apiBase": "https://your-private-vps-ip-or-domain:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "DeepSeek Coder 1.3B (Private)",
    "provider": "ollama",
    "model": "deepseek-coder:1.3b-base",
    "apiBase": "https://your-private-vps-ip-or-domain:11434"
  },
  "customCommands": [
    {
      "name": "test",
      "prompt": "Write comprehensive unit tests for this selected code using standard enterprise frameworks.",
      "description": "Generate unit tests"
    }
2  ],
  "contextProviders": [
    { "name": "code", "options": {} },
    { "name": "docs", "options": {} }
  ]
}

Save the file. Continue.dev will instantly re-initialize and establish a secure handshake with your private VPS backend.

Step 5: Testing the Workflow

You can now test the fully private ecosystem. Open any proprietary codebase and try the following two core behaviors:

1. Inline Code Tab-Completion

Begin typing a complex function header, such as public function validateJwtToken($token) {. Within milliseconds, the DeepSeek-Coder-1.3B model hosted on your server will generate gray ghost text outlining the suggested implementation. Press Tab to accept it.

2. Context-Aware Code Chat

Highlight a segment of your code, hit Cmd+L (or Ctrl+L on Windows), and ask your chat window to optimize the execution or refactor the algorithmic complexity. By typing @code, you can feed entire structural context directories into your private Qwen2.5-Coder instance safely.

ROI Analysis: Commercial AI vs. Self-Hosted VPS

While absolute security is the primary driver for a private AI server, the economics are equally compelling for expanding engineering teams. Let us look at a financial comparison:

Metric	GitHub Copilot Business	Self-Hosted VPS (Private Setup)
Cost Structure	$19 - $39 per user / month	Fixed VPS cost ($40 - $120/month flat)
Data Privacy	Third-party telemetry & storage risk	Absolute data sovereignty (100% Private)
Customization	Standard default models	Ability to fine-tune models on internal code
Scale Efficiency	Costs scale linearly with team size	Cost remains flat as more developers connect

For a development department of 20 engineers, a commercial solution can exceed $7,800 annually. A single high-performance GPU VPS hosting a shared private endpoint can serve that same team for a fraction of the cost, achieving immediate positive ROI alongside flawless security compliance.

Conclusion

Protecting intellectual property no longer requires sacrificing developer velocity. By engineering a private AI pipeline using a secure VPS, Ollama, and Continue.dev, your organization can foster cutting-edge software development workflows inside a completely locked-down environment. Your code never leaves your server, your parameters remain completely isolated, and your developers stay empowered.