VSCode AI Coding Plugins Compared: GitHub Copilot vs Continue.dev vs Kilo Code

AI-powered coding assistants have become essential tools for modern developers. In this post, I’ll compare three popular VSCode extensions: GitHub Copilot, Continue.dev, and Kilo Code (kilo.io), helping you choose the right one for your workflow.

GitHub Copilot

GitHub Copilot is the original AI pair programmer, powered by OpenAI’s Codex and GPT models.

Features

• Inline code suggestions: Real-time code completions as you type
• Chat interface: Ask questions about your codebase
• Copilot Edits: Multi-file editing with natural language instructions
• Context awareness: Understands your current file and project structure
• Multiple model support: GPT-4, Claude, and custom models (depending on plan)

Pricing

Pros

• ✅ Mature and stable product
• ✅ Excellent code completion quality
• ✅ Deep integration with GitHub ecosystem
• ✅ Strong enterprise features and security
• ✅ Works offline for basic completions (recent feature)
• ✅ Supports 20+ programming languages
• ✅ Fast response times

Cons

• ❌ Closed-source models
• ❌ Limited customization options
• ❌ Can be expensive for teams
• ❌ Code may be sent to Microsoft servers (privacy concerns)
• ❌ No local model support
• ❌ Limited context window (varies by model)

Best For

Teams already in the GitHub ecosystem, enterprises needing security guarantees, and developers who want a polished, out-of-the-box experience.

Installation

# In VS Code
# 1. Open Extensions (Cmd+Shift+X)
# 2. Search for "GitHub Copilot"
# 3. Click Install
# 4. Sign in with GitHub account

# Or via CLI
code --install-extension GitHub.copilot
code --install-extension GitHub.copilot-chat

Configuration

// settings.json
{
  "github.copilot.enable": {
    "*": true,
    "plaintext": false,
    "markdown": false
  },
  "github.copilot.editor.enableAutoCompletions": true,
  "github.copilot.chat.codeGeneration.style": "balanced"
}

Continue.dev

Continue.dev is an open-source AI code assistant that gives you complete control over your AI models and configuration.

Features

• Open Source: Full transparency and customization
• Model Agnostic: Use any LLM (Claude, GPT, Llama, local models)
• Custom Context: Define what context the AI sees
• Slash Commands: Custom commands for common tasks
• Tab Autocomplete: Intelligent code completion
• Chat & Edit: Conversational interface with code editing
• Local Models: Run models locally with Ollama, LM Studio

Pricing

Note: With the free version, you pay directly for API usage (OpenAI, Anthropic, etc.)

Pros

• ✅ Completely open-source
• ✅ Works with any LLM provider
• ✅ Supports local models (Ollama, LM Studio)
• ✅ Highly customizable
• ✅ No vendor lock-in
• ✅ Active community
• ✅ Privacy-focused (local models)

Cons

• ❌ Requires more setup and configuration
• ❌ Quality depends on chosen model
• ❌ Less polished than Copilot
• ❌ Smaller team behind the project
• ❌ Documentation can be sparse

Best For

Developers who want control over their AI stack, privacy-conscious teams, and those who prefer open-source solutions.

Installation

# In VS Code
# 1. Open Extensions (Cmd+Shift+X)
# 2. Search for "Continue"
# 3. Click Install

# Or via CLI
code --install-extension continue.continue

Configuration

Continue uses config.ts for configuration:

// .continue/config.ts
import { defineConfig } from "@continue/config";

export default defineConfig({
  models: [
    {
      title: "Claude 3.5 Sonnet",
      provider: "anthropic",
      model: "claude-3-5-sonnet-20241022",
      apiKey: process.env.ANTHROPIC_API_KEY,
    },
    {
      title: "Ollama - Llama 3",
      provider: "ollama",
      model: "llama3:8b",
      apiBase: "http://localhost:11434",
    },
    {
      title: "GPT-4 Turbo",
      provider: "openai",
      model: "gpt-4-turbo-preview",
      apiKey: process.env.OPENAI_API_KEY,
    },
  ],
  
  tabAutocompleteModel: {
    title: "Ollama - StarCoder",
    provider: "ollama",
    model: "starcoder2:3b",
    apiBase: "http://localhost:11434",
  },
  
  context: [
    {
      name: "docs",
      provider: "docs",
      params: {
        docs: [
          "https://react.dev",
          "https://nodejs.org/docs",
        ],
      },
    },
    {
      name: "codebase",
      provider: "codebase",
    },
  ],
  
  slashCommands: [
    {
      name: "test",
      description: "Generate unit tests",
    },
    {
      name: "review",
      description: "Review code for issues",
    },
    {
      name: "refactor",
      description: "Refactor selected code",
    },
  ],
});

Example Usage

// In chat: @codebase Explain the authentication flow

// Slash command: /test src/auth/login.ts

// Custom command: "Refactor this function to use async/await"

Kilo Code

Kilo Code (from kiro.dev) is an agentic AI platform that goes beyond code completion to manage entire development workflows.

Features

• Agentic AI: Autonomous task execution with planning
• Multiple Modes: Orchestrator, Architect, Code, Debug
• Spec-Driven Development: Structured approach to feature building
• Codebase Indexing: Semantic search with Qdrant
• Steering Rules: Persistent custom instructions
• Custom Agents: Specialized AI personas
• MCP Integration: Connect to external tools (GitHub, databases)
• Skills System: Modular expertise packages
• Parallel Agents: Multiple agents working simultaneously
• Git Worktree Integration: Isolated development environments

Pricing

Pros

• ✅ Full agentic capabilities (not just completion)
• ✅ Spec-driven development workflow
• ✅ Highly customizable with steering rules
• ✅ Local model support (Ollama, Qwen)
• ✅ Parallel agent execution
• ✅ MCP integration for external tools
• ✅ Skills system for specialized tasks
• ✅ Git worktree integration
• ✅ Open architecture

Cons

• ❌ Steeper learning curve
• ❌ More complex setup
• ❌ Newer product (less mature)
• ❌ Smaller community
• ❌ Documentation still growing

Best For

Teams building complex features, developers who want AI to handle entire workflows, and those who value customization and control.

Installation

# VS Code Extension
code --install-extension kilo.kilo-code

# Or standalone IDE
# Download from https://kiro.dev

# CLI installation
npm install -g kiro
# or
bun install -g kiro

Configuration

// .kilocode/config.json
{
  "provider": "qwen-code",
  "model": "qwen-coder-plus",
  "mode": "orchestrator",
  "indexing": {
    "enabled": true,
    "provider": "qdrant",
    "embedding": {
      "model": "nomic-embed-text",
      "provider": "ollama"
    }
  },
  "steering": {
    "rules": ["coding-standards", "security", "testing"]
  },
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

Example Usage

// Spec-driven development
/spec "Build a user authentication system with JWT"

// Mode switching
/mode architect "Design the database schema"
/mode code "Implement the login endpoint"
/mode debug "Fix the failing tests"

// Custom agent
/agent security-reviewer "Audit the auth code"

// Parallel agents
kiro agent-manager start "Build full-stack feature"

Feature Comparison Table

Performance Comparison

Code Completion Speed

Code Quality (Benchmarks)

Based on HumanEval and internal testing:

Context Window

Use Case Recommendations

Choose GitHub Copilot If:

• ✅ You want the most polished experience
• ✅ You’re already using GitHub Enterprise
• ✅ You need enterprise security features
• ✅ You prefer minimal configuration
• ✅ Budget is not a primary concern

Example Scenario:

Enterprise team at a Fortune 500 company:
- Need IP indemnity
- Require audit logs
- Want seamless GitHub integration
- Budget approved for premium tools

Choose Continue.dev If:

• ✅ You want open-source software
• ✅ You need to use local models
• ✅ You want to switch between multiple LLMs
• ✅ Privacy is a top concern
• ✅ You’re comfortable with configuration

Example Scenario:

Privacy-focused startup:
- Can't send code to external APIs
- Want to run models locally
- Need flexibility to switch providers
- Prefer open-source stack

Choose Kilo Code If:

• ✅ You’re building complex features
• ✅ You want AI to manage entire workflows
• ✅ You need parallel agent execution
• ✅ You value spec-driven development
• ✅ You want deep customization

Example Scenario:

Development team building a new product:
- Need to build full-stack features
- Want AI to handle planning + execution
- Need to coordinate multiple agents
- Value structured development approach

Real-World Examples

Task: Build a REST API Endpoint

GitHub Copilot:

Developer types: // Create a GET endpoint for /api/users/:id
Copilot suggests the route handler code
Developer reviews and accepts
Developer types: // Add error handling
Copilot suggests try-catch blocks

Continue.dev:

Developer: @codebase Create a GET endpoint for /api/users/:id
Continue generates the complete handler
Developer: Add error handling and validation
Continue updates the code

Kilo Code:

Developer: /spec "Build user API with CRUD endpoints"
Kilo creates specification
Developer: Approve spec
Kilo creates tasks, implements, tests
Developer reviews final result

Task: Debug a Production Issue

GitHub Copilot:

Developer pastes error message
Copilot suggests possible causes
Developer asks follow-up questions
Copilot suggests fixes

Continue.dev:

Developer: @logs Analyze this error stack
Continue explains the issue
Developer: @codebase Where could this originate?
Continue searches codebase

Kilo Code:

Developer: /mode debug "Fix the 500 error on /checkout"
Debug mode analyzes logs
Identifies root cause
Proposes and applies fix
Runs tests to verify

Migration Guide

From GitHub Copilot to Continue.dev

// 1. Install Continue.dev
code --install-extension continue.continue

// 2. Configure similar experience
// .continue/config.ts
{
  models: [{
    provider: "openai",
    model: "gpt-4-turbo-preview",
  }],
  tabAutocompleteModel: {
    provider: "openai",
    model: "gpt-4-turbo-preview",
  }
}

// 3. Uninstall Copilot
code --uninstall-extension GitHub.copilot

From GitHub Copilot to Kilo Code

// 1. Install Kilo Code
code --install-extension kilo.kilo-code

// 2. Configure provider
// .kilocode/config.json
{
  "provider": "openai",
  "model": "gpt-4-turbo-preview"
}

// 3. Learn Kilo modes
/mode code  // Similar to Copilot chat

From Continue.dev to Kilo Code

// 1. Export Continue config
// Review .continue/config.ts

// 2. Install Kilo Code
code --install-extension kilo.kilo-code

// 3. Migrate models
// .kilocode/config.json
{
  "provider": "anthropic",
  "model": "claude-3-5-sonnet-20241022"
}

// 4. Explore Kilo features
/spec "Try spec-driven development"

Cost Analysis

Annual Cost for Team of 10

Note: Continue.dev API costs vary based on usage. Estimate ~$50-200/month for active team.

Individual Developer Annual Cost

Security Considerations

GitHub Copilot

• ✅ SOC 2 Type II certified
• ✅ Data encrypted in transit and at rest
• ✅ Enterprise: Code not used for training
• ⚠️ Code sent to Microsoft servers
• ⚠️ Limited control over data handling

Continue.dev

• ✅ Open-source (auditable)
• ✅ Local models keep data on-device
• ✅ You control API endpoints
• ⚠️ Cloud models depend on provider
• ⚠️ Configuration security is your responsibility

Kilo Code

• ✅ Local model support
• ✅ Configurable data handling
• ✅ Enterprise: Self-hosted option
• ✅ MCP servers can be audited
• ⚠️ Newer product (less security track record)

Conclusion

Each tool excels in different scenarios:

My Recommendation:

• Start with Kilo Code’s free tier if you’re building complex features and want to explore agentic AI
• Choose GitHub Copilot if you want the most polished, enterprise-ready solution
• Choose Continue.dev if you value open-source and want maximum flexibility

The best approach? Try all three! Each offers a free tier or trial.