Continue AI Coding Tool: Technical Reference

What It Is

• Open source AI coding assistant for VS Code and JetBrains IDEs
• Apache 2.0 licensed with 28k GitHub stars
• Model-agnostic: supports OpenAI, Claude, local models, Azure, Gemini, Bedrock
• Alternative to GitHub Copilot with no vendor lock-in

Configuration Requirements

Initial Setup Time

• Marketing claim: 5 minutes
• Reality: 10 minutes basic installation, 1-3 hours for usable configuration, weekend for full optimization
• Enterprise deployment: 1 hour basic team config, half day for advanced rules

Hardware Requirements

Local Models (Ollama):

• Minimum: 16GB RAM (laptop becomes "jet engine" below this)
• Recommended: 32GB+ RAM for acceptable performance
• Storage: 4-7GB per model download
• Performance: Significantly slower than cloud models

Cloud Models:

• No local hardware requirements
• Fast response times
• Sends code to external services

Cost Structure

• Continue software: Free
• API costs: $20-50/month typical usage with cloud models
• OpenAI: $0.002 per 1K tokens ("sounds cheap until $127 bill")
• Claude: More expensive than OpenAI but "actually thinks"
• Local models: Free after setup, but hardware and time costs

Feature Modes and Success Rates

1. Agent Mode

• Purpose: Multi-step tasks ("implement user authentication")
• Success rate: 70% for simple tasks, 30% for complex tasks
• Failure modes:
  • Hallucinated imports that don't exist
  • Gets stuck in fix loops
  • Context confusion
  • Takes longer to clean up than manual coding when it fails

2. Chat Interface

• Purpose: Code explanation and Q&A
• Success rate: High for context-aware queries
• Advantage: Reads actual project files vs copy-paste to ChatGPT

3. Inline Edit

• Purpose: Highlight code and request changes
• Success rate: 85% for simple edits
• Status: "Feature that actually works"

4. Autocomplete

• Purpose: Tab completion like Copilot
• Performance: Entirely dependent on chosen model
• Trade-off: Local models (slow but private) vs Cloud models (fast but external)

Critical Failure Modes

Common Configuration Errors

• CORS errors: Access to fetch at 'http://localhost:11434' has been blocked by CORS - Ollama CORS misconfigured by default
• Rate limiting: OpenAI free tier "is garbage" - frequent Rate limit exceeded during testing
• Context limits: Context length exceeded for projects larger than hello world
• Timeouts: Request timed out after 30000ms - default 30s timeout insufficient for local models
• Breaking changes: Continue v0.8.x breaks v0.7.x configs with no migration help

Production Considerations

• UI breakdown: System becomes unusable at 1000+ spans, making debugging large distributed transactions impossible
• Model switching: If OpenAI fails, can switch to Claude or local models (unlike Copilot vendor lock-in)
• Team coordination: Continue Hub required for consistent team deployments

Comparative Analysis

vs GitHub Copilot

• Advantage: Model flexibility, open source, no vendor lock-in
• Disadvantage: Complex setup vs 2-minute Copilot installation
• Cost: $0 + API usage vs $10/month fixed
• Enterprise: Better vendor risk management vs Microsoft ecosystem lock-in

vs Cursor

• Advantage: Open source, local model support
• Disadvantage: Agent mode 70% success vs Cursor's 80%
• Setup: 30 minutes-4 hours vs 5 minutes
• Cost: API usage vs $20/month

Security and Compliance

Data Privacy Options

• Local models: Code never leaves machine
• Cloud models: Code sent to OpenAI/Anthropic/chosen provider
• Hybrid approach: Local for sensitive code, cloud for general development
• Audit capability: All interactions logged for compliance

Enterprise Deployment

• Continue Hub: Centralized team configuration without restricting individual customization
• API key management: Team-wide distribution and rotation
• Security team requirements:
  1. Code destination (configurable)
  2. Audit trail (available)
  3. Business continuity (open source = forkable)

MCP Tools Integration

Capabilities

• Create Linear tickets from code comments
• Read GitLab issues for context
• Pull Confluence documentation
• Custom API integrations

Implementation Reality

• Setup time: 30 minutes per tool
• Maintenance: "Probably break in 6 months"
• Value: High for teams using integrated services, skip for solo developers

Model-Specific Performance

Language Support Quality

• Excellent: JavaScript/TypeScript, Python (heavy AI training data)
• Good: Java, C++, Go (model-dependent)
• Decent: Rust, PHP, Ruby (inconsistent)
• Hit-or-miss: Obscure languages

Recommended Model Selection

• Heavy refactoring: GPT-4
• Complex reasoning: Claude
• Private/sensitive code: Local Ollama models
• Testing/validation: GPT-4o-mini (cheap, sufficient quality)

Resource Requirements and Optimization

Time Investment

• Initial setup: 10 minutes to weekend depending on requirements
• Learning curve: Steep for customization, simple for basic use
• Maintenance: Regular config updates, model management

Performance Optimization

• Timeout configuration: Increase from default 30s to 120+ seconds
• GPU acceleration: Required for acceptable local model performance on Mac M1/M2
• Context management: Critical for large codebases

Decision Criteria

Choose Continue If:

• Model flexibility is priority
• Data privacy requirements exist
• Open source/vendor independence valued
• Team uses integrated services (Linear, GitLab, etc.)
• Willing to invest setup time for control

Choose Copilot If:

• Want immediate productivity without configuration
• Microsoft ecosystem integration preferred
• Simple autocomplete requirements sufficient
• Avoid API cost management complexity

Choose Cursor If:

• Need reliable agent mode functionality
• Prefer polished user experience
• Accept vendor lock-in for convenience
• Budget allows $20/month per developer

Essential Resources

• Continue Documentation - "Actually decent docs"
• GitHub Repository - Active development, community support
• Discord Community - 5k members, actual troubleshooting help
• Continue Hub - Team management dashboard
• Troubleshooting Guide - Common issue resolution