What is Continue.dev?
Continue.dev is open-source AI code assistant with support for local and cloud LLMs offering flexible alternative to Copilot. Continue enables customizable AI coding assistance.
This AI developer tools and ecosystem term is currently being developed. Detailed content covering features, use cases, integration approaches, and selection criteria will be added soon. For immediate guidance on AI tooling strategy, contact Pertama Partners for advisory services.
Continue.dev's open-source model eliminates per-seat licensing costs of USD 19-39 per developer monthly that proprietary alternatives charge, saving teams of 10-50 developers USD 2K-23K annually. The platform's model-agnostic architecture prevents vendor lock-in by allowing seamless switching between LLM providers as pricing, capabilities, and privacy requirements evolve. For mid-market companies with strict data governance requirements, Continue's local model support ensures proprietary source code is never transmitted to external APIs, addressing the primary security concern blocking AI coding tool adoption. Development teams using Continue report 20-30% productivity improvements on routine coding tasks while maintaining full control over which AI models access their codebase.
- Open source AI code assistant.
- Works with any LLM (OpenAI, local, etc).
- VS Code and JetBrains support.
- Customizable prompts and behavior.
- Free to use.
- Good for teams wanting control.
- Deploy Continue.dev with locally hosted models for codebases containing proprietary algorithms, ensuring source code never leaves your infrastructure during AI-assisted development.
- Configure Continue to work with your preferred LLM backend since it supports OpenAI, Anthropic, local Ollama, and over 20 other model providers through a unified interface.
- Customize the system prompt and context providers to include your company's coding standards, reducing code review rejection rates by 25-35% on AI-generated suggestions.
- Evaluate Continue against GitHub Copilot by measuring accepted suggestion rates across your actual codebase rather than relying on generic benchmark comparisons.
- Deploy Continue.dev with locally hosted models for codebases containing proprietary algorithms, ensuring source code never leaves your infrastructure during AI-assisted development.
- Configure Continue to work with your preferred LLM backend since it supports OpenAI, Anthropic, local Ollama, and over 20 other model providers through a unified interface.
- Customize the system prompt and context providers to include your company's coding standards, reducing code review rejection rates by 25-35% on AI-generated suggestions.
- Evaluate Continue against GitHub Copilot by measuring accepted suggestion rates across your actual codebase rather than relying on generic benchmark comparisons.
Common Questions
Which tools are essential for AI development?
Core stack: Model hub (Hugging Face), framework (LangChain/LlamaIndex), experiment tracking (Weights & Biases/MLflow), deployment platform (depends on scale). Start simple and add tools as complexity grows.
Should we use frameworks or build custom?
Use frameworks (LangChain, LlamaIndex) for standard patterns (RAG, agents) to move faster. Build custom for novel architectures or when framework overhead outweighs benefits. Most production systems combine both.
More Questions
Consider scale, latency requirements, and team expertise. Modal/Replicate for simplicity, RunPod/Vast for cost, AWS/GCP for enterprise. Start with managed platforms, migrate to infrastructure-as-code as needs grow.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
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Need help implementing Continue.dev?
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