Continue

Pros

• +Completely free and open-source with no vendor lock-in
• +Works with any model provider including local models
• +Highly customizable through configuration files
• +Active open-source community with transparent development
• +Supports both VS Code and JetBrains IDEs

Cons

• −Requires setup and configuration to work well
• −No built-in model hosting means you manage API keys yourself
• −Less polished experience than commercial alternatives
• −Autocomplete quality depends entirely on the model you choose

Continue is an open-source AI code assistant that brings autocomplete, chat, and inline editing to VS Code and JetBrains IDEs. Unlike proprietary tools, Continue gives you full control over which models you use, how they are configured, and what context they receive. It is the most customizable AI coding extension available.

You can connect Continue to any model provider: Anthropic, OpenAI, Google, Azure, local models via Ollama, or self-hosted solutions. This flexibility makes it popular with developers who want AI assistance without being locked into a specific vendor.

Continue's tab autocomplete works with any model you configure, from fast local models for low-latency suggestions to powerful cloud models for complex completions. The chat panel supports codebase context through @ mentions and custom context providers that can pull information from documentation, URLs, or databases.

The configuration system is Continue's differentiator. A JSON config file lets you define custom prompts, context providers, model parameters, and slash commands. You can tailor the experience to your exact workflow and project needs in ways that closed-source tools do not allow.

Continue is the right choice for developers who prioritize openness, customization, and model flexibility over a polished out-of-the-box experience. It works well for teams that want to standardize on a shared AI configuration across the organization without paying per-seat licensing. It is also the best option for developers who want to use local models for privacy or cost reasons.

Start with a cloud model like Claude or GPT-4 to evaluate Continue, then experiment with local models via Ollama for tasks where latency and privacy matter more than peak quality.