Continue

Snapshot Verdict

Continue is an open-source AI coding assistant that acts as a bridge between your local development environment and virtually any Large Language Model (LLM). Unlike commercial giants like GitHub Copilot, Continue gives you total control over which "brain" powers your code suggestions, allowing you to swap between local models for privacy or high-end cloud models for complexity. It is the best choice for developers who value autonomy and data sovereignty over a polished, locked-in ecosystem.

Product Version

Version reviewed: 0.8.54 (VS Code Extension)

What This Product Actually Is

Continue is an IDE extension for Visual Studio Code and JetBrains editors. It is not an AI model itself. Instead, it is the interface that allows you to bring your own AI models into your coding workflow. It enables three primary functions: a chat sidebar for asking questions about your codebase, an inline "edit" feature for refactoring code with natural language commands, and an autocomplete engine that predicts the next few lines of code as you type.

The core differentiator is its "bring your own model" (BYOM) philosophy. While most AI coding tools force you into a specific subscription (like Copilot's $10/month plan), Continue lets you connect to OpenAI’s GPT-4, Anthropic’s Claude 3.5 Sonnet, or even locally hosted models via Ollama or LM Studio. This means if you are working on sensitive intellectual property, you can run the AI entirely on your own hardware without an internet connection, ensuring no code ever leaves your machine.

It also indexes your local files to provide "context-aware" answers. It builds a local embeddings database of your project, so when you ask "Where is the authentication logic located?", it can actually find the relevant files rather than guessing based on general knowledge.

Real-World Use & Experience

Setting up Continue feels more like configuring a power tool than installing a simple app. After installation, you are greeted with a configuration file (config.json). For a beginner, this might be intimidating, but it is where the tool's true power lies. You define your "models"—perhaps using Claude 3.5 Sonnet for complex architectural questions and a smaller, faster model like StarCoder2 via Ollama for basic autocomplete.

In daily use, the @ mention system is the standout feature. When you want to discuss a specific part of your project, you type @ in the chat window. This allows you to explicitly pull in files, folders, or even terminal output into the prompt. This solves the "copy-paste" fatigue common with using ChatGPT in a browser. You aren't just chatting with an AI; you are chatting with an AI that is looking at your specific folder structure.

The inline editing (usually mapped to Cmd/Ctrl + I) is snappy. You highlight a block of code, tell it to "convert this to a switch statement," and it generates a diff showing the changes. You can then accept or reject the changes line-by-line. The latency depends entirely on the model you choose. If you use a local model, the speed is limited by your GPU; if you use a cloud API, it's limited by your internet and the provider's speed.

One of the more profound experiences is the ability to use "Slash Commands." You can create custom commands like /tests that automatically prompt the AI to write unit tests for the current file using your preferred framework. This level of customization makes it feel like a bespoke assistant tailored to your specific tech stack.

Standout Strengths

• Total control over AI model selection.
• Local-first architecture ensures high privacy.
• Deep codebase indexing for better context.

The flexibility of Continue cannot be overstated. In a world where software is increasingly moving toward "walled gardens," Continue is a rare gate-opener. If a better model comes out tomorrow from a new company, you don't have to wait for a plugin update; you just change one line in your config file.

The context-awareness is also superior to many "first-gen" AI assistants. Because it indexes your local files, it understands the relationship between your components. It doesn't just know how to write Python; it knows how you write Python in this specific project. This reduces the number of "hallucinations" where the AI suggests libraries or functions that don't exist in your environment.

Finally, the cost efficiency is remarkable. For hobbyists or developers in regions where a $20/month subscription is a burden, using Continue with free-tier APIs or local models makes professional-grade AI assistance accessible to everyone.

Limitations, Trade-offs & Red Flags

• High initial configuration complexity for beginners.
• Latency varies wildly depending on backend.
• Autocomplete requires significant local hardware resources.

The biggest hurdle is the "blank canvas" problem. Because Continue doesn't come with a "standard" model pre-configured in a seamless way like Copilot, you have to spend time setting up API keys or local servers. If your configuration file has a syntax error, the extension simply won't work, which can be frustrating for someone who just wants to start coding.

There is also the "hardware tax." If you want to use Continue for autocomplete without sending data to the cloud, you need a machine with a decent GPU (ideally an Apple Silicon Mac or a PC with an NVIDIA RTX card). Running a local model and a modern IDE simultaneously can turn a laptop into a space heater and drain the battery quickly.

Lastly, while the open-source nature is a strength, the UI isn't always as polished as commercial competitors. You might encounter occasional bugs where the chat window stops scrolling or the indexing process gets stuck on large repositories. It requires a certain level of "tinkering" that a busy professional might find distracting compared to a "just works" solution.

Who It's Actually For

Continue is for the "privacy-conscious power user." If you work for a company with strict data policies that forbid sending code to third-party servers, Continue paired with a local Ollama instance is your only viable path.

It is also for the "AI polyglot"—the developer who wants to use GPT-4 for logic, Claude for refactoring, and Llama 3 for quick explanations. If you enjoy optimizing your workflow and don't mind editing a JSON file to get things exactly right, this tool will feel like a superpower.

It is less suited for the absolute beginner who is just learning to code and doesn't want to manage API keys or understand the difference between an LLM and an embedding model. If you want the "iPhone experience" of AI coding, this isn't it. This is the "Linux experience."

Value for Money & Alternatives

The software itself is free and open-source (Apache 2.0 license). Your only costs are the API usage fees if you choose to use cloud models (like OpenAI or Anthropic) or the electricity and hardware costs for running local models. For most users, using an API like Groq or OpenRouter with Continue results in a monthly bill of $2–$5, significantly cheaper than the $10–$20 charged by most subscription services.

Value for money: great

Alternatives

• GitHub Copilot — The industry standard with the smoothest "just works" experience but limited model choice and high privacy concerns.
• Cursor — A separate code editor (fork of VS Code) that is more deeply integrated with AI than a plugin but locks you into their ecosystem.
• Supermaven — A high-speed autocomplete alternative that focuses on exceptionally large "context windows" and low latency.

Final Verdict

Continue is the most important tool in the open-source AI coding space. It breaks the monopoly held by large corporations over developer productivity. While it requires more setup than its rivals, the reward is a truly custom, private, and future-proof coding environment. It is a "forever tool" that evolves as fast as the AI field itself. If you are willing to spend thirty minutes on configuration, you will likely never go back to a locked-down alternative.