Compare AI coding assistants at a glance
Choosing an AI coding tool means weighing IDE support, the model backend, how much codebase context it can use, price, and whether it offers an offline or self-hosted privacy mode. This table puts the major assistants side by side — Cursor, GitHub Copilot, Continue, Cody, Windsurf, Codeium, and more — so you can pick the right pair-programmer for your stack and policy constraints.
How to read the table
- IDE is which editors the tool supports — VS Code, JetBrains, its own fork, or multiple.
- Model backend is which LLMs power it; tools that let you bring your own model are the most flexible.
- Context is a 1–5 rating of codebase-wide awareness, from single-file completions to full-repo agents.
- Privacy mode flags whether you can run it offline or self-host for code that cannot leave your network.
- $/mo is a per-developer list estimate for the paid individual tier.
Filter by privacy requirement and budget, search by tool name, and click a column header to sort.
What the context rating actually means
A context score of 1 means the tool looks at the file you have open — typical of basic extension-based completions. A 3 means it maintains a semantic index of the project so it can reference a type defined in another module. A 5 means an agent mode that can open, read, write, and run files across the repo autonomously, making it genuinely capable of multi-file refactors and cross-service changes. The jump from 3 to 4 is where the experience changes qualitatively: the assistant stops complaining about unknown imports.
How model backends differ in practice
The model backend determines raw capability and, crucially, data residency. Tools like GitHub Copilot lock to specific OpenAI models; Cursor and Windsurf let you switch between GPT-4o, Claude Sonnet, or their own inference. Bring-your-own-key options (some tiers of Continue, for example) mean the request goes to your own provider account — useful if you already have a negotiated enterprise agreement. Open-weight models via Ollama offer the strongest privacy guarantee because nothing leaves your machine, though smaller local models are typically weaker at multi-file reasoning.
Privacy and enterprise considerations
Most cloud coding assistants send snippets of your code — including context windows around the cursor — to a remote inference endpoint. Free tiers may use that data to improve models. Enterprise plans generally offer a zero-retention agreement (the provider does not log or train on your code), but the requirement is to check the tier-specific terms, not the marketing copy. Tools with a self-hosted option (Cody Enterprise, Tabby, Continue against a local Ollama instance) avoid this entirely because the request never leaves your network, which matters for regulated industries, defence contractors, and any team with strict code-confidentiality requirements.
Tips for picking a tool
- For agentic multi-file refactors, Cursor and Windsurf lead with deep indexing and autonomous edit modes.
- For a lightweight completion layer in your existing editor, GitHub Copilot and Codeium are fast and inexpensive.
- For strict privacy or air-gapped environments, Continue and Tabby run fully local against your own models — no code leaves your machine.
- Match the model backend to your needs: tools that let you point at Claude, GPT, or a local model give you control over quality, cost, and data residency.
- Trial before committing. Most tools have a free tier or trial. Run your typical daily tasks — autocomplete, a small refactor, a multi-file change — for a week before paying. Speed, latency, and how well the tool handles your language and framework matter more than feature lists on a marketing page.
- Check IDE compatibility carefully. JetBrains users, Neovim users, and teams on Emacs have meaningfully fewer options than VS Code users. The IDE column in the table is the first filter for many developers.