What Sets Them Apart
Cursor and Continue both extend VS Code with AI-assisted coding, but they solve the problem from opposite ends: Cursor is a closed-source, opinionated editor fork optimized for frictionless AI interactions, while Continue is an open-source extension that lets you route completions and chat through any LLM backend — local, cloud, or self-hosted. The choice between them comes down to whether you want a polished, batteries-included editor or a flexible plugin you wire into your own model stack.
Cursor and Continue at a Glance
Cursor is an AI-native editor built as a fork of VS Code, with the assistant woven into the experience rather than bolted on. Its Composer agent can plan and execute multi-file edits, the inline chat (Cmd-K) handles surgical refactors, and Tab completions are tuned on Anthropic and OpenAI frontier models. The product is paid (Pro at $20/month, Business at $40/seat), runs against a curated set of hosted models, and prioritizes low-friction defaults over configurability.
Continue is a free, Apache-2.0 licensed extension for VS Code and JetBrains that brings inline completions, chat, and slash commands without forcing a model choice. The architecture is provider-agnostic: you point it at OpenAI, Anthropic, Mistral, Ollama, vLLM, OpenRouter, AWS Bedrock, or any OpenAI-compatible endpoint, and the extension handles the rest. Configuration lives in a JSON file you commit alongside your repo.
On common ground, both deliver inline edits, chat-with-codebase, and autocomplete inside the same editor families developers already use. The split shows up in three places: who controls the model, how polished the agent flow feels, and whether the bill is yours or your provider's. Cursor optimizes for a fast first ten minutes; Continue optimizes for control over months of use.
Model Flexibility and Data Sovereignty
Continue's BYOK story is its strongest argument. A team running Llama 3.1 on a vLLM server behind a corporate firewall can use Continue without any data leaving the network — completions, chat, and embeddings all route through the local endpoint. The same config can fall back to GPT-4 for hard problems and to a cheap local 7B model for autocomplete, with rules per file type or directory.
Cursor takes the opposite trade. The product hides the model layer behind a single subscription, with frontier model access (Claude Opus, GPT-5, Gemini) baked into the price. You cannot point Cursor at a private model server, cannot run it air-gapped, and your prompts plus partial code context flow through Cursor's infrastructure. For teams in regulated industries — healthcare, defense, financial services with strict data residency — that constraint is a non-starter.
For everyone else, Cursor's model strategy is a feature, not a bug. The team handles model routing, context window management, and quality regression testing across providers, so users get consistent behavior without tuning prompts or chasing API key budgets. Continue's flexibility costs configuration time and the burden of choosing which model fits which task.