Acquisition status: Continue's official site says Continue has joined Cursor. Continue is therefore a historical product on aicoolies, not a current independent option. The feature sections below preserve the pre-acquisition comparison as historical context; present-tense descriptions of Continue are not claims of current availability, support, pricing, or roadmap.
Acquisition Outcome
Refact.ai and Continue should no longer be framed as two current products. Continue's official acquisition notice turns the old comparison into a migration record: Refact.ai is the active winner, while Continue's former extension, model-routing, and local-control traits are retained only to help existing users map requirements to a supported destination.
Historical Product Snapshot
Continue takes a deliberately minimal approach as a VS Code and JetBrains extension that acts as a bridge between developers and their preferred LLM providers. It supports inline code completion via Tab autocomplete, chat-based assistance for explaining and refactoring code, and context injection from files, terminal output, documentation, and codebase search. The philosophy is to provide a clean interface layer rather than an autonomous agent.
Self-hosted deployment is where Refact.ai creates the strongest separation from competitors. Organizations can run the entire AI coding infrastructure on their own NVIDIA GPUs using Docker, ensuring source code never leaves company servers. This addresses the fundamental trust barrier that prevents many enterprises from adopting cloud-based AI coding tools, particularly in regulated industries like finance, healthcare, and defense.
Continue's model flexibility is its core strength. The extension works with virtually any LLM provider through a unified configuration, including OpenAI, Anthropic, Google, Mistral, Ollama for local models, and any OpenAI-compatible API endpoint. Users can configure different models for different functions, using a fast small model for completions and a larger model for complex chat interactions, optimizing both speed and cost.
Historical Completion and Agent Workflows
The code completion experience differs in approach. Refact.ai uses a fine-tuned Qwen2.5-Coder model powered by RAG that indexes the entire codebase for context-aware suggestions reflecting project-specific patterns. Continue offers Tab autocomplete that works with any configured model, relying on the model's own capabilities supplemented by context from the current file, open tabs, and manually added documentation.
Agent capabilities represent the starkest contrast. Refact.ai's agent connects to development tools and databases, executes shell commands, browses the web, and maintains a growing knowledge base that improves with each interaction. Continue provides no autonomous agent functionality, instead offering manual code actions like explain, refactor, and generate tests that execute in a single turn without multi-step planning.
Enterprise features and pricing models diverge significantly. Refact.ai offers tiered pricing from a free tier with 5,000 coins through Pro and Enterprise plans that include on-premise deployment, custom model fine-tuning on organizational codebases, and dedicated engineering support. Continue is entirely free and open-source under Apache 2.0, with optional Continue for Teams offering centralized configuration management.
Migration Requirements
The learning and adaptation dimension favors Refact.ai. The platform maintains a project-specific memory that accumulates insights from developer interactions, learns coding preferences and standards, and shares knowledge across team members. Continue does not persist learning between sessions, relying instead on static context providers and manual configuration of documentation sources.
Community size and ecosystem breadth favor Continue. With broad adoption across the VS Code ecosystem and active development of context providers for diverse documentation sources, Continue benefits from a larger contributor base. Refact.ai has a smaller but dedicated community focused on self-hosted deployment scenarios and enterprise agent workflows that require deeper tool integration.
