GitHub Copilot and Continue are both AI coding assistants that integrate into VS Code, but they solve the problem differently. Copilot is a cloud subscription service backed by OpenAI models, sending your code to GitHub’s servers for every completion and chat request. Continue is an open-source VS Code extension that routes requests to whatever LLM backend you configure — local models via Ollama, cloud APIs, or a mix of both. This guide compares the two on the dimensions that actually matter for day-to-day development work.
Setup and Getting Started
Copilot requires a GitHub account and a paid subscription (or an active free trial). Authentication happens through VS Code’s GitHub integration. Once authenticated, completions and chat start working immediately — no model selection or configuration required. This simplicity is Copilot’s biggest practical advantage for teams: zero infrastructure, zero configuration, works out of the box in under five minutes.
Continue requires the VS Code extension plus a backend. With Ollama already running, the setup is: install the Continue extension, open its config, set the model to your Ollama model. Five to ten minutes, but you own the full stack. For teams, each developer needs Ollama running locally or a shared Ollama server needs to be set up — an additional infrastructure step that Copilot avoids entirely.
Completion Quality
GitHub Copilot uses GPT-4o class models for completions and chat, which are among the strongest available for code generation. The completion quality on common patterns — React components, Python functions, SQL queries, standard library usage — is excellent. Copilot excels at completing code it has seen many times in training: boilerplate, common algorithms, standard framework patterns.
Continue with a strong local model like Qwen2.5-Coder 7B or DeepSeek-Coder V2 produces good completions that are competitive with Copilot for many everyday tasks. The gap is noticeable on less common languages, complex algorithmic problems, and cases where Copilot’s larger model size gives it an advantage. For the 80% of completions that involve common patterns in popular languages, the quality difference is often hard to distinguish in day-to-day use. For the remaining 20% — unusual libraries, complex refactoring, less common languages — Copilot’s model quality shows more clearly.
Chat and Codebase Understanding
Copilot Chat in VS Code can reference open files and selected code, and the newer Copilot Workspace features allow deeper project-level context. The chat quality is strong — GPT-4o handles complex debugging questions, architectural discussions, and code explanations well.
Continue’s chat has a significant advantage in one area: the @codebase command. Continue indexes your entire project using a local embedding model and enables semantic search across all files — ask “where is the authentication logic?” or “show me all places where this function is called” and Continue retrieves the relevant code from anywhere in your project, not just open files. Copilot’s context is limited to what is currently open in the editor. For large codebases where the relevant code is spread across many files, Continue’s codebase indexing is a meaningful practical advantage that Copilot does not currently match.
Privacy
Copilot sends code snippets to GitHub’s servers for every completion and chat request. GitHub’s policy states that code snippets are used to generate suggestions and not retained for model training when the telemetry setting is off, but the data does leave your machine and pass through third-party infrastructure. For proprietary codebases, unreleased features, client projects under NDA, or any code containing sensitive business logic, this is a policy decision that deserves explicit consideration rather than assumption.
Continue with local models processes everything on your machine. The code never leaves your computer. For organisations with data handling requirements — healthcare, legal, financial, government — this is often the deciding factor. For individual developers working on personal or open-source projects, it matters less, but local processing is still the privacy-preserving default for those who prefer it.
Cost
GitHub Copilot Individual costs roughly $10 per month. Copilot Business (with organisation-wide management features) is approximately $19 per user per month. For a team of 10 developers, this is around $190–$2,280 per year depending on the tier.
Continue with local models has zero ongoing cost after the hardware is in place. If you already have a machine capable of running Ollama (most modern developer workstations qualify), Continue costs nothing. For teams, a single shared Ollama server costs hardware plus electricity — typically well under $1,000 per year for a mid-range GPU server serving a small team, amortised over several years of use.
Continue + Cloud APIs: The Hybrid Option
Continue is not exclusively a local model tool. You can configure it to use OpenAI, Anthropic, or any other API provider as the backend, paying per token rather than a flat subscription. This hybrid approach — local models for everyday completions and chat, cloud API for complex questions where a larger model is worth the cost — is a practical middle ground. Many developers configure Continue with Ollama as the default and add a Claude or GPT-4o API key as a fallback for difficult problems, giving them local-first privacy and cost-efficiency with cloud-quality on demand.
IDE Support
GitHub Copilot supports VS Code, JetBrains IDEs, Neovim, and several other editors through official plugins. Continue supports VS Code and JetBrains primarily, with community support for Neovim via a separate plugin. If your team uses a mix of editors, Copilot’s broader official support may simplify deployment. For VS Code-first teams, both tools offer equivalent integration quality.
Which Should You Use?
Choose Copilot if you want the highest completion quality available with zero infrastructure setup, your work does not involve sensitive code, and the subscription cost is not a constraint. Copilot is the simpler choice for individuals and teams that want AI coding assistance without managing any infrastructure.
Choose Continue if privacy matters for your codebase, you want zero ongoing cost, you value the codebase-wide semantic search that @codebase provides, or you want to experiment with different models for different tasks. Continue requires slightly more setup but offers more flexibility and is the better long-term platform for developers who want to control their AI tooling stack rather than depend on a cloud subscription.
Many developers end up running both: Copilot for inline completions (where its speed and quality are hard to beat) and Continue for chat and codebase questions (where its indexing and flexibility are superior). The two tools are complementary rather than strictly competitive, and using both together is a reasonable configuration for developers who want the best of each approach.
Latency and Responsiveness
Copilot’s completions come from GitHub’s servers, so network latency is part of every request. On a good broadband connection this is barely noticeable — 100–300ms added per completion. On slower connections, VPNs, or from regions distant from GitHub’s inference servers, this becomes frustrating during active coding. Copilot also occasionally throttles under server load, producing inconsistent response times that break flow.
Continue with a local model has zero network latency — only inference time on your GPU or CPU. On a mid-range GPU with a 7B model, first-token latency is typically 200–500ms, competitive with cloud latency on a good connection. The key difference is consistency: local inference is predictable and doesn’t vary with external server load, your network quality, or geographic location. For developers in regions with less reliable internet, local models have a structural advantage cloud tools cannot overcome.
Model Customisation
Copilot does not allow model selection or customisation — you use whatever GitHub has deployed, and all tuning is in their control. This simplicity works well for most users most of the time, but it means you cannot adjust model behaviour for your specific codebase or domain.
Continue gives you full control. You can switch models per task — a fast 3B model for quick completions, a 12B model for complex debugging, Qwen2.5-Coder for Python and JavaScript work. You can write Modelfiles with custom system prompts that prime the model with your project’s conventions, preferred patterns, or domain vocabulary. This level of customisation simply isn’t available in Copilot and represents a meaningful advantage for teams with specific technical domains or coding standards.
Offline Use
Copilot requires a live internet connection and GitHub authentication every session. If you work offline — on a plane, in a restricted environment, or during an outage — Copilot is completely unavailable. Continue with local models works without any internet connection after the initial model download. For developers who travel heavily, work in air-gapped environments, or want guaranteed availability regardless of connectivity, local models via Continue are the only viable option.
The Real-World Decision Framework
The choice between Copilot and Continue is ultimately a values question as much as a technical one. Copilot optimises for simplicity and maximum model quality at a fixed monthly cost. Continue optimises for privacy, control, and flexibility at the cost of a higher initial setup investment. Neither is objectively superior — the right answer depends on what you value most about your development tooling.
For individual developers just getting started with AI coding assistance, Copilot is the lower-friction entry point. The quality is high, setup is minimal, and the subscription cost is reasonable for professional use. For developers who have already invested in local AI infrastructure for other purposes, adding Continue on top of an existing Ollama setup is a natural extension with essentially zero additional cost or complexity. For privacy-conscious professionals or those at organisations with data handling requirements, Continue with local models may be the only compliant option.
Many developers end up running both: Copilot for inline completions where its speed and quality are hard to beat, and Continue for chat and codebase questions where indexing and flexibility are superior. The two tools are complementary rather than strictly competitive, and using both together is a reasonable configuration for developers who want the best of each approach without committing fully to either.
Getting Started with Continue
If you want to try Continue alongside or instead of Copilot: install the Continue extension from the VS Code marketplace, ensure Ollama is running with a model pulled (ollama pull qwen2.5-coder:7b is a strong starting point for coding), and open Continue’s config to point it at http://localhost:11434. Within five minutes you have a working AI coding assistant that costs nothing per request and keeps all your code local. Run it for a week on real work, compare the experience to Copilot if you have a subscription, and let your own workflow data inform the decision rather than any benchmark comparison. The local AI tooling ecosystem has matured rapidly and Continue represents one of its most practical and immediately usable entry points for developers — one that gets more capable as models improve without requiring any changes to your tooling or workflow.