Continue

Why Choose Continue?

Continue is strongest when a team wants AI coding to be configurable, inspectable, and tied to existing development infrastructure. Instead of asking developers to migrate into a new AI-native editor, Continue keeps the assistant close to the tools many teams already use: VS Code, JetBrains, the command line, and GitHub pull requests.

Its most distinctive direction is source-controlled AI checks. A check is not just a prompt hidden in a SaaS product; it can live in the repository, describe a concrete engineering standard, and run against every pull request. That makes Continue useful for teams trying to convert recurring review comments into repeatable AI-assisted quality gates.

Continue also appeals to developers who care about model choice. The same workflow can route chat, editing, autocomplete, embeddings, or apply operations to different providers. This is especially useful when a team wants fast local completions, stronger cloud reasoning for refactors, and a separate embedding model for codebase context.

Core Workflow

The practical Continue workflow has two layers. The first layer is the in-editor assistant: use chat to understand code, edit mode to change selected blocks, autocomplete to speed up repetitive implementation, and agent mode for broader tasks. This layer is familiar to anyone who has used Copilot-style coding assistants.

The second layer is more process-oriented. Teams define checks in the repository, run them locally while developing, and then enforce them through pull request status checks. This creates a feedback loop where an AI agent is not only writing code but also checking whether the code follows team-specific standards.

For mature teams, the best pattern is to start with narrow checks. Examples include migration safety, missing input validation, accessibility regressions, logging conventions, test coverage rules, or framework-specific anti-patterns. Broad “review everything” prompts tend to create noise; Continue works better when checks are specific enough that developers trust the signal.

Use Cases

Continue is a good fit for teams that already have clear engineering standards but struggle to enforce them consistently. A senior engineer can encode repeatable review rules once, store them with the codebase, and let every pull request receive the same first-pass inspection.

It is also useful for developers who want a BYOK or local-model coding setup. Continue can sit in the editor while the actual model runs through a preferred API provider, local runner, cloud gateway, or self-hosted endpoint. That makes it a practical option for privacy-sensitive experimentation and cost-controlled AI adoption.

Another strong use case is AI workflow standardization. Instead of each developer inventing prompts from scratch, teams can share agent configurations, rules, prompts, and model roles. This turns AI coding from a personal productivity trick into a more repeatable engineering workflow.

Comparison to Alternatives

Compared with GitHub Copilot, Continue gives developers more control over model routing and workflow configuration. Copilot is easier to adopt for many teams, but Continue is more attractive when a team wants open-source visibility, local models, custom rules, and repository-level AI checks.

Compared with Cursor or Windsurf, Continue is less of a full editor replacement. Cursor and Windsurf offer a more integrated AI-native environment, while Continue is better for teams that want to keep their current editor and gradually layer AI workflows into existing development processes.

Compared with Cline, Continue is usually a better fit for configurable team workflows and model-role separation, while Cline is often chosen for more autonomous VS Code agent behavior. The right choice depends on whether the priority is controlled repeatability or hands-on agent autonomy.

Compared with Aider or Claude Code, Continue has a broader surface area. Terminal-first agents are excellent for direct repo manipulation, but Continue connects editor assistance, CLI behavior, and pull request enforcement into one workflow.

Best Configuration

For individual developers, the best starting setup is usually the VS Code extension with a familiar cloud model for chat and edit, plus a faster or cheaper model for autocomplete. This avoids overpaying for every keystroke while keeping stronger reasoning available for complex tasks.

For local-first setups, start with a local model provider for autocomplete and low-risk exploration, then reserve cloud models for tasks that need stronger reasoning or larger context. Disable telemetry and verify local configuration when offline or air-gapped behavior matters.

For teams, the best setup is to standardize model roles and rules before rolling out checks broadly. Decide which provider handles chat, which model handles autocomplete, which model applies diffs, and which repositories should run AI checks. Without that discipline, Continue can become powerful but inconsistent.

For PR checks, start with three to five narrow standards that already show up repeatedly in human review. Treat each check like code: review it, version it, refine it, and remove it if it generates too much noise.

Migration Notes

Moving from GitHub Copilot to Continue is mostly a workflow migration rather than an editor migration. Developers can keep VS Code, but they will need to think more deliberately about providers, model roles, YAML configuration, and rules. The tradeoff is more control in exchange for more setup responsibility.

Moving from Cursor or Windsurf is different. Continue does not try to reproduce every AI-native editor affordance. The migration only makes sense if the team wants to return to an existing editor stack or prioritize open configuration and repository-defined checks over a tightly integrated editor experience.

For JetBrains users, evaluate the current plugin path carefully. Continue still references JetBrains support, but official repository notes recommend the CLI over the JetBrains plugin. Teams using IntelliJ, WebStorm, or PyCharm should test the exact workflow they expect before committing to a large rollout.

The most important migration principle is to separate personal coding assistance from team enforcement. Continue can do both, but they should be adopted in stages: first prove the editor workflow, then standardize model configuration, then add repository checks where the signal is clearly useful.