Cursor Review: Pricing, Features, Pros and Cons

Cursor is an AI-first code editor for developers who want strong AI help while staying in a familiar environment. It builds on VS Code and adds strong autocomplete, chat that understands your codebase, and agentic editing that can reach across your whole project. Below we cover what sets it apart and where you need to be careful.

What is Cursor?

Anysphere builds Cursor, a code editor forked from Visual Studio Code and redesigned around AI. It keeps the extensions, themes and keybindings developers already use, and it builds AI into every layer of the workflow instead of attaching a plugin on the side. That base pays off: you keep the editor you probably use today and get AI on top, with no new tool to learn. It competes in a crowded field of programming AI tools, so try two or three before you pay for a plan.

Three features shape the experience. Predictive Tab completion proposes multi-line edits and guesses where your cursor goes next. The in-editor chat knows your codebase because Cursor indexes your repository, so its answers and completions come from your real files instead of generic boilerplate. An agent mode plans and carries out changes across several files and runs terminal commands along the way. Cursor works with leading frontier models, and you can choose speed or depth for each task. That covers everything from a quick inline rewrite to a large refactor with many steps, all inside the window you already use.

Who is Cursor for?

Cursor targets software developers, from solo builders and hobbyists to full engineering teams. If you work in VS Code and want faster iteration, better autocomplete and an AI that can reason over a whole project, Cursor fits your existing habits with almost nothing new to learn.

It helps most in a few cases. You get your bearings in an unfamiliar codebase much faster when you can ask the editor how the project fits together and get answers drawn from the real files. Scaffolding new features, wiring up boilerplate and automating repetitive edits across many files are all jobs where the agent workflow pays for itself. Teams also gain from the Business tier’s central controls and enforced privacy settings. Non-developers will get little from Cursor, because the whole product assumes you write code and know how software projects are laid out. If you never open an editor, pick another tool. The ChatGPT review covers a similar service and gives you a good point of comparison.

Getting started and everyday use

Cursor forks VS Code, so setup takes almost no effort. On first launch it offers to import your VS Code extensions, settings, themes and keybindings, and within seconds the editor looks and acts like the one you came from. Most VS Code extensions work as you expect, which removes the usual hassle of moving to a new environment.

In daily use, the AI feels built into the editor. You notice Tab completion first: as you type, Cursor suggests multi-line changes and moves you to the next logical edit, and you accept with one keystroke. The Composer and agent workflow takes on bigger jobs. You describe a change in plain language, and Cursor drafts coordinated edits across files, shows diffs and runs commands you can review before you accept. Chat with your codebase answers questions about the project and points to your real files. The agent is strong but works best with clear instructions, and it sometimes gets overeager, so keep changes small enough to review and read each diff before you apply it.

Standout features

• Tab completion that predicts multi-line edits and where your cursor goes next.
• Codebase-aware chat based on your indexed repository instead of generic snippets.
• Agent and Composer modes for edits across files and running terminal commands.
• Model choice, so you can pick among leading frontier models for different tasks.
• VS Code compatibility, with import of extensions, themes and settings.
• Inline edits that rewrite selected code from plain-language instructions.
• Repository indexing so answers and completions follow your project’s real structure.

Output quality in practice

Cursor’s output is strong, mostly because it combines leading frontier models with rich indexed context from your project. Tab completion predicts what you mean within a file with unusual accuracy, and chat answers stick to the code you have instead of inventing plausible-looking code. With the repository indexed, Cursor grasps a large codebase better than tools that only see the open file: it can follow how the parts connect and suggest edits that match your existing patterns. If you want to know what powers these assistants, read how to build an AI model for the basics.

The agent can build sizable features with little guidance, and a specific, tightly scoped instruction usually gets you cleaner results than a vague one. Large, sprawling tasks still need oversight. Model choice helps here: pick a stronger frontier model for hard reasoning and a faster one for routine edits. As with any AI coding tool, read, test and understand generated code before you ship it. Cursor speeds up the work, and you still own the result.

Privacy and safety

Cursor has a Privacy Mode. Turn it on and Cursor stops storing your code or using it for training, which matters for proprietary or client work. On the Business tier, admins can enforce it across the organization, so no developer can switch it off by accident. That opens Cursor to teams that would otherwise hesitate to send code to any AI tool.

Know how it works even with Privacy Mode on: the AI features send relevant code context to model providers for processing, because the frontier models doing the work run remotely. For most projects that trade-off is fine. If your team handles sensitive intellectual property, read Cursor’s privacy documentation, confirm the mode is on and set expectations to match. As with any AI tool, never paste secrets, credentials or API keys into prompts, and check the editor’s data controls for yourself instead of assuming they are set.