Cursor

Cursor is the AI code editor that made me stop copy-pasting code into ChatGPT. If you’re a developer who writes code daily and you’re still using vanilla VS Code with a Copilot subscription, Cursor is a genuine upgrade — particularly for multi-file edits and project-aware AI assistance. If you’re a casual coder or someone who only touches code occasionally, the free tier will be plenty, and the $20/month Pro plan might not justify itself. But for working developers shipping production code? This is the tool that’s changed how I build things.

What Cursor Does Well

The standout feature is how deeply the AI understands your actual codebase. This isn’t just autocomplete on steroids. When you open the chat panel with Cmd+L and ask a question, Cursor has already indexed your entire repository into semantic embeddings. Ask it “where does the authentication middleware run?” and it’ll point you to the exact file and line — even in a project with hundreds of files. I’ve tested this on a 30k-line TypeScript monorepo and a Django project with 200+ files, and the accuracy of its retrieval is consistently impressive.

Composer is the feature that gets the most buzz, and it deserves it. You describe what you want — “add a dark mode toggle to the settings page that persists in localStorage and updates the Tailwind classes” — and Composer generates or modifies code across multiple files simultaneously. It’ll update your React component, add the utility function, modify your CSS config, and create the localStorage hook. All in one shot. When it works, it saves 30-45 minutes of tedious wiring. When it doesn’t, you’ve got a clean diff view to see exactly what it changed, which makes fixing mistakes fast.

The tab completion is the other daily driver that’s hard to go back from. GitHub Copilot started this trend, but Cursor’s predictions feel noticeably more context-aware. It doesn’t just complete the current line — it anticipates the next 3-5 lines based on the surrounding code, your project conventions, and even your recent edits. I’ve watched it correctly predict an entire error handling block because it saw the pattern I used in a different file. The hit rate isn’t perfect (I’d estimate 65-75% accuracy for multi-line suggestions), but even the misses are usually close enough to accept and tweak.

The model flexibility is something I didn’t think I’d care about but now rely on. Claude 4 Sonnet is my go-to for complex refactoring and explaining tangled code. GPT-4o handles quick one-off questions and simple generation faster. Being able to switch models mid-conversation — without leaving the editor — means I’m always using the best tool for the specific task. Some AI coding tools lock you into a single model provider. Cursor treats models like interchangeable parts, which is how it should be.

Where It Falls Short

The request limits are Cursor’s most consistent frustration. On the Pro plan, you get 500 fast premium requests per month. That sounds like a lot until you’re deep in a feature build, using Composer and chat heavily for two days straight. I’ve burned through 100+ requests in a single afternoon when working on a complex migration. Once you hit the limit, you’re downgraded to slow requests, which can take 15-30 seconds each. That delay breaks flow state. Anysus charges $0.04 per additional fast request, but that adds up quickly if you’re a heavy user.

Composer’s confidence can be a liability on larger codebases. On smaller projects (under 10k lines), it’s remarkably accurate. But on bigger, more complex projects with nuanced architecture — dependency injection patterns, complex state management, or microservice boundaries — Composer sometimes makes changes that look correct but violate your architectural conventions. It’ll import something from the wrong layer, or create a new utility when one already exists three directories over. You absolutely cannot accept Composer’s output without reviewing the diff. Treat it like a code review for a fast-but-sometimes-careless junior developer.

The codebase indexing, while powerful, has real limitations. It can be sluggish to index monorepos with 100k+ lines. More annoyingly, I’ve noticed it sometimes misses context from files I just edited moments ago — probably because the index hasn’t refreshed yet. There’s a manual re-index option, but remembering to trigger it mid-flow is friction you shouldn’t have to deal with. The .cursorignore file helps by excluding irrelevant directories (node_modules, build artifacts), but getting the indexing tuned right for large projects takes some upfront effort.

There’s also no real collaboration story. If you’re on a team, each person has their own Cursor instance with their own chat history and Composer sessions. There’s no way to share a particularly useful prompt chain with a teammate, no shared knowledge base of “here’s how the AI should handle our project.” The Business plan adds admin controls and billing, but it doesn’t add collaborative AI features. For teams that want to standardize how they use AI across a codebase, you’ll need to manage that through .cursorrules files committed to your repo — which works, but it’s a manual process.

Pricing Breakdown

Hobby (Free): You get 2,000 code completions per month and 50 slow premium model requests. This is enough if you’re coding a few hours per week on side projects. The completions are the tab-autocomplete suggestions, and 2,000 per month will last a casual user. The 50 premium requests cover Cmd+L chat and Composer usage, but they’re all slow (meaning they queue behind paying users). Honestly, it’s a solid free tier for evaluating the tool.

Pro ($20/month): This is where most individual developers should land. Unlimited completions, 500 fast premium requests, and unlimited slow requests. The 500 fast requests are the key differentiator — fast means sub-5-second responses from frontier models. You also get access to every supported model (Claude 4 Sonnet, GPT-4o, and others as they’re added). For a developer who codes 30+ hours per week, you’ll likely use 300-500 requests per month. Heavy users will occasionally bump against the limit.

There’s no hidden setup fee, and you can cancel anytime. Cursor also offers the ability to bring your own API key for OpenAI or Anthropic, which bypasses the request limits entirely — you just pay the API provider directly. This is a smart option for power users who’d blow through the 500 fast request limit regularly. I’ve done the math: if you’re consistently using more than 700 requests per month, bringing your own API key often works out cheaper than buying additional fast requests at $0.04 each.

Business ($40/user/month): Doubles the per-user cost but adds centralized billing, admin usage dashboards, enforced privacy mode across the organization, and SAML SSO. The enforced privacy mode is the big draw for companies with compliance requirements — it ensures zero code retention by model providers. If you’re a team of 5-15 developers at a company that handles sensitive code, this tier makes sense. Below 5 users, the admin features probably aren’t worth the extra $20/user/month.

Enterprise (Custom pricing): Self-hosted deployment options, dedicated support, custom security reviews, and volume discounts. Cursor doesn’t publish these prices, but from conversations with their sales team, expect $50-70/user/month for mid-size deployments. This tier exists primarily for companies that can’t send code to external APIs at all.

Key Features Deep Dive

Composer (Multi-File AI Agent)

Composer is Cursor’s flagship feature and the primary reason people switch from competing tools. You open it with Cmd+I, describe what you want in plain English, and it generates a plan that spans multiple files. The key difference from single-file AI editing: Composer understands relationships between files in your project.

I recently used Composer to add a complete API endpoint to a Node.js project. I typed: “Create a GET endpoint at /api/reports that queries the reports table, filters by the authenticated user’s org_id, supports pagination, and returns the results with the total count.” Composer created the route handler, added the appropriate middleware, wrote the database query using my project’s existing Knex configuration (it recognized I was using Knex, not Sequelize), added input validation, and updated the route index file. Five files modified, all correctly wired together.

The limitation: Composer works best when your instruction is specific. Vague prompts like “improve the dashboard” produce mediocre results. The more context and constraints you give it, the better the output. I’ve also found that breaking large tasks into 2-3 Composer sessions (rather than one massive prompt) produces more reliable results.

Codebase-Aware Chat (Cmd+L)

The chat panel is your conversational interface to the AI, and what makes it special is the @-mention system. Type @filename to include a specific file as context. Type @codebase to let the AI search your indexed repository for relevant code. Type @docs followed by a URL to pull in external documentation.

This feature replaces the workflow of copying code into ChatGPT, pasting the response back, and losing context between messages. The AI can see your imports, understand your type definitions, and reference your project conventions — all without you manually providing that context.

In practice, I use the chat for three things: understanding unfamiliar code (“explain what this middleware chain does and why the error handler is positioned here”), debugging (“this test is failing with this error — what’s wrong?”), and design decisions (“I need to add caching to this service — given my existing architecture, what approach would you recommend?”). It handles all three well, though it sometimes over-explains when a brief answer would suffice.

Tab Autocomplete

Cursor’s autocomplete runs locally with a smaller model and doesn’t count against your premium request limit. It predicts code as you type, showing ghost text that you accept with Tab. The multi-line predictions are where it shines — it’ll see you writing a function signature and predict the entire function body.

What makes it better than GitHub Copilot’s autocomplete (in my experience with both over the past year): Cursor’s completions account for code in other files more consistently. If you defined a type in types.ts, Cursor’s autocomplete will use that type correctly in a different file without you having to open the types file first. Copilot does this sometimes, but Cursor does it more reliably.

The main annoyance: occasionally, the autocomplete suggestion conflicts with what you’re trying to type, and dismissing it (Escape) can feel disruptive if it happens repeatedly. You can tune the aggressiveness of suggestions in settings, and I’d recommend turning it down slightly from the default.

Inline Editing (Cmd+K)

Select a block of code, press Cmd+K, and describe what you want changed. The AI modifies just that selection, showing you a diff before you accept. This is the surgical tool compared to Composer’s broader approach.

I use Cmd+K constantly for things like: “refactor this function to be async,” “add error handling for the case where the API returns a 429,” or “convert this to use the builder pattern.” It’s fast (usually 2-3 seconds for small edits) and the diff preview means you never accept changes blind.

The limitation is that Cmd+K only modifies the selected code — it won’t update other files that might need corresponding changes. For cross-file modifications, you need Composer. I think of Cmd+K as a scalpel and Composer as a renovation crew. You need both.

.cursorrules Configuration

This is an underappreciated feature. You create a .cursorrules file in your project root with instructions that apply to all AI interactions in that project. Things like: “Always use functional React components with hooks, never class components. Use Tailwind CSS for styling. Error messages should be logged with our custom logger, not console.log. All API responses follow the { data, error, metadata } shape.”

This acts as a persistent system prompt. Every Composer session, every chat message, every Cmd+K edit respects these rules. For teams, committing this file to your repo means everyone’s AI assistant follows the same coding standards. I’ve seen this single feature reduce the amount of AI-generated code that needs correction by roughly 40% in my projects.

Privacy Mode

When enabled, Cursor guarantees that none of your code is sent to any server for storage or training. Code is transmitted to model providers for inference only, with no logging or retention. On the Business plan, admins can enforce this organization-wide.

For freelancers working with client code under NDA, or teams at companies with strict data policies, this feature is non-negotiable. I keep privacy mode enabled by default and have never noticed a performance difference.

Who Should Use Cursor

Solo developers and freelancers who code 20+ hours per week. The Pro plan at $20/month pays for itself if it saves you even an hour per week — and for most active developers, it saves significantly more than that. You’ll get the most value if you work on full-stack projects where the multi-file capabilities of Composer shine.

Small to mid-size development teams (3-20 devs) who want to standardize AI-assisted development. The .cursorrules file plus the Business plan’s admin controls give you enough governance without heavy process. Teams working in TypeScript, Python, or JavaScript/React ecosystems will find the AI accuracy highest for those languages.

Developers who already use VS Code. The migration is trivial — your settings, extensions, and keybindings import directly. If you’re invested in the JetBrains ecosystem (IntelliJ, PyCharm), switching to Cursor means giving up JetBrains-specific features, which may not be worth it. Check JetBrains AI Assistant as an alternative if you’re committed to that ecosystem.

Prototypers and indie hackers building MVPs. Composer can scaffold a significant amount of boilerplate, and the speed advantage for getting a working prototype together is substantial. I’ve used it to build proof-of-concept apps in a third of the time it would’ve taken without AI assistance.

Budget: $0-40/user/month. If you’re trying to spend $0, the free tier is genuinely useful for light usage. Most working developers will want the $20 Pro plan. Teams with compliance needs should budget $40/user.

Who Should Look Elsewhere

If you’re primarily a JetBrains user and rely heavily on IDE-specific features like IntelliJ’s refactoring tools, database browser, or Spring integration, switching to Cursor means losing those. GitHub Copilot works inside JetBrains IDEs and is a better fit if you don’t want to change editors.

If you need collaborative AI features, Cursor doesn’t have them. No shared sessions, no team prompt libraries, no way to see how your teammates are using AI on the same codebase. Windsurf is exploring some team-oriented features, and tools like Cline offer more transparency into the AI’s reasoning process that might benefit team code reviews.

If you work primarily with niche languages (Rust, Haskell, Elixir, COBOL), the AI accuracy drops noticeably compared to mainstream languages. The underlying models have less training data for these languages, and Cursor can’t fix that. You’ll still get value, but expect to correct the AI more often.

If you’re cost-sensitive and code very heavily, the request limits might frustrate you. A developer who uses Composer and chat 50+ times per day will blow through 500 fast requests in 10 working days. You can bring your own API key to avoid this, but then your effective monthly cost could reach $50-80 depending on usage. Aider is an open-source terminal-based alternative where you bring your own API key from the start and have full control over costs.

If you want AI for non-code tasks — writing documentation, managing projects, handling devops workflows — Cursor is laser-focused on code editing. It won’t help with your Jira tickets or your architecture diagrams. Look at more general-purpose AI tools for those needs.

The Bottom Line

Cursor is the best AI code editor available right now for developers who write code daily in mainstream languages. The combination of codebase-aware chat, multi-file Composer, and excellent tab completions creates a workflow that’s genuinely faster than anything I used before — including the year I spent with GitHub Copilot. The $20/month Pro plan is the sweet spot for most developers, and the only real caveat is watching your fast request budget on heavy coding days.

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