TL;DR
- Cursor is a VS Code fork with a built-in AI agent: writes, edits, and runs code directly in the IDE without a separate plugin.
- Pro plan costs $20/month (~85 PLN) and provides access to frontier models (Claude Sonnet 4.6, GPT-4o, Gemini 2.5 Pro).
- Cursor wins on project context and in-IDE editing speed; Claude Code wins on autonomous, multi-step tasks.
- For junior and mid-level developers and diving into unfamiliar codebases: Cursor. For autonomous agents working unsupervised: Claude Code.
- Pro breakeven threshold: if you save 2+ hours weekly, $20/month pays for itself in the first month.
Most "reviews" of Cursor AI boil down to: "AI in an editor, pretty cool." That tells you nothing about when Cursor beats Copilot, when it loses to Claude Code, and for whom $20/month is a solid investment versus money down the drain.
What is Cursor AI and how does it differ from a plugin?
Cursor is an IDE built on VS Code, where AI is a foundational layer, not an add-on. When you install GitHub Copilot as a plugin, you add autocomplete to your editor. Cursor is an editor redesigned from the ground up for agentic workflow.
Key difference: project context. Copilot operates mainly on the open file. Cursor sees the entire project: reads dependencies between files, understands types from other modules, knows what's in neighboring components. In a 500+ line project, this difference is invisible. In a 10,000+ line project, it's a fundamental quality gap in suggestions.
Cursor is an AI-powered IDE available on macOS, Windows, and Linux. VS Code extensions work without modification, and VS Code config takes about ten minutes.
How do the 3 work modes in Cursor AI function?
Cursor has three ways to interact with AI, each for a different task.
Tab (multi-line autocomplete): Cursor predicts what you'll write and suggests several lines at once, not just one. Activates automatically while typing. You accept with Tab or ignore. For repetitive code (CRUD endpoints, unit tests, serializers), Tab eliminates 70-80% of boilerplate typing.
Chat (Ctrl+L): Chat in a side panel with project context. You attach a specific file, code fragment, or entire directory using @ in your message. The model analyzes and responds with text plus code blocks. Chat doesn't edit directly: you paste code from the response manually or click "Apply". Good for architecture questions, explaining fragments, and planning approaches.
Agent (Ctrl+I, Composer mode): You write a task in Polish or English, Cursor opens files, edits code, creates new files, runs terminal commands on its own. Every change is visible in diff view: you accept or reject. Agent mode is what separates Cursor from simple autocomplete. It's also the biggest risk: the model can cause damage if you don't review what you accepted.
For a developer coming from VS Code: the first 30 minutes are orientation. After a week, Tab and Agent become natural flow.
How much does Cursor AI cost in 2026?
Cursor Pro is $20/month, Teams is $40/user/month. Cursor.com/pricing offers four tiers:
Hobby (free): Limited agent queries and limited Tab completions. Limits hit after a few hours of daily production work. Enough to test whether Cursor fits your style at all.
Pro ($20/month, ~85 PLN): Extended agent and Tab limits, access to frontier models (Claude Sonnet 4.6, GPT-4o, Gemini 2.5 Pro), full Composer Agent. Most solo developers and small teams need nothing more.
Teams ($40/user/month, ~170 PLN): Everything in Pro plus shared project context for the entire team, SAML/OIDC SSO, shared AI rules, usage analytics, centralized billing. Makes sense from 3-5 people when you want uniform AI rules for the entire project and single invoicing.
Enterprise (custom pricing): Pooled usage, SCIM seat management, AI code tracking API, audit logs, granular model control, dedicated support. Level for 50+ developers where compliance is a requirement.
For a Polish developer working independently: Pro, about 80-85 PLN/month at current exchange rates. Less than one tank of gas.
Cursor vs GitHub Copilot: comparison for Polish developers
GitHub Copilot Pro is $10/month (~42 PLN), Cursor Pro is $20/month (~85 PLN). For double the price, you get primarily better project context.
GitHub Copilot works great in one scenario: inline autocomplete in the file you have open. Copilot Chat improved significantly in 2025, but project context remains limited: each query must have explicitly attached files, the model doesn't "see" the rest of the project automatically.
Cursor Tab and Agent see the entire project from the start. When you write a new endpoint, Cursor knows what types are defined in the model, what an analogous endpoint looks like in a neighboring file, what conventions exist in the project. This translates to fewer corrections: suggestions fit existing code instead of requiring manual adjustment.
When Copilot has the advantage: company security policy requires code not leave Microsoft infrastructure, or you already pay for GitHub Advanced Security and Copilot comes bundled.
When Cursor has the advantage: you work on your own hardware, the project is large enough that context makes a difference, or you want Agent mode with autonomous editing of multiple files at once.
Cursor vs Claude Code: two different AI paradigms
Cursor and Claude Code solve different problems and aren't direct substitutes.
Cursor is IDE-first: you live in the editor, AI helps you inside, every change is under your real-time control. You see the diff, accept or reject, full control over what enters the code.
Claude Code is terminal-first: you give the agent a task and walk away. The agent works autonomously for ten-plus minutes: reads code, runs tests, fixes errors, makes commits. Less supervision, more autonomy. I wrote about this in detail in comparing both tools.
In practice: for a feature you understand well and want to code quickly with control over every step, Cursor is faster. For comprehensive legacy code refactoring or writing tests for 50 functions where you prefer the agent dig through on its own, Claude Code often delivers better results without micromanagement.
Most experienced developers use both. Cursor daily in the IDE, Claude Code for deeper autonomous operations in the terminal.
How do you configure Cursor Rules for your own project?
Cursor Rules is a .cursorrules file in the project root that defines how the model should behave in your specific codebase.
Without Rules, Cursor produces generic code matching general patterns. With Rules, the model knows your conventions: "we use camelCase for variables, snake_case for database keys", "every new class must have unit tests", "in this project we don't use any in TypeScript".
Example .cursorrules for a TypeScript/Node.js project:
You are working in a TypeScript/Node.js project.
- Use strict TypeScript, never `any` type
- All async functions must handle errors with try/catch
- Tests go in __tests__ folders co-located with source files
- Use PLN for all monetary values, not USD
- No TODO comments in production code
Rules have a direct impact on code quality from Agent mode. Without them, the model generates code consistent with its training, not your project. With good Rules, you reduce the number of correction iterations.
Where does Cursor AI actually save time?
Cursor saves the most time in three specific scenarios.
Diving into unfamiliar codebases. Cursor Agent can read the entire project and generate an explanation of architecture, data flow, conventions. The first 2-3 days of orientation in a new project shrink to a few hours.
Boilerplate and repetitive code. Every CRUD endpoint, every serializer, every test for an analogous class. Tab completes them in seconds instead of minutes. On a project where you write 20 similar endpoints, you save hours.
Debugging with console errors. Paste the stack trace into Agent, say "fix this". Cursor reads the files indicated in the error and proposes a fix. Eliminates 60-70% of trivial errors without searching documentation. For more complex errors it requires iteration, but directs you to the right place faster than reading the stacktrace alone.
Cursor AI limitations: what the marketing doesn't tell you
Token limits in context for large projects. Cursor doesn't see the entire project simultaneously in a 200,000+ line monorepo. It must prioritize what to include in context. Large projects can cause important files to be skipped. Solution: use .cursorignore to cut out node_modules, dist, generated files. Reducing noise improves suggestion quality.
The model doesn't know project history. Cursor doesn't know why you made a specific architectural decision 3 years ago, what a commented block means, and why legacy-handler.js MUST remain unchanged. That's your job: either document it in .cursorrules or review every suggestion.
Suggestion quality is proportional to prompt quality. "Fix this code" gives worse results than "Fix null reference error on line 247, don't change method signature, preserve empty array handling". Cursor amplifies instruction precision in both directions.
Risk for juniors. The model generates code that looks correct even when it isn't. For someone learning to program, automatically accepting suggestions without understanding is a straight path to code you don't understand and can't debug. Cursor isn't a learning tool, it's a tool to accelerate work for someone who already knows how.
Is Cursor AI worth buying?
The only reliable answer: check how many hours per week you save after 5 days on Hobby (free).
Calculation for a Polish freelancer billing clients:
- Cursor Pro: $20/month, about 85 PLN
- Savings 2h weekly x 4 weeks = 8h monthly
- Billable rate 150 PLN/h: 8h x 150 PLN = 1,200 PLN value
- ROI: 1,200 PLN gain at 85 PLN cost
This calculation isn't detached from reality for an experienced developer who writes code daily. For boilerplate-intensive projects (backend API, CRUD-heavy applications), 2 hours weekly is a conservative estimate.
For a developer learning and writing little code daily: Hobby for a year, learn to program first, Cursor later.
For teams: Teams ($40/user/month, ~170 PLN) starts making sense at 3+ people with active coding and a shared project. Below 3 people, everyone can have their own Pro separately.
Start with Hobby for 5 working days on a real project. If Tab and Agent become natural flow, Pro will pay for itself. If you constantly check what the model changed and undo more than you accept, it's not your workflow and the $20 is better left unspent.
If you're interested in automating developer workflow with AI agents, or want to know how to implement agentic coding in your company, check out our services.