Cursor AI Review 2025: Is It the Best AI Code Editor? (I Used It for 90 Days)

I’ll be honest — I was skeptical. I’d been using VS Code with GitHub Copilot for nearly two years and considered it the gold standard for AI-assisted development. When colleagues started raving about Cursor AI, I assumed it was just another hyped tool that would fade within a few months. Then I actually spent 90 days building real projects with it — a Python FastAPI backend, a React dashboard frontend, and a Django-based SaaS prototype — and my opinion changed completely. This isn’t incremental improvement. Cursor AI represents a fundamentally different way of writing code.

Over those 90 days, my workflow transformed in ways I didn’t expect. I reduced boilerplate writing by roughly 60%, eliminated repetitive copy-paste cycles for CRUD operations, and — in one memorable session — completed a fully functional 200-line CRUD API with authentication middleware in under 20 minutes. That last achievement genuinely surprised me. What used to be a 90-minute task involving documentation lookups, Stack Overflow dives, and manual wiring became a fluid conversation with an AI that actually understood my codebase context, not just the file I had open.

For this Cursor AI review, I’m going deep. I’m not going to just list features — I’ll tell you exactly how they performed under real project pressure, where the tool fell short, and whether the $20/month Pro plan is worth it compared to GitHub Copilot’s equivalent tier. I’ll also be transparent about the learning curve, the privacy considerations, and who this tool is genuinely best suited for. If you’re evaluating AI code editors in 2025, this is the review you need to read before making a decision.

One thing became clear almost immediately: Cursor AI is not just a plugin or extension bolted onto an existing editor. It’s a ground-up rethinking of what an AI-native IDE should look like. The context awareness alone — the fact that the AI can reference your entire project, not just your current file — puts it in a different category than most competitors. Whether that’s enough to justify switching from your current setup depends on your workflow, your stack, and your budget. Let’s break it all down.

What Is Cursor AI?

Cursor AI is an AI-native code editor launched in 2023 by Anysphere, a small but well-funded AI research company. Rather than building an editor from scratch, the Cursor team made a strategically smart decision: they forked Visual Studio Code. This means Cursor inherits the full VS Code extension ecosystem, keyboard shortcuts, settings, and UI conventions that millions of developers already know. If you’ve used VS Code — and statistically, you probably have — you can migrate to Cursor in under five minutes without relearning anything. All your themes, your keybindings, your favorite extensions like ESLint, Prettier, and GitLens work out of the box.

What makes Cursor different from “VS Code with a Copilot plugin” is the depth of AI integration. Cursor uses a rotating panel of frontier AI models — primarily Claude Sonnet (Anthropic) and GPT-4o (OpenAI) — and allows you to switch between them depending on the task. Claude Sonnet tends to excel at long-context reasoning and large codebase refactors, while GPT-4o is fast and excellent for quick completions and SQL queries. Cursor also supports its own fine-tuned Tab completion model, optimized specifically for code autocomplete latency. Beyond the models, Cursor introduces architectural features that don’t exist in any plugin ecosystem: Codebase Indexing, Composer for multi-file editing, Agent Mode for autonomous task execution, and the .cursorrules file system for injecting persistent project-level context into every AI interaction.

Cursor AI Features: A Deep Dive

Cursor’s feature set is layered — you can use it superficially as a smarter autocomplete tool, or you can go deep and let it autonomously build features across your entire codebase. Here’s an honest breakdown of each major capability.

Tab Autocomplete (Cursor Tab)

Cursor Tab is the baseline autocomplete layer, and it’s noticeably better than GitHub Copilot’s standard inline suggestions in one critical way: it uses multi-line diff awareness. When you start editing partway through a function, Cursor Tab doesn’t just predict what comes next — it predicts the full set of changes needed across that block, offering up to a dozen lines of suggested edits simultaneously. This is particularly powerful when refactoring: change a function signature, and Cursor Tab will suggest updated call sites below in the same file.

In practice, Tab completions feel snappier than Copilot’s because Cursor uses a custom-trained, lighter model specifically for this task rather than routing everything through a large language model. There’s noticeably less latency on first keystroke. Over 90 days, I accepted roughly 65-70% of Tab suggestions without modification, compared to around 50% with Copilot in my prior setup. It’s a small number but it compounds into significant time savings across a full workday.

AI Chat (Cmd+L) — Inline Chat with Codebase Context

Pressing Cmd+L opens the AI chat panel, which at first glance looks like a standard ChatGPT window embedded in your editor. The difference is context. By default, Cursor includes your currently open file in the conversation, but you can also pull in other files, documentation, web URLs, or even terminal output using @ mentions (covered below). You can ask the AI to explain a block of legacy code, suggest a refactor, debug an error message you’ve pasted directly from your terminal, or write tests for a function you’ve just written.

What makes this genuinely useful rather than gimmicky is that the responses are actionable. Cursor Chat shows a diff view for any suggested code changes, and you can apply them with a single click without manually copying and pasting. During my FastAPI project, I used Cmd+L constantly to ask things like “why is this Pydantic model throwing a validation error for nested objects?” and received contextually accurate answers because the AI could see the model definition I was working on, not a decontextualized snippet I’d pasted into a separate chatbot window.

Composer (Cmd+I) — Multi-File Edits

Composer is where Cursor starts to feel genuinely next-generation. While Chat handles conversation and single-file suggestions, Composer is designed for coordinated changes across multiple files simultaneously. Press Cmd+I, describe what you want to build or change, and Cursor plans and executes edits across your entire project structure — creating new files, modifying existing ones, updating imports, and wiring components together.

I used Composer extensively when adding authentication to my FastAPI project. I described what I needed: JWT-based auth middleware, a users table migration, login and refresh token endpoints, and a dependency injection setup. Composer opened a multi-file diff showing changes to main.py, a new auth/router.py, a new models/user.py, and an updated requirements.txt. All I did was review the diff and click Accept. What would have been 45 minutes of careful wiring took about eight minutes. This is the feature that most dramatically changes the economics of software development when used well.

Codebase Indexing — Semantic Search Across Your Project

When you open a project in Cursor, it indexes your entire codebase using vector embeddings — essentially a semantic map of your code that allows the AI to retrieve relevant functions, classes, and patterns based on meaning rather than just keyword matching. This indexing runs locally and is stored on Cursor’s servers (with privacy mode options available on the Business plan).

In practice, this means you can ask “find all places where we manually construct user objects instead of using the factory function” and Cursor will accurately surface those locations across hundreds of files — something grep cannot do reliably because it requires understanding intent, not just text. The indexing was most valuable during code reviews and when onboarding to the Django SaaS project, which had a large legacy codebase. Instead of spending hours tracing data flows manually, I used Chat with codebase context enabled to ask architectural questions and got accurate, grounded answers in seconds.

@ Mentions and Context Controls

Cursor’s @ mention system is its context control mechanism. In any Chat or Composer session, you can type @ to reference specific files (@auth/router.py), folders (@/models), documentation URLs (@https://docs.pydantic.dev), terminal output (@Terminal), Git history (@Git), or even previously defined cursor rules. This gives you precise control over what context the AI is working with, which directly impacts response quality.

The .cursorrules file deserves special mention. Placed at the root of your project, this plain-text file contains persistent instructions that are automatically injected into every AI interaction — your code style preferences, your folder conventions, which libraries you prefer, what error handling patterns to follow. Think of it as a project-level system prompt. For teams, this is transformative: everyone working in the repo gets AI assistance that follows the same conventions automatically, without anyone having to re-explain the rules each session. Cursor also supports MCP (Model Context Protocol) tools, allowing you to connect external data sources and APIs directly into the AI’s context window.

Agent Mode — Autonomous Multi-Step Coding

Agent Mode is Cursor’s most ambitious feature and, arguably, its most impressive. Rather than responding to a single prompt and stopping, Agent Mode allows Cursor to autonomously plan and execute a sequence of coding tasks — running terminal commands, reading file outputs, making iterative edits, checking for errors, and course-correcting — all without requiring you to confirm each step. You describe a goal, and the agent works toward it.

I tested Agent Mode by asking it to “set up a complete test suite for the auth module using pytest, including fixtures for a test database and mocked JWT tokens.” The agent created a tests/ directory, scaffolded the fixture files, wrote 14 test cases, installed the necessary test dependencies, ran pytest, caught two failing tests caused by import errors in its own generated code, fixed them, and re-ran the suite — all autonomously. The final result passed 12 of 14 tests on the first fully human-reviewed run. For a complex, multi-step task, that’s an extraordinary hit rate. Agent Mode does occasionally get stuck in loops on ambiguous tasks, so it works best with clear, bounded goals rather than vague directives.

Real-World Performance: 90 Days of Testing

Testing Cursor in controlled conditions tells you very little. What matters is how it holds up under real deadline pressure, with messy legacy code, unclear requirements, and the kind of non-obvious bugs that don’t have Stack Overflow answers. Across three projects — a Python FastAPI microservice, a React + TypeScript dashboard consuming that API, and a Django-based SaaS prototype — Cursor’s performance was consistently strong, with some notable caveats.

On the Python FastAPI project, Cursor saved significant time in two specific areas: endpoint scaffolding and async database session management, both of which involve repetitive patterns that are tedious to write correctly from memory. Cursor’s completions were accurate for SQLAlchemy async patterns about 80% of the time, with the remaining 20% requiring minor corrections — mostly around edge cases with relationship loading strategies. Compared to Copilot, which I’d used on a similar project the previous quarter, Cursor was faster to accept (less latency), more often contextually correct (because it could reference the project’s existing models), and far better at multi-file edits. Copilot simply doesn’t have an equivalent to Composer.

On the React TypeScript frontend, Cursor Tab’s performance shone brightest. TypeScript’s verbose typing requirements mean a lot of mechanical typing, and Cursor Tab eliminated almost all of it — correctly inferring prop types from component usage, suggesting accurate interface definitions, and even predicting CSS-in-JS patterns based on existing styled-component conventions in the codebase. I estimated a 55% reduction in keystrokes on TypeScript-heavy files. The Chat feature was also invaluable for debugging TypeScript compiler errors, which can be notoriously cryptic — Cursor explained them clearly and suggested targeted fixes faster than I could manually parse the error messages. If you’re comparing AI models for development tasks, our analysis of ChatGPT vs Claude vs Gemini is worth reading alongside this review to understand the underlying model differences.

Where Cursor fell short: very large repositories (500,000+ lines) can slow indexing noticeably, and Agent Mode occasionally makes overconfident architectural decisions on ambiguous prompts. It’s also worth noting that Cursor’s AI suggestions reflect training data biases — it tends to suggest older library versions or deprecated patterns occasionally, particularly for fast-moving frameworks like Next.js. You still need to review AI output critically. The tool makes you faster; it doesn’t make careful engineering unnecessary.

Cursor AI Pricing

Cursor’s pricing is straightforward and, especially at the Pro tier, excellent value for professional developers. Here’s the full breakdown as of 2025:

The Hobby tier is genuinely useful for evaluation — 2,000 completions per month is enough for a few weeks of serious development. The Pro plan at $20/month is the sweet spot for individual developers: unlimited completions mean you never hit a productivity wall mid-sprint, and access to Claude Sonnet for Composer and Agent Mode tasks is what unlocks Cursor’s full potential. The Business plan’s killer feature is Privacy Mode, which ensures your code is never stored on Cursor’s servers or used for model training — a must-have for enterprise teams, agencies, or anyone working with proprietary codebases.

Cursor AI vs GitHub Copilot vs Tabnine

The AI code editor market is crowded, but realistically most developers are choosing between Cursor, GitHub Copilot, and (for teams with data-sensitivity needs) Tabnine. Here’s how they stack up head-to-head:

The narrative here is nuanced. GitHub Copilot remains an excellent tool, particularly if your workflow is tightly integrated with GitHub Pull Requests, GitHub Actions, and GitHub Issues — its contextual PR descriptions and code review assistance are genuinely valuable features Cursor doesn’t match at the repository management level. However, for the actual act of writing and editing code, Cursor’s Composer and Agent Mode represent a genuine generational leap. Copilot’s inline suggestions are good; Cursor’s multi-file orchestration is transformative. If you’re spending more than 60% of your time writing code (rather than managing repositories), Cursor wins decisively.

Tabnine occupies a different niche. Its on-premise deployment option means enterprise clients can run the AI model entirely within their own infrastructure, which is a non-negotiable requirement in sectors like finance, healthcare, and defense contracting. If data sovereignty is your primary concern and you’re willing to sacrifice raw capability for it, Tabnine is your answer. For everyone else, Cursor’s Business plan Privacy Mode provides a meaningful middle ground. We cover the full competitive landscape in our guide to the best AI coding assistants in 2025 if you want a broader comparison.

Cursor AI: Pros and Cons

Who Should Use Cursor AI?

Not every tool is right for every developer, so let me be specific about who gets the most value from Cursor AI based on 90 days of hands-on use across different project types and team configurations.

Solo SaaS developers and indie hackers are probably Cursor’s single strongest use case. When you’re building alone with no one to pair with, no senior developer to review your architecture, and a finite runway to ship, Cursor functions as a tireless technical co-pilot. The combination of Composer, Agent Mode, and codebase-aware Chat compresses the time from idea to working prototype dramatically. I built a Django SaaS skeleton — authentication, billing integration hooks, multi-tenancy structure, and basic admin — in two days of focused work that I estimate would have taken a week solo without AI assistance.

Full-stack developers working in Python, TypeScript, or React will find Cursor’s AI particularly well-trained on these stacks. The autocomplete accuracy is highest here, the type inference is excellent, and the framework-specific patterns (FastAPI dependency injection, React hooks, Next.js routing conventions) are well understood. Students and bootcamp graduates benefit enormously from Cursor’s Chat explanation features — it’s an always-available senior developer who can explain why something works, not just what to write. Engineering teams with the Business plan benefit from the .cursorrules standardization, which enforces code style and architectural patterns across the entire team’s AI-assisted output. This is a genuine quality-control mechanism, not just a convenience.

How to Get Started with Cursor AI

Getting up and running with Cursor takes less than ten minutes, even if you’ve never used it before. Here’s the step-by-step process:

Step 1: Download and Install. Visit cursor.com and download the installer for your operating system (macOS, Windows, or Linux are all supported). The installer is a standard executable — no complex setup required.

Step 2: Import Your VS Code Settings. On first launch, Cursor will prompt you to import your existing VS Code configuration, including extensions, themes, keybindings, and settings. Click “Import from VS Code” and your environment will be replicated instantly. If you’re not a VS Code user, you can set up a fresh environment using the built-in extension marketplace.

Step 3: Create a Free Account or Start Your Pro Trial. Sign up with your email or GitHub account. The Hobby (free) tier activates immediately. If you want to evaluate Pro features — and you should — Cursor offers a 14-day Pro trial. Enter your payment information and your trial begins. You won’t be charged until the trial ends.

Step 4: Open Your Project and Allow Codebase Indexing. Open your project folder in Cursor. You’ll see a prompt asking permission to index your codebase. Accept it. Indexing happens in the background and typically completes within a few minutes for projects under 50,000 lines. Once indexed, the AI can reference your entire project in Chat responses.

Step 5: Create Your .cursorrules File. In the root of your project, create a file named .cursorrules. Add your project-specific instructions: preferred framework versions, naming conventions, error handling patterns, testing requirements, and any constraints you want the AI to respect. This single file dramatically improves the relevance of all AI output in this project.

Step 6: Try Composer on Your First Real Task. Press Cmd+I (or Ctrl+I on Windows) to open Composer. Describe a self-contained coding task — “add input validation to all API endpoints and return standardized 422 error responses” — and watch Cursor plan and execute the multi-file changes. Review the diff carefully, accept what’s right, and reject or modify what isn’t. This first Composer session will immediately demonstrate whether Cursor’s value proposition works for your codebase.

Frequently Asked Questions about Cursor AI

Is Cursor AI free?

Yes, Cursor offers a genuinely functional free Hobby tier that includes 2,000 code completions per month and 50 slow premium AI requests. This is enough for casual users and developers who want to evaluate the tool before committing to a paid plan. The free tier does not include unlimited completions, fast request priority, or full Claude Sonnet access, which are gated behind the Pro plan. For serious daily development use, most developers will need the $20/month Pro plan within a few weeks of adoption.

Is Cursor AI better than GitHub Copilot?

For most developers focused on writing and editing code, yes — Cursor AI is more capable than GitHub Copilot in 2025. The decisive advantages are multi-file editing via Composer, Agent Mode for autonomous task execution, and codebase-aware chat that pulls context from your entire project rather than just the current file. Where GitHub Copilot maintains an edge is in GitHub ecosystem integration: pull request descriptions, code review suggestions, and GitHub Actions integration are tightly linked to GitHub workflows that Cursor doesn’t replicate. If you live inside GitHub’s platform, Copilot’s ecosystem alignment may outweigh Cursor’s raw code generation power.

Which programming languages does Cursor support?

Cursor supports every programming language that VS Code supports, which effectively means all major languages: Python, JavaScript, TypeScript, Rust, Go, Java, C++, C#, PHP, Ruby, Swift, Kotlin, SQL, HTML/CSS, and dozens more. The AI quality varies by language — Python, TypeScript, and JavaScript receive the best completions because they’re well-represented in training data. Less mainstream languages like Elixir or Zig work correctly but with lower AI suggestion accuracy. Infrastructure-as-code files (Terraform, Dockerfile, YAML) are handled well, making Cursor useful for DevOps workflows in addition to application development.

Is Cursor AI safe? Does it send my code to the cloud?

On the Hobby and Pro plans, code snippets are sent to Cursor’s servers and to the underlying AI model providers (Anthropic and OpenAI) to generate responses. Cursor states that it does not use your code to train its models and that data is handled according to its privacy policy. For developers working on proprietary, confidential, or regulated codebases, the Business plan’s Privacy Mode is the appropriate choice — it ensures your code is never stored on Cursor’s or the model providers’ servers. Always review Cursor’s current privacy policy directly on their website and consult your organization’s data governance requirements before use.

Can Cursor AI write entire apps from scratch?

Cursor can dramatically accelerate building applications from scratch, particularly for standard patterns and well-defined requirements, but “writing entire apps” with zero human oversight overstates its current capability. Using Composer and Agent Mode, I’ve scaffolded full-stack applications including models, API routes, authentication, and frontend components much faster than building manually. However, the AI makes architectural assumptions that may not align with your specific requirements, occasionally introduces subtle bugs in complex business logic, and doesn’t have design judgment. Think of it as an extraordinary accelerant for experienced developers, not a replacement for engineering expertise.

Does Cursor work with TypeScript, React, and Python?

Exceptionally well. These three are arguably Cursor’s strongest supported environments. TypeScript support is particularly impressive — Cursor Tab correctly infers complex generic types, suggests accurate interface definitions, and understands framework-specific patterns in Next.js, tRPC, and Zod. React support includes hooks, context patterns, component composition, and styled-component conventions. Python support is excellent for both web frameworks (FastAPI, Django, Flask) and data science workflows (pandas, NumPy, PyTorch). If your stack is Python backend + React/TypeScript frontend, Cursor was practically built for you.

Is Cursor AI worth $20/month?

For professional developers, the ROI calculation is straightforward. If Cursor saves you even one hour of development time per month — which is a very conservative estimate given real-world productivity gains — you’re well ahead at $20/month. In my own testing, I estimate Cursor saved between 8 and 12 hours per month across the projects I worked on, primarily from faster boilerplate generation, multi-file editing, and reduced debugging time. At even a modest billing rate, the tool pays for itself many times over. The free tier exists specifically to let you verify this claim with your own codebase before spending anything.

How is Cursor different from VS Code with Copilot?

This is the most common question from developers evaluating Cursor, and the answer is more than “it’s faster.” VS Code with GitHub Copilot gives you inline autocomplete suggestions and a basic Chat panel — powerful features, but ones that operate file-by-file without broader codebase awareness. Cursor, built on VS Code’s foundation, adds layers that don’t exist in the plugin model: Composer creates coordinated multi-file edits in a single operation; Codebase Indexing gives the AI semantic knowledge of your entire project; Agent Mode enables autonomous multi-step execution; .cursorrules files inject persistent project context; and model switching lets you choose Claude Sonnet or GPT-4o based on task type. It’s the difference between a smart autocomplete tool and an AI that understands your codebase as a system.

Final Verdict: Cursor AI in 2025

After 90 days of real-world use across Python APIs, React frontends, and a Django SaaS prototype, my verdict on Cursor AI is unambiguous: it’s the most capable AI code editor available in 2025, and it represents a genuine inflection point in how software is built. The combination of multi-file Composer editing, codebase-aware chat, Agent Mode autonomy, and the VS Code foundation makes it simultaneously the most powerful and least disruptive AI coding tool in the market. You get frontier AI capabilities without relearning your editor.

The limitations are real but manageable. Privacy-sensitive teams should budget for the Business plan. Developers working in very large monorepos should test indexing performance before committing. Agent Mode requires clear, bounded instructions to perform reliably. None of these are dealbreakers — they’re calibration points for setting appropriate expectations. The core productivity gains are undeniable, and for most developers, they dwarf the cost and the caveats.

If you’re a developer who also creates content — technical blog posts, documentation, tutorials — Cursor solves the code side of your workflow. For the writing and SEO side, tools like Surfer SEO complement Cursor beautifully by optimizing your technical content for search visibility, while NeuronWriter provides AI-powered content briefs and NLP optimization for developer-bloggers who want their tutorials and reviews to rank. For developers writing product documentation, technical guides, or marketing copy, Jasper AI offers a purpose-built writing assistant that integrates smoothly into a content workflow alongside your code editor.

Ready to go deeper on AI coding tools? Our comprehensive guide to the best AI coding assistants in 2025 covers the entire landscape — from Cursor and Copilot to specialized tools for data science, embedded systems, and enterprise development. Whether you’re just getting started with AI-assisted development or evaluating a team-wide switch, it’s the resource we’d recommend reading next.