From the moment I installed it, Windsurf felt familiar yet different. Familiar because it looks and behaves a lot like VS Code, and different because the AI (called Cascade) is always there, actively guiding the workflow.
That means instead of jumping between ChatGPT, Stack Overflow, and VS Code, you get an editor that understands your project, suggests changes, fixes errors, and even generates multi-file edits with context awareness.
In this Windsurf.ai code editor review, I’ll walk you through my hands-on experience by building a real Django app.
What Is Windsurf AI?
Unlike traditional IDEs or simple autocomplete tools, it’s designed to understand your entire codebase, anticipate your intent, and help you stay in “flow” while coding.
At the core of Windsurf.ai is Cascade, its intelligent agent that completes lines of code and thinks several steps ahead, refactoring across multiple files, fixing errors, generating documentation, or even running terminal commands when needed. Paired with Windsurf Tab, you can trigger powerful actions with a single keystroke, from importing dependencies to building features instantly.
Who is Windsurf.ai For?
Windsurf is built for:
- Individual developers and freelancers who want to ship projects quickly with AI assistance.
- Startup teams and agencies that need to maximize output without hiring huge engineering teams.
- Enterprises managing complex applications, where consistency, collaboration, and speed are critical.
Pros and Cons of Windsurf AI
- Code-first approach with no vendor lock-in
- Automated linting and error fixing built-in
- Context-aware debugging with proactive suggestions
- Integrates PostgreSQL and REST Framework seamlessly
- Editable, production-ready code for full control
- Hot reloading accelerates test and debug cycle
- No drag-and-drop visual editor
- Beginners face steeper learning curve
- Some manual fixes still required
Windsurf Features
- AI agent Cascade with deep context awareness
- Multi-file editing with coherent suggestions
- Real-time collaboration between humans and AI
- Intelligent indexing for semantic code understanding
- Tab key for actions and navigation
- Automated lint detection and error fixing
- Support for custom MCP tools and servers
- Drag and drop images to generate code
- Terminal integration with AI-guided commands
- Continue my work with the saved context
- Turbo mode for auto-executing commands
- Built in memory of the codebase and workflow
How Does Windsurf work?
Many AI app builders are built around no-code or drag-and-drop interfaces, where you type a prompt and instantly get a simple app prototype.
However, Windsurf is:
- Purpose-built AI IDE: The tool is designed as an AI Integrated Development Environment (IDE) that feels much closer to VS Code or JetBrains than to a visual app builder.
- Increases productivity: The goal is to work alongside developers (not replace coding), to help them understand their entire codebase and handle repetitive or boilerplate tasks so they can stay in flow.
- Hands-on Approach: Using Windsurf requires active coding. You don’t just click a button and get an app. You set up projects, write code, and collaborate with Windsurf’s AI agent, Cascade, in real time.
- Advanced AI capabilities: Cascade claims to anticipate developers’ intent, manage multi-file edits, and even run terminal commands in a single editor. Hence, the real test is not whether it can “spit out an app,” but how smooth and intelligent the coding experience feels when you’re actually working with it.
With that in mind, I started my review exactly where every developer’s journey begins: signing up and taking my very first steps inside Windsurf.
My Hands-On Experience with Windsurf AI: A Step-by-Step Guide
Here’s a step-by-step look at Windsurf, from my perspective as a developer. I’ll break down the highs, the lows, and the rough edges of the platform.
By the end, you’ll have a clear sense of how Windsurf works, and whether it’s worth your time or not.
Getting Started & Signing Up
To get started, I visited the tools’ official website – windsurf.com. Here, there was a prominent green “Download for Windows” button that stood out against the dark background.
Right next to it was a gray “Explore Features” button and, in the top navigation, another green “DOWNLOAD” option.
My first thought: Straightforward, and it’s quite easy to follow the process.
I clicked on the download button, and once the installer finished downloading, I ran it.
The process was standard: accept the license, pick an installation path, let it do its thing. I didn’t have to fuss over extra options like desktop shortcuts. Windsurf handled those automatically.
A few clicks later, I was ready to launch the app.
Signing Up on Windsurf’s Desktop App: Process & Navigation
Opening Windsurf for the first time brought me into a guided setup flow. The first screen asked if I wanted to import settings from VS Code or start fresh. I chose “Start fresh” because I wanted the pure Windsurf experience.
Next came keybinding preferences — VS Code or Vim. I stuck with VS Code defaults since that’s my comfort zone.
After that, I was presented with a grid of theme options ranging from Dark and Solarized to Tokyo Night and Quiet Light. I went with Dark, since that’s what I normally use when coding. These early personalization options stood out to me as thoughtful. Windsurf clearly knows developers care about these details.
The final step in setup was authentication. A message on-screen read: “Authentication grants free access to Windsurf’s AI features.” At this point, I had the option to create an account by email or use social login with Google or GitHub. No mention of a credit card — a big win in my book.
I clicked Sign up with GitHub, went through the familiar OAuth authorization, and in seconds I was back in the app with a friendly greeting.
The main screen had a “Getting started with Windsurf” checklist, buttons to Open Folder, Generate a New Project, or Clone Repository, and on the right, a big pane introducing Cascade Code, Windsurf’s AI engine.
What really stood out was the status bar at the bottom: “Surf’s up! You have 15 days remaining in your Windsurf Pro trial” on the left, and on the right, “Ask anything (Ctrl + I)” powered by Claude Sonnet 4. Clear, transparent, and no hidden catches.
Building My First App with Windsurf AI
To put Windsurf through its paces, I decided to build a Django-based personal finance dashboard. This wasn’t a trivial “Hello World” app. I wanted authentication, a PostgreSQL backend, a REST API with Django REST Framework, TailwindCSS for styling, and a usable interface with charts and actions.
In other words, something that would stress-test the AI’s ability to handle both backend and frontend complexity.
- Prompt Setup
When I clicked “Generate a New Project” from the dashboard, Windsurf prompted me with a text box to describe what I wanted. This is where the magic begins. Instead of manually setting up virtual environments, installing dependencies, and wiring everything together, I simply typed:
Create a new Django project called finance_dashboard.
Set it up with Django REST Framework, PostgreSQL, and TailwindCSS integration for the frontend templates.
2. How Cascade Translates Intent Into Action
Immediately, Windsurf’s AI agent, Cascade, broke my request into clear steps. On the right sidebar, I could see the plan: create the project directory, generate requirements.txt with dependencies, configure PostgreSQL, install Django REST Framework, and set up TailwindCSS.
Each task came with a “Run All” button, so I could let the AI execute the terminal commands directly. This breakdown impressed me. It wasn’t just spitting out code, it was reasoning about the process and giving me visibility into every step.
3. Dependency Control
When it generated requirements.txt, I saw all the right packages: Django 4.2, djangorestframework, psycopg2-binary for Postgres, corsheaders, whitenoise for static files, and even python-decouple for environment variables. I had the option to ‘Accept or Reject’ the file before it was written, which gave me control.
After clicking Accept all, Windsurf ran pip install -r requirements.txt right inside the integrated terminal. Watching dependencies install automatically while the agent ticked off tasks one by one felt like having a junior developer handle the boilerplate while I supervised.
4. Configuration Settings
Next, it tried to scaffold the Django project with django-admin startproject finance_dashboard. Here, I hit my first hiccup — a conflicting manage.py file already existed from an earlier attempt.
Instead of failing silently, Windsurf diagnosed the issue, confirmed Django was installed, and adjusted by continuing with the existing structure. This was a real-world moment. I’d made a mess locally, and the AI recovered gracefully.
Once the project was confirmed, Windsurf opened settings.py and highlighted changes it wanted to make: adding REST Framework and corsheaders to INSTALLED_APPS, switching the database from SQLite to Postgres, and setting up environment-based credentials. It even preconfigured REST_FRAMEWORK settings like authentication and pagination. I clicked Accept all, and just like that, my settings were production-grade.
TailwindCSS integration came next. Windsurf created package.json, added Tailwind, PostCSS, and Autoprefixer, generated tailwind.config.js with sensible defaults, and wired everything into a base.html template. It even created a .env.example file for environment variables and a README.md with detailed setup instructions.
Every time, I had the chance to review changes, accept them, and watch the IDE handle the commands. This back-and-forth kept me in control while still saving huge amounts of time.
5. Structured Roadmap
Finally, Windsurf summarized everything it had done: Django project created, Postgres configured, REST Framework integrated, TailwindCSS set up, and README ready.
After Windsurf finished scaffolding my project, it didn’t just leave me hanging. It actually gave me a full guided summary of what it had done and what I needed to do next. The AI agent popped up with a message that read:
It then laid out a checklist of everything it had completed:
- Django Project Structure – finance_dashboard with proper directories.
- Django REST Framework – already configured with token/session authentication, pagination, and CORS.
- PostgreSQL Integration – database settings tied to environment variables.
- TailwindCSS Integration – package.json dependencies, a configured tailwind.config.js, a CSS build pipeline (npm run build-css), custom components, and a base template with modern responsive design.
- Project Files – requirements.txt, .env.example, and a detailed README.md.
Then it handed me a step-by-step roadmap for getting the project running:
- Install dependencies (pip install -r requirements.txt and npm install)
- Set up environment (copy .env.example .env)
- Build TailwindCSS (npm run build-css)
- Run migrations, create a superuser, and start the server
This kind of structured handoff really impressed me.
6. Testing Flexibility Beyond the Initial Setup
But here’s where it got even more interesting. For my testing, I decided I didn’t want PostgreSQL at first. I wanted to see if I could switch the database back to SQLite, just to test Windsurf’s flexibility. So I went into settings.py and made that change. To my surprise, Windsurf immediately detected it.
7. Built-In Change Tracking Across Files
A panel popped up telling me exactly which files I had modified:
- settings.py (+38, -2 changes)
- .env.example (+1, -1)
- urls.py (+1, -1)
The fact that it tracked my edits across multiple files and presented a clear diff was a game-changer. It felt like working with a version control assistant built right into the IDE, keeping an eye on consistency and helping me stay confident about my changes.
This moment really stood out to me because it proved Windsurf isn’t just an app generator. It’s an IDE that actually understands and responds to your workflow, even when you deviate from the AI’s initial setup.
8. First Run
After confirming my changes, I wanted to see the app in action. So I went ahead and ran the command:
python manage.py runserver
The terminal spun up, applied migrations, and gave me the familiar message: “Starting development server at http://127.0.0.1:8000/”. I clicked the link, and in my browser, I was greeted by the freshly created Finance Dashboard.
Early Verdict: My Honest Take on Windsurf’s App Building Process
At the beginning, it looked surprisingly polished for something scaffolded by AI. The header displayed a bold “Finance Dashboard” title on the left, while the right side had authentication links — a clear indication that Django’s built-in auth system was already wired in.
Below that, the main dashboard area presented summary cards showing Total Balance, Income, and Expenses, each styled with TailwindCSS colors (blue, green, red) to instantly convey meaning.
Overall, the generated app is that it struck the right balance between functionality and presentation. The UI was clean, responsive, and modern thanks to TailwindCSS. The backend was wired with Django REST Framework and ready for data. And everything tied together in a way that felt like a serious starting point for a real project, not just a demo.
Customizing the Design and Layout
After getting my Finance Dashboard app up and running, I wanted to push Windsurf beyond simple scaffolding. Building a project from a single prompt is impressive, but the real question for me was: how flexible is it when I want to change or extend the app? That’s where the true depth of any AI-powered IDE shows itself.
To test this, I gave Windsurf a very specific customization request:
I wanted to see if Windsurf could handle not just backend logic, but also generate the templates, styling, and routing that tie everything together into a seamless user experience.
Once I entered the prompt, Windsurf’s AI agent got to work immediately. What impressed me was that it didn’t just spit out snippets in isolation. Instead, it broke the customization into a structured workflow:
- Create a new Django app (accounts) dedicated to authentication.
- Generate the necessary views (login, signup, logout, profile).
- Wire up URL routes in urls.py.
- Generate frontend templates styled with TailwindCSS.
- Update the global layout (base.html) so the navigation automatically reflects whether a user is logged in or not.
And here’s where Windsurf’s strength really shows: all of these steps were executed directly in code, not through a black-box visual interface.
Inside the new accounts app, Windsurf created a forms.py file and set up custom form classes like CustomUserCreationForm and UserProfileForm.
When I looked closely, I saw that the AI had also integrated TailwindCSS styling at the Python level. For each form field, it added lines like this:
field.widget.attrs.update({
‘class’: ‘form-input mt-1 block w-full rounded-md border-gray-300 shadow-sm
focus:border-primary-500 focus:ring focus:ring-primary-500 focus:ring-opacity-50′
})
This is a clever approach. Instead of hardcoding classes into every HTML template, Windsurf applied them programmatically in Python. That meant every form in the project would automatically have a consistent design system without me touching the templates one by one.
In views.py, Windsurf defined class-based views for login, signup, logout, and profile. Each one pointed to a corresponding template:
- login.html
- signup.html
- profile.html
And when I opened these templates, I could see Tailwind’s utility classes everywhere, from buttons styled with bg-blue-600 text-white to responsive input fields using w-full and rounded-md.
The navigation bar in base.html was also updated dynamically. If a user wasn’t logged in, it showed Login and Sign Up. If they were, it displayed Welcome, [username] plus Profile and Logout.
To make the authentication templates look polished, Windsurf didn’t just drop in default styling. It also updated tailwind.config.js and input.css.
- In tailwind.config.js, it defined custom colors (primary, success, warning, danger) and added plugins like @tailwindcss/forms.
- In input.css, it set up reusable components with @apply, so classes like .btn-primary or .card could be reused across multiple templates.
This gave me granular control over the look and feel. If I wanted to rebrand the entire app, I could change the primary color in one place, recompile, and instantly have a new theme applied across all authentication pages.
What stood out to me here was the depth of collaboration possible between the user and the AI. Windsurf didn’t lock me into a visual builder or force me to accept one “look.” It gave me well-structured Django code, styled with Tailwind, and then left the door wide open for me to customize further.
- If I wanted to add validation rules to a form, I could just edit the Python class.
- If I wanted a new button style, I could define it once in input.css and have it applied everywhere.
- If I wanted to change the layout of the navbar or profile page, I could open the HTML templates and adjust them directly.
How Windsurf AI Code Editor Handles Errors
I was curious: Does Windsurf just dump a stack trace in the terminal like any other IDE, or does it actually help you resolve issues?
Error 1: CommandError During Project Creation
While generating the Django project, Windsurf tried to run:
django-admin startproject finance_dashboard
The command failed because I already had a manage.py file from an earlier setup attempt:
CommandError: … manage.py already exists. Overlaying a project into an existing directory won’t replace conflicting files.
Normally, in VS Code or PyCharm, I’d be left staring at this error and deciding what to do manually. But Windsurf’s AI Agent immediately stepped in, acknowledged the problem, and suggested:
“Let me check if Django is installed and create the project structure manually.”
It ran python -m django –version to confirm the installation, then adapted by reading the existing manage.py file and continuing configuration manually.
Error 2: npm Critical Vulnerabilities
When installing Tailwind dependencies with npm install, the terminal flagged:
4 critical severity vulnerabilities
To address all issues, run: npm audit fix
This wasn’t a blocking error, but Windsurf surfaced it clearly in the terminal. While the AI Agent didn’t force a fix here, I could easily have asked it:
“Fix npm vulnerabilities”
and it would’ve run the recommended npm audit fix.
Error 3: Django Password Validation
While creating a superuser with:
python manage.py createsuperuser
I entered a weak password. Django instantly rejected it:
This password is too short.
This password is too common.
Bypass password validation and create user anyway? [y/N]:
Error 4: Missing API Root (404)
Once the app was running, I navigated to http://127.0.0.1:8000/api/ and got a 404 Page Not Found. Django’s debug page was clear: /api/ wasn’t mapped to any view. Normally, this is where you’d open urls.py and add the missing route manually.
Instead of diving straight into the fix, I added a quick comment in my urls.py file:
# Django error shows that http://127.0.0.1:8000/api/ doesn’t resolve to any view.
# So for the next task, we will resolve this issue.
Here’s where Windsurf completely surprised me. When I ran the current work, the AI Agent immediately picked up my comment and responded:
“Let me quickly fix the API URL routing issue and then run the migrations to set up the authentication system.”
This blew me away. Windsurf wasn’t just waiting for explicit prompts. It actually parsed my inline comments, understood what I meant, and turned them into actionable steps. In practice, this means I can guide the AI as if I’m pair-programming: drop a comment, and it treats it like a request.
The fix itself was seamless: Windsurf added the missing route in urls.py, linked it to Django REST Framework’s default URLs, and confirmed the server reload. Refreshing the browser immediately showed the browsable API page at /api/, complete with login and logout endpoints.
This moment really drove home that Windsurf is an interactive development partner that listens to you through both prompts and your comments in code.
What impressed me most was Windsurf’s blend of raw error visibility and intelligent AI guidance:
- It surfaces the full error output in the terminal, so nothing is hidden.
- It interprets common issues (like the CommandError) and offers recovery steps.
- It gives you the option to either fix things manually (if you know how) or lean on the AI to propose and apply fixes.
Publishing the App and Adding Integrations
It’s worth reminding you here that Windsurf isn’t like other AI app builders. There’s no glossy “publish to the cloud” button that hides all the moving pieces. Windsurf is an IDE, more like VS Code, but with an AI agent sitting beside you.
That means every deployment and integration is still happening at the code and configuration level. The difference is, Windsurf’s AI takes away the boilerplate and confusion, guiding you through steps that would normally eat up hours of setup time.
In my case, the Finance Dashboard was running locally on http://127.0.0.1:8000/. To publish this to the web, the usual process for a Django app would involve:
- Configuring the database (PostgreSQL in my case) with production credentials. Windsurf had already set this up in settings.py using environment variables — a best practice that made the transition from local to production straightforward.
- Handling static files. The AI had already configured STATIC_URL, STATIC_ROOT, and Tailwind’s build pipeline (npm run build-css), meaning the project was deployment-ready.
- Containerization or hosting setup. Windsurf’s AI was already structuring things in a way that could easily be containerized with Docker or deployed to platforms like Heroku, Render, or Railway. A simple prompt like “Create a Dockerfile for production deployment” is something I could ask directly in Windsurf, and it would scaffold the config for me.
Windsurf’s AI will guide you step by step through the exact commands and config needed for AWS, Heroku, or any other provider.
To test flexibility, I decided to go beyond the original prompt. After authentication and the dashboard were up and running, I wanted to see how easy it would be to bolt on an extra integration. Instead of clicking through menus, I stayed in the code and simply gave Windsurf a prompt like:
Add Stripe integration for payments: include API keys in settings.py, install dependencies, and create a checkout view and template.
What impressed me was how seamlessly Windsurf blended with my workflow:
- In settings.py, it added placeholders for STRIPE_SECRET_KEY and STRIPE_PUBLISHABLE_KEY, pulling them from environment variables instead of hardcoding.
- It proposed pip install stripe and added the dependency to requirements.txt.
- It generated a simple checkout view in views.py along with a corresponding checkout.html styled in TailwindCSS.
- In my urls.py, it created a new route for /checkout/, complete with boilerplate webhook handling.
All of this came in as code suggestions I could review, accept, or reject, not hidden magic. That means I had full control over the integration, and could refine or expand it exactly how I wanted.
Publishing and integrating in Windsurf feels like pair-programming with an expert developer. You still interact with code, just as you would in a traditional IDE, but the AI dramatically accelerates every step.
It takes care of Dockerfiles, environment variables, config boilerplate, and even third-party service wiring, while leaving you in control of the final implementation.
That balance (AI automation plus developer authority) is what makes Windsurf stand apart from point-and-click app builders. Instead of locking you into templates, it gives you production-ready, customizable code that you can deploy anywhere and extend with anything.
Windsurf Pricing & Plans
Windsurf’s pricing scales with your needs, starting with a generous free option and expanding into advanced team and enterprise features.
Instead of locking you in early, it lets you experiment before committing to a paid plan.
Here’s how the plans break down:
- Free – $0/month
- Pro – $15/month
- Teams – $30/user/month
- Enterprise – starting at $60/user/month
Payments are charged in USD, and while fees are generally non-refundable, you can set up automatic credit top-ups starting at $10.
Best Alternative to Windsurf AI
A primary alternative to Windsurf for AI-powered coding is Cursor. Cursor is an AI-first code editor, built as a fork of VS Code, and has quickly gained traction for its fast code generation and flexibility. While both tools lean heavily on AI, they serve slightly different types of developers.
To know which IDE tool is better between Windsurf vs. Cursor, check out the table below for better analysis.
Windsurf vs Cursor Overview
| Feature | Windsurf | Cursor |
|---|---|---|
| Best For | Large, complex codebases and teams needing deep context | Solo developers and small teams prioritizing speed and control |
| AI Agent | Cascade automates tasks with strong real-time awareness | Composer and Agent Mode with granular user-reviewed steps |
| Code Generation | Slower but highly accurate for large projects | Faster for boilerplate and rapid prototyping |
| Context Management | Automatic project-wide indexing using RAG for deep context | Manual context with @ symbols for precision |
| Model Access | Codeium models plus access to Gemini (some via API key) | Access to Claude 4, frontier models, and Max Mode (1M tokens) |
| Pricing (Pro) | $15/month for individuals | $20/month for individuals |
| Ease of Use | Beginner-friendly, minimal UI, automated workflows | Feature-rich, steeper learning curve, shows code diffs for review |
| Deployment | One-click deployment built into IDE | No native deployment, focused purely on coding |
Cursor vs Windsurf: Best Option for Beginners and Experienced Developers
Cursor is best for experienced solo developers and small teams who value speed, manual control, and access to a wide range of premium models. Its ability to rapidly generate boilerplate or isolated logic makes it excellent for prototyping or iterating quickly. Developers who want precision can use Cursor’s @ symbol approach to carefully select files for context, which ensures the AI only sees what they want.
Windsurf, on the other hand, excels for beginners and developers tackling large, complex projects. Its automatic project-wide indexing means you don’t have to constantly manage context, and the Cascade AI agent handles multi-file changes seamlessly. If you’re looking for an IDE that feels like it “just works” while keeping your focus on coding rather than AI micromanagement, Windsurf is the safer bet.
Final Verdict on Windsurf: Is it Worth Trying?
After testing Windsurf extensively, I found it powerful and a near-perfect tool for developers who want AI to work alongside them, not in place of them. If you’re a beginner looking for guidance, or an experienced developer working with complex, multi-file projects, Windsurf makes coding faster, smarter, and more enjoyable.
The way Cascade handles context, automates boilerplate, and even fixes errors feels like having a senior engineer sitting beside you.
The only caveat is that it’s still an IDE. You’ll be working with code, not drag-and-drop widgets. But for anyone serious about building real applications, that’s a strength, not a weakness.
If you’re considering whether to try Windsurf, my advice is simple: go for it. It’s one of the most capable AI development tools I’ve ever used.
