Windsurf or Cursor? I Used Both AI Editors: Which One for Whom

Recently, while integrating native packages on the Flutter side of an Android app for one of my side products, writing cross-platform widgets was quite time-consuming. For such repetitive and templated tasks, I wondered how AI-powered code editors could help me, and I decided to seriously test two popular tools on the market: Windsurf and Cursor. Both offer different approaches, and their integration into a developer's workflow creates significant differences in the value they provide.

AI-powered code editors promise to fundamentally change our software development process. They can provide significant productivity gains in areas such as code generation, debugging, refactoring, and even documentation creation. In this post, I will detail how I used these two tools in my own projects (both for the backend of an enterprise ERP and for my personal side products), which features stood out, and for whom they are more suitable.

Why Are AI-Powered Code Editors Important?

One of the biggest challenges we face in software development is writing repetitive, boilerplate code and the process of understanding existing code. AI-powered editors step in precisely at this point to alleviate this burden. In my opinion, they play a critical role not only in writing code but also in quickly understanding an existing codebase and making changes with confidence.

For someone like me, who has worked with various technologies for years, doing both system administration and software development, getting AI support when learning a new language or framework makes a huge difference. Especially when optimizing complex PostgreSQL queries or configuring an Nginx reverse proxy, the suggestions offered by AI allow me to reach the correct solution directly, instead of spending hours in documentation. In debugging processes, whether analyzing a journald output or trying to understand why a systemd unit isn't working properly, AI's ability to quickly suggest possible root causes significantly shortens problem-solving time.

💡 A Tip from My Experience

When using AI-powered editors, always make sure you understand the code generated by the AI and that it meets your expectations, rather than blindly copying it. Especially in security or performance-critical code snippets, combining AI's suggestions with your own experience is vital. I always double-check the generated output myself and improve it if necessary.

Cursor: An In-Depth Look and My Usage Experiences

Cursor was one of the editors that excited me when I first heard about it, because being VS Code-based meant I wouldn't deviate much from my existing habits. This was a big advantage for me, especially as a cross-platform developer. Cursor's strongest points were its integrated AI chat interface and its ability to understand the codebase.

When working on the backend of a production ERP, if I needed to develop a new feature, I could ask Cursor's AI questions by referencing my current file or even the entire project. For example, when I asked, "How do I add idempotency to this FastAPI endpoint?", Cursor provided me with both code examples and texts explaining why this pattern is important. Especially when implementing complex transaction outbox patterns, the initial drafts provided by AI formed a solid foundation I could build upon, rather than starting from scratch. However, sometimes Cursor tended to lose context; especially in refactoring tasks with complex dependencies from different files or modules, I had to guide the AI repeatedly. When optimizing a specific PostgreSQL query in a large codebase, Cursor usually offered good suggestions, but sometimes it struggled to automatically detect and resolve ORM pitfalls like N+1 query problems. In these situations, I had to manually examine EXPLAIN ANALYZE outputs and guide the AI more specifically.

# An example idempotency middleware draft from Cursor
from fastapi import Request, Response, status
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.types import ASGIApp

class IdempotencyMiddleware(BaseHTTPMiddleware):
    def __init__(self, app: ASGIApp):
        super().__init__(app)
        self.processed_requests = set() # In a real project, Redis or a database should be used

    async def dispatch(self, request: Request, call_next):
        idempotency_key = request.headers.get("X-Idempotency-Key")
        if idempotency_key:
            if idempotency_key in self.processed_requests:
                return Response(
                    "Request already processed",
                    status_code=status.HTTP_409_CONFLICT,
                )
            self.processed_requests.add(idempotency_key)
            # In a real system, the key should expire or be deleted

        response = await call_next(request)
        return response

# Example of adding to the application
# app.add_middleware(IdempotencyMiddleware)

This example was one of the starting points Cursor provided me. I then extended it with my own Redis or PostgreSQL-based idempotency key management.

Windsurf: A Next-Generation Approach and My Experiences

Although Windsurf is newer to the market, it caught my attention, especially with its AI model selection and RAG (Retrieval-Augmented Generation) capabilities. Instead of offering a ready-made integration like Cursor, Windsurf allows developers to choose their own AI models (with multi-provider fallback like Gemini Flash, Groq, Cerebras, OpenRouter) and define RAG sources (project documentation, web pages, etc.). This flexibility is invaluable, especially for those like me who know that a specific AI model performs better for a particular task.

I tested Windsurf, particularly in a client project, while migrating a complex legacy codebase to a modern event-sourcing architecture. Thanks to the RAG feature, I could feed the project's old documentation and specific business rules to the AI. This allowed the AI to provide suggestions enriched not only with general coding knowledge but also with the project's specific context. For example, when I asked, "How do I refactor the CRUD operations in this module according to event-sourcing principles?", Windsurf, by referencing the project's old IFRS integration documents, provided highly accurate suggestions on how events should be designed to maintain the consistency of financial records. This demonstrated a truly project-specific, in-depth understanding beyond Cursor's general context comprehension. As a disadvantage, Windsurf's initial setup and configuration of RAG sources required a bit more effort. However, this initial investment paid off handsomely with the high-quality, context-aware outputs received in later stages. Especially when working on the CQRS pattern, Windsurf's suggestions for separating command and query models helped me build the project's architecture on a more solid foundation.

This diagram roughly summarizes Windsurf's RAG-supported flow. When the developer asks a question, the Windsurf AI engine, via the selected AI model, retrieves relevant information from RAG sources (project documents, information pulled from the web, existing codebase), enriches the context with this information, and then generates the response. This offers much more than simply asking a general AI model a question.

Code Generation and Refactoring Capabilities: Who's Better?

Both Cursor and Windsurf boast impressive capabilities in code generation and refactoring. However, in this area, the quality of the AI models they use and the depth of their context understanding create significant differences between the two tools.

Code Generation:
Cursor is generally successful at producing quick and practical code snippets. It's quite useful for small functions, test cases, or boilerplate code conforming to a specific pattern. For example, when adding simple form validation to a Vue/React frontend component, Cursor's suggestions usually did the trick. However, when a larger and architecturally significant code block was needed (e.g., a complex PostgreSQL stored procedure or a FastAPI middleware), I sometimes had doubts about the level of detail or adherence to best practices in the AI-generated code. Especially in optimistic vs pessimistic lock scenarios, Cursor's examples often favored optimistic lock, but sometimes overlooked situations where pessimistic lock would be more appropriate.

Windsurf, on the other hand, thanks to its RAG capabilities, produced stronger output, especially for project-specific code generation. Since I could teach the AI my own code style and architectural preferences, the code it generated required fewer corrections. When bridging between Flutter and native code for an Android spam blocker app for a side product, by providing Windsurf with existing Android modules and Flutter method channel definitions, I was able to generate much more integrated and error-free native bridging code. This was an indication that the AI was working not only with general language knowledge but also with the specific structure and dependencies within the project. Retrieval-augmented learning made a real difference here.

Refactoring:
Refactoring is one of the areas where AI-powered editors show their true potential. Cursor is quite good at small to medium-scale refactoring tasks (e.g., renaming a variable, extracting a function). It can usually make such changes while preserving the meaning of the code. However, when it comes to larger architectural refactorings (e.g., splitting a monolith into microservices or event-sourcing integration), Cursor's suggestions can sometimes be superficial and struggle to ensure the consistency of the entire codebase. Especially when implementing complex distributed system patterns like eventual consistency or idempotency, I found that the AI-generated code did not correctly handle all side effects.

Windsurf, with its RAG-enhanced structure, offers a more holistic refactoring approach. Since I could teach the AI the project's general architectural principles and workflows, Windsurf's refactoring suggestions aimed not only to improve the code itself but also the overall structure of the project. When rewriting a portion of an iSCSI supply chain integration in a production ERP with the transaction outbox pattern, Windsurf provided suggestions not only for code changes but also for observability (metrics/logs/traces) integrations, considering the operational impacts of this refactoring. This shows that AI can help not only with coding but also with operational excellence, especially when considered alongside deployment strategies (blue-green, canary) in large and critical systems.

ℹ️ A Mistake and a Lesson

Last month, I implemented an optimistic lock in a FastAPI project using a draft from Cursor without sufficiently checking it. As a result, race conditions occurred under high concurrency, and database consistency was broken. This situation reminded me once again that AI's solutions may not always be context-appropriate, and I must always verify them with my own experience, especially in places involving critical business logic. My mistake was over-relying on AI, not AI's mistake.

Integration and Developer Experience

Not only the AI capabilities of a code editor but also how well it integrates into the developer's existing workflow is critically important. Both Cursor and Windsurf exhibit different approaches in this area.

Cursor:
Because Cursor is VS Code-based, it reduces the learning curve for VS Code users to almost zero. All existing VS Code extensions, themes, and keyboard shortcuts work directly in Cursor. This allowed me to quickly adapt to my familiar environment, especially when developing on my bare-metal servers on Linux or in containerized environments with Docker Compose. When examining journald logs or setting a cgroup memory.high limit, Cursor's terminal integration and file explorer offered the same comfort as VS Code. However, since Cursor's AI engine generally relies on cloud-based services, the quality of the internet connection can directly affect the speed of AI responses. Sometimes, when working over a VPN or when my internet connection was slow, I experienced a noticeable delay in AI responses. This could be annoying, especially for instant feedback-dependent code completion or quick refactoring operations. Furthermore, Cursor does not offer as much customization flexibility regarding AI models as Windsurf.

Windsurf:
Although Windsurf is VS Code-based, it offers more flexibility in terms of AI engine and RAG configuration. This means the ability to run AI models locally or choose between different cloud providers. For someone like me, who uses multi-provider fallbacks like Gemini Flash + Groq + Cerebras + OpenRouter, this flexibility allowed me to optimize the quality and speed of AI responses. Especially when using fast models like Groq for instant code completion or small corrections, I could use more powerful models like Cerebras for complex architectural suggestions. However, this flexibility comes at a cost: Windsurf's initial setup and configuration of AI/RAG integrations require a bit more technical knowledge and time. When setting up Windsurf on my own VPS or container environment, additional steps were required, such as configuring systemd units, optimizing cgroup limits, and writing Nginx reverse proxy rules. This could be a barrier for a less technically savvy developer. However, once set up, the performance offered by local AI models or fast providers allowed me to work without latency concerns.

⚠️ Performance and Resource Management

The AI features of both Cursor and Windsurf can significantly consume system resources (CPU, RAM), especially in large projects or with heavy use. There were times when I experienced build OOM errors or container memory limit issues in my own Docker Compose environment. Therefore, having a powerful development machine or optimizing AI models is important. I particularly tried to preserve overall system performance by setting soft limits like cgroup memory.high.

Which One in Which Scenario: My Preferences

Both AI editors excel in specific scenarios. With years of experience, choosing when to use which tool has been key to increasing my productivity.

Who is Cursor Suitable For?

• Those Who Want Quick Start and Easy Integration: If you are used to VS Code and want to quickly start using AI features, Cursor is ideal for you. Installation is simple, and you can start coding immediately.
• General Code Generation and Debugging: For small functions, tests, boilerplate code, and general debugging tips, Cursor is quite sufficient. It's useful for those who want to quickly get examples when learning a new language or framework.
• Projects with Not-Too-Complex Context: In smaller or medium-sized projects, Cursor's ability to understand the codebase is usually sufficient. It's a good option for those who don't need project-specific documentation or RAG integration.

I generally use Cursor for quick prototyping, trying out a new idea, or for standard configurations like Redis cache settings. Also, when doing PostgreSQL connection pool tuning or debugging systemd timer reliability issues, instant suggestions from Cursor speed up my work.

Who is Windsurf Suitable For?

• Those Who Want Deep Context Understanding and Customization: If your project has a unique architecture, extensive documentation, or very specific business rules, Windsurf's RAG capabilities are invaluable. The ability to train the AI with your own data allows you to get much more accurate and project-specific outputs.
• Complex Architectural Refactoring and Large-Scale Projects: During the implementation of complex architectural patterns such as migrating from a monolith to microservices, event-sourcing, or CQRS, Windsurf's broad context understanding ability helps you make fewer mistakes and create a more consistent architecture.
• Those Who Want Control Over AI Models: For developers who want to leverage the advantages of different AI providers or local models, and optimize performance and cost, Windsurf's flexibility is crucial.

I use Windsurf, especially when developing an AI-powered production planning module in a production ERP or when dealing with PostgreSQL partition strategies. In complex logical vs physical replication scenarios or read replica routing optimizations, Windsurf's RAG-integrated documentation knowledge helps me make the right decisions. By teaching the AI the details of Play Store publishing processes for my Android spam blocker app, it allowed me to detect issues like metadata rejections in advance.

Conclusion: The Future of AI-Powered Editors

Windsurf and Cursor are two strong players in the field of AI-powered code editors, and both have made significant contributions to my development process. Which one is "better" depends entirely on the developer's needs, project complexity, and customization expectations.

If you want to get started quickly, and get general AI support without giving up the comfort of VS Code, Cursor is a great choice. However, if you are like me, someone who wants to dive deep into the project, teach the AI specific documentation and architectural principles to get much more context-specific and accurate outputs, Windsurf's RAG and AI model flexibility offer an undeniable advantage.

In conclusion, I continue to use both tools in different scenarios. For quick and simple tasks, Cursor is my preference, while for in-depth analysis and complex architectural tasks, it's Windsurf. The future of AI-powered editors will undoubtedly bring more integrated, more contextual, and more responsive tools to individual developer needs. These tools have the potential to increase not only our coding speed but also the quality of our software and the robustness of our architectural decisions. In my next post, I will detail how I detect and resolve PostgreSQL WAL bloat issues.