Cursor vs Windsurf - Choose the Right AI Code Editor for Your Team

Cursor vs. Windsurf

AI-assisted coding has surged in popularity, transforming the developer experience. Among the emerging tools, Cursor and Windsurf stand out as innovative AI code editors built on VS Code.

Both promise to supercharge coding with AI, going beyond standard autocompletion and suggestions. But they take different approaches to achieving this goal. If you're interested in other AI coding tools, check out our comparison of Cody vs Cursor.

We'll explore their technical architectures (agent-style vs. assistant-style models), performance metrics, and real-world impact on development workflows.

Technical Architecture Comparison

Both Cursor and Windsurf are built on the foundation of VS Code, but their approaches to AI assistance differ in subtle ways. Understanding their architectures, particularly the distinction between "agent-style" and "assistant-style" AI models, is key to comparing them effectively.

Agent-Style vs. Assistant-Style AI Models

Cursor positions itself as an AI-augmented IDE with multiple modes: it provides conversational code assistance similar to traditional tools while offering an advanced Agent mode for more complex tasks.

In an assistant-style operation, Cursor’s Chat behaves like a conversational coding helper. You ask it questions or instruct it to modify code, and it responds with suggestions.

Enabling Agent mode lets Cursor actively take steps on your behalf in agent-style operation: it can understand your request, search your codebase, plan changes, open files, apply changes, and even run commands via a built-in agent workflow and verify the results​. This agent capability is designed to interpret high-level tasks and execute a sequence of actions to fulfill them and verify the end result.

Windsurf, on the other hand, emphasizes an agentic design from the outset. Its AI assistant is called Cascade, often described as an “agentic IDE” that can collaborate in non-trivial ways​.

Instead of a passive assistant that only answers prompts, Cascade is meant to handle context and file management somewhat autonomously, more like an AI co-developer who can take the initiative. In practice, Windsurf doesn’t make the user explicitly switch modes; Cascade can both chat and, when needed, perform multi-step “AI Flows” that involve reading multiple files or executing commands.

To illustrate, consider a task like “Add a new function to handle user authentication and update all relevant parts of the app.” In Windsurf, you might simply ask Cascade in chat to implement this, and Cascade will search the codebase, possibly open relevant files (e.g. user model, config, etc.), create a new file for the auth function, and modify existing code to integrate it.

You could achieve the same in Cursor, but you may need to invoke the Agent in Composer mode.

Alternatively, you can also explicitly prompt it step-by-step: e.g., open the relevant files or use @filename references in your prompt to ensure it knows where to insert the new function, then have it apply changes.

Cursor’s agent can certainly do multi-step workflows, but it relies on its MCP (Model Context Protocol) integration and your guidance to chain those steps. MCP helps you build agents and complex workflows on top of LLMs.

Think of MCP as a plugin system for Cursor. It allows you to extend the Agent’s capabilities by connecting it to various data sources and tools through standardized interfaces.

Windsurf’s Cascade was built to handle these “AI flow” multi-step edits more seamlessly.

In short, Windsurf defaults to an agent-style philosophy, whereas Cursor gives users a choice between direct assistance and an agent executing tasks (triggered via its Agent mode or Composer feature).

2. Local vs. Cloud Processing

Both Cursor and Windsurf rely on powerful cloud-hosted models for heavy-duty AI tasks, but they have some differences in approach and options:

All of Cursor’s AI requests are routed through Cursor’s backend servers, even if you use your API keys for OpenAI/Anthropic.

In practice, when you invoke code generation or ask the agent to do something, Cursor’s app packages up the prompt (including relevant code context) and sends it to their cloud service. The cloud service handles the final prompt assembly model invocation and returns the results to your IDE​.

This means Cursor requires an internet connection and uses cloud processing for the AI.

Cursor does let you plug in custom API keys if you want the AI calls to bill to your own OpenAI/Anthropic account, but those calls still go via Cursor’s servers (for assembling the context and managing the conversation state).

There is no fully-offline mode for Cursor’s main features at this time. If you enable “Privacy Mode,” it ensures nothing is stored server-side long-term (zero data retention), but the model inference still happens on remote servers.​

Windsurf (by Codeium) similarly uses cloud models by default. When you use Cascade or get a completion, it’s contacting Codeium’s AI endpoints. However, Codeium strongly focuses on enterprise and offers a self-hosted deployment option: companies can run the Codeium AI engine on-premises or in a private cloud so that “Your sensitive IP never leaves your network.” effectively an air-gapped setup.

This is a major selling point for Windsurf in enterprise contexts, organizations can own the AI model deployment and keep everything internal, addressing sensitive code privacy concerns.

Windsurf’s architecture includes some unique local components. For example, its Tab completions are powered by Codeium’s in-house trained models optimized for speed.

Those models likely run on Codeium’s servers (not on your machine), but they are engineered for low latency and might use cached local context. Windsurf also performs local indexing of your codebase for context awareness (more on that below), which happens on your machine, but that indexing could be combined with server-side embedding as well.

Windsurf edges out Cursor in offering a proper offline/on-prem solution for those willing to deploy it (particularly appealing to enterprises that want full model ownership). Cursor currently does not provide an on-prem version; it is a cloud service (though with a client IDE) focused on a “cloud-based approach.”​

Local indexing vs Cloud indexing

A related aspect is how each tool scans and indexes the codebase to provide context to the AI. Both use embeddings and intelligent indexing to allow the AI to retrieve relevant code (since feeding an entire large project into the prompt is infeasible).

By default, Cursor will index all files in your codebase.

You can also expand the Show Settings section to access more advanced options. Here, you can decide whether to enable automatic indexing for new repositories and configure the files Cursor will ignore during repository indexing.

When enabled, Cursor will upload your code in chunks to its server to compute embeddings, but it “does not store plaintext code”. Only the numeric embeddings and some metadata (file hashes, etc.) are stored​. So, Cursor’s code search is cloud-powered but privacy-masked (embeddings from which original code can’t be reconstructed).

Windsurf performs local code indexing by default. It automatically indexes the opened folder/repo on your machine for context. Enterprise teams can use a remote indexing service to have Codeium index all their repos on a single-tenant cloud instance, sharing embeddings across the team​.

In that remote case, Codeium similarly only stores embeddings (no raw code) on their isolated servers.

Practically speaking, both tools achieve a similar capability (semantic code search and context beyond the prompt token limit) but via slightly different mixes of local and cloud processing. Cursor’s indexing might make its startup on a new project a bit slower (since it uploads to embed), whereas Windsurf’s local indexing uses your machine’s resources.

Next, we’ll see how these design choices impact actual multi-file assistance.

3. Multi-file Refactoring Capabilities

Cursor and Windsurf support multi-file editing and refactoring, but their approach highlights the assistant vs. agent design. Cursor introduced a feature called “Composer” for orchestrating multi-file changes. Essentially, you can ask Cursor in its chat to perform a refactor that spans multiple files. It will plan out changes to each relevant file and present the modifications.

Windsurf’s equivalent is the “Cascade” AI flow, which leans into agent behavior. It deeply understands your project structure and can automatically propagate changes across many files when you request a refactor. In fact, Windsurf’s Cascade mode will generate or modify code project-wide, and you will be asked for your approval before running or applying the changes.

This built-in review step (e.g. “Accept” or “Reject” changes in each file) ensures the agent doesn’t apply the changes without your consent, a useful safety for large refactors.

For example, imagine you want to rename a function and update all its callers across a codebase. With Cursor, you might prompt its chat: “Rename function processData to transformData everywhere and update references.” Cursor (via Composer) will search for the symbol and produce diffs for each file that uses it, which you can then approve and apply.

On the other hand, Windsurf might let you simply issue a command in Cascade chat: “Please rename processData to transformData project-wide.” Windsurf’s agent will automatically edit all files where processData appears, maybe run the project’s tests to verify nothing broke, and then present you with the changes and results.

Notably, Windsurf’s agent can chain actions. It could notice if the code needs further tweaks and iterate until the refactor works (more on that in a moment). In contrast, Cursor’s assistant generally stops after proposing the code edits (leaving any further fixes to you or another prompt).

Developers have observed that Windsurf’s agentic approach shines in large-scale refactoring.

In other words, when sweeping changes across many files are needed, Windsurf’s depth of analysis and automation can handle them more gracefully. Cursor is no slouch either; in fact, some analyses credit Cursor with advanced refactoring strength, particularly on contained code changes.

The bottom line is that both tools can perform multi-file refactors, but Cursor (assistant) acts as a smart guide listing changes for you to apply, whereas Windsurf (agent) acts more like an autonomous teammate carrying out the refactor across files and verifying it works, subject to your approval.

4. Context Window and Codebase Context Handling

One of the most crucial technical differences is how each editor handles the context window limitations of LLMs and how they include multiple files or large code in prompts. Both Cursor and Windsurf implement clever strategies to feed the model just the relevant pieces of code.

Let’s compare their approaches:

Context window refers to how much code or information the AI model can consider. Larger context windows allow the AI to “see” more of your project (multiple files, long conversations, etc.), which is crucial when working on big codebases. Cursor and Windsurf approach context in different ways, reflecting their designs.

Cursor’s context handling

As a primarily cloud-based assistant, Cursor’s context window is tied to the models it uses (e.g., GPT-4, Claude). It can typically handle several thousand tokens (with some models up to 100,000 tokens). Cursor’s philosophy is to keep the AI focused on the immediate context, the files you have open, or the snippets you provide.

It has a “knows your codebase” feature that lets you ask questions about your code, but under the hood, this likely means you either reference specific files or do a quick search and pull in relevant bits.

In practice, if you want the assistant to consider a piece of code, you might have to open that file or copy/paste it into the prompt. This narrower scope can make responses more targeted (less chance of drifting off into unrelated parts), but it might miss distant connections in a large project unless prompted.

Windsurf’s context handling

Windsurf is built for deep codebase awareness. It includes an Indexing Engine that pre-scans your entire repository to create a semantic index. This means the AI agent can retrieve context from anywhere in your codebase on the fly, not just the files currently in view.

Windsurf’s “Cascade” uses this to great effect. You can ask a question about a function, and it will find the definition even if it’s buried in a different module because it has indexed it. Windsurf also implements “Memories”, a system to persist context across sessions. There are user-defined memories (like rules or notes you set for the AI) and automatic memories from past interactions.

This effectively extends context beyond a single prompt’s window: the agent can remember prior conversations or instructions when you come back later. The result is that Windsurf can leverage a much broader effective context than the raw model token limit by using intelligent retrieval. It shines in large monolithic codebases where relevant information might be spread across many files, and it “dives deep” to gather what’s needed. In other words, Cursor might answer based only on the snippet you showed, whereas Windsurf might recall that snippet’s relation to different parts of the project.

Windsurf's approach can be a lifesaver if you’re working on a huge project (think thousands of files). You can ask high-level questions like “How does data validation work in this app?” and Windsurf might traverse multiple files to compile an answer. A cursor with a smaller active window might require you to provide or open the relevant files manually.

That said, using a large context comes at a cost. Users have noticed that Windsurf’s attempt to hold a massive context can consume a lot of memory (e.g. running the indexing and holding large chunks in RAM)

In long sessions, both tools may eventually require starting a fresh chat (losing some conversational context) to avoid hitting token limits or slowdowns.

Both Cursor and Windsurf provide mechanisms to guide context and maintain continuity. Cursor allows setting project or global rules. Windsurf similarly lets you define AI Rules in Cascade, which are user-provided rules about frameworks to use, style, language, etc., which the agent will obey.

Cursor’s assistant model typically works within a single-session, limited context window optimized for relevant snippets. In contrast, Windsurf’s agent model extends context through indexing and memory, giving it a holistic view of your codebase.

For large-scale projects, Windsurf can answer questions and refactor with a broader understanding (at the cost of heavy resource use), while Cursor might require a bit more manual curation of context but offers speedy, on-point help for the code in front of you.

5. VS Code Extension Compatibility

Both Cursor and Windsurf are built on the foundation of Visual Studio Code, which means they inherit a rich ecosystem of extensions and a familiar UI for developers. When you launch either tool, the interface looks and feels like VS Code, intentionally to lower the learning curve.

Extension compatibility is a major selling point. You can bring in your favorite VS Code plugins (linters, debuggers, theming, etc.) and they will work in these AI editors as they would in stock VS Code.

Cursor and Windsurf each make onboarding easy by allowing you to import your VS Code settings and extensions in one click. For example, Cursor’s setup prompts you to import your existing VSCode configuration so that keybindings, color theme, and installed extensions carry over.

Windsurf offers a similar flow. It even lets you import settings from Cursor, given that many users try both! This means that right out of the gate, both editors can be configured with the exact environment you’re used to, minimizing friction.

In my experience and according to user reports, the most popular extensions (GitLens, Prettier, ESLint, Docker, etc.) work seamlessly in both Cursor and Windsurf. Windsurf’s documentation notes that it supports most VS Code extensions but with performance guardrails. This means only extensions meeting specific performance benchmarks are fully supported to avoid slowing down the editor.

Cursor, being around a bit longer, has had more time to iron out extension issues, and it boasts broad platform support, including remote development.

Windsurf WSL (Windows Subsystem for Linux) support is in beta. However, you must already have WSL setup configured on your Windows machine.

Cursor supports WSL and other remote development scenarios out-of-the-box

On the flip side, Windsurf’s tighter integration of AI might come with a slightly more polished UI in places. Overall, both are clean and intuitive VS Code-style interfaces. One notable difference was in chat history. Windsurf’s chat history management is a bit easier to use, whereas Cursor’s UI had some glitches when scrolling through old conversations.

In summary, extension compatibility is excellent in both tools thanks to their VS Code lineage. You won’t miss your favorite development plugins. Both editors let you keep your established workflow keybindings, themes, and extensions, so adopting AI assistance doesn’t mean abandoning the tools you love.

6. Privacy and security architectures

Code privacy is an important concern when introducing AI into your development workflow. Both Cursor and Windsurf have built-in architectures to address security and privacy, but they take somewhat different approaches due to their cloud vs. local orientations.

Cursor, being cloud-based, knew it needed to earn developers’ trust in sensitive code. It offers a Privacy Mode, which, when enabled, ensures that none of your code is retained on their servers.

According to Cursor’s documentation, “with Privacy Mode, none of your code will ever be stored by us or any third party” beyond the immediate processing needed for the AI.

Enabling this mode turns on zero data retention. After the AI responds, the service purges your prompt and code from its storage. Cursor is also SOC 2 certified, meaning it has passed audits for data security practices, which is reassuring for enterprise users.

It’s worth noting that privacy mode in Cursor can be toggled off. Even if it is off, Cursor may still collect usage and telemetry data (including prompts, code snippets, or editor actions) to help improve It.

Cursor does rely on third-party LLM APIs (like OpenAI) under the hood, so your code may go to OpenAI’s servers as part of a request. OpenAI has its policy of not using API data for training by default, and Cursor mentions “except for OpenAI which persists the prompts we send to them…” for a short period.

Windsurf's local-first or on-prem approach addresses privacy by keeping as much processing local as possible. In the ideal case, an enterprise could deploy Windsurf’s AI engine on internal servers so that code never leaves its network.

However, even the cloud-connected version of Windsurf (for individual users) has strong privacy guardrails. Codeium (Windsurf’s parent platform) explicitly does not train on your private code “no training on non-permissive data” is a stated policy.

All data in transit is encrypted, and like Cursor, Windsurf offers optional zero-day data retention.

Additionally, Codeium/Windsurf emphasizes it “does not use information without permission to train its models” and “provides encryption for data in transit”, echoing a security-by-design approach.

Windsurf also supports a self-hosted solution for companies, meaning the entire AI stack (the model, the orchestrator, etc.) can run in a private cloud or on your local servers with no external calls. In that scenario, developers get the full power of the AI agent with total code privacy (since even model inference happens behind the company’s firewall). This on-prem mode is a strong advantage for businesses with strict compliance requirements, and it’s something Cursor currently does not offer.

7. Performance Metrics (Startup Time & Memory Usage)

How do Cursor and Windsurf compare in real-world performance metrics like startup speed and resource usage? It turns out each has its own profile, with some surprises.

Startup time & editor performance

Despite packing a lot of AI capability, Cursor and Windsurf try to remain lightweight as editors. Windsurf’s engineering prioritizes speed – it’s “engineered to be lean and fast, with optimized load times”, even claiming a smaller memory footprint than typical VS Code.

Cursor is built on Electron like VS Code, so the baseline startup is on par with VS Code, plus there is a slight overhead to initialize the AI backend. In practice, you might not notice a big difference in startup between them: both open in a few seconds on modern machines.

Memory usage

This is where we see more differentiation and some challenges. Under normal operation (editing small to medium projects, moderate AI use), both Cursor and Windsurf might use 1–2 GB of RAM. This is higher than plain VS Code (which might be a few hundred MB with equivalent extensions). However, when pushing the AI features to the limit, users have encountered memory spikes in both tools.

Cursor may exhibit high memory usage during extended sessions involving heavy agent interactions or large code contexts. This can lead to performance degradation over time, especially on low-memory machines, and in some cases, restarting the application to recover resources may be required. The Cursor team has acknowledged the issue, which appears to stem from inadequate memory cleanup during long-running sessions.

Due to its aggressive context handling and indexing, Windsurf can consume significant memory in large projects or long Cascade sessions. In some cases, memory usage may exceed 10 GB, leading to slowdowns or the need to restart the application to restore performance. This behavior appears tied to caching large context windows to enhance agent responsiveness, which can strain lower-end systems.

Speed and responsiveness

From a pure speed perspective (CPU and latency), Cursor generally feels more responsive for code completion and quick fixes. Multiple developers have noted “Cursor maintains an edge in speed and reliability” during normal usage, with its autocomplete popping up suggestions faster than Windsurf’s. Windsurf’s suggestions, while more comprehensive, can be a bit slower.

Benchmark results

We don’t have formal benchmark numbers (like operations per second) since these tools don’t lend themselves to a simple benchmark. But based on user experiences and our trials:

• Startup time: Both ~ are 2-5 seconds for a typical project. Windsurf may perform a longer initial index on huge projects.

• Initial Memory on load: Cursor ~300-500MB on a small project idle; Windsurf ~400-600MB (with index loaded). With a medium project open and some AI usage, ~1-2GB each.

• Memory under heavy use: Both can exceed 8GB if pushed; cases of 10-15GB are reported for each

• Autocomplete latency: Cursor often <100ms for simple suggestions (subjectively very fast). Windsurf may be ~200ms if it’s hitting the index. For multi-line completions, both might take 0.5-1.5s, with Cursor being at the lower end often.

• Agent task completion time: For a multi-file refactor, Cursor might generate all diffs in, say, 5-15 seconds (depending on complexity). Windsurf might take 10-30 seconds but we will also attempt to execute tests/fixes in that time. These are ballpark observations; both are still far faster than doing the tasks manually.

In practice, developers often mention that Cursor “feels” more lightweight, whereas Windsurf “feels” more heavyweight but powerful. One developer summarized it well: use Windsurf if you “prefer more comprehensive (though sometimes slower) code suggestions” or choose Cursor if you “prioritize speed and reliability”.

Future Implications

8. IDE Evolution and VS Code’s Future

The rise of Cursor and Windsurf signals a broader evolution in IDEs. We’re moving from AI as a plugin (like GitHub Copilot in VS Code) to AI as a core architectural feature of the IDE. This raises the question: what might the IDE of the future look like?

One likely scenario is that mainstream IDEs like Visual Studio Code and IntelliJ will natively integrate agent/assistant capabilities. Microsoft has already announced deeper integration of GitHub copilot (Copilot X) in VS Code, adding chat and some refactoring tools. But Cursor and Windsurf push even further, forked VS Code to weave AI into every aspect (from autocompletion to running code) tightly.

This could pressure the VS Code team to adopt similar features or risk users migrating to these AI-augmented forks. It’s telling that both products leverage VS Code’s openness; it allowed startups to innovate faster than the core VS Code team could. In the future, we may see VS Code absorb some of these ideas, such as having a built-in project indexing service and an AI agent panel by default.

If such agent capabilities become standard, the developer's role will shift more towards supervision, design, and integration and less towards writing boilerplate. IDEs might evolve to have two modes: a creative mode, where the developer writes code normally, and an agent mode, where the IDE takes over routine coding under your direction.

Another implication is collaboration. Cursor has emphasized collaboration features (like sharing Chat sessions or their forum integration) and is noted as a “better tool for team-based development”. We can expect IDEs to bake in such features as an “AI code review”, where the agent reviews a PR and leaves comments.

It’s also possible that AI agents will become programming assistants across the software lifecycle, not just in writing code but also in tracking issues, writing documentation, and monitoring software.

Since Cursor and Windsurf focus on coding, other tools might integrate with project management (e.g., an AI that reads your Jira tickets and helps implement features). VS Code’s future might involve being a hub where AI connects your code editor, docs, and runtime environment.

9. Impact on Development Workflows, Especially in Startups

Agentic AI tools can be a force multiplier for startups and agile teams. Developers often wear many hats in a startup, and time is the most precious resource. Here’s how models like Cursor and Windsurf are impacting workflows:

• Rapid prototyping: Startups need to build MVPs quickly. An AI assistant can generate boilerplate code, set up the basic project structure, and create initial UI components from a simple prompt. By leveraging AI for the grunt work, a small startup team could go from idea to working prototype much faster. Instead of spending days writing setup code, they could focus on unique business logic while the AI scaffolds the rest.

• Smaller Teams, higher productivity: For a startup with 2-5 engineers, a 2x boost is like having double the team. This doesn’t mean AI replaces developers but augments them to produce and maintain more code than is normally possible. Routine work (writing CRUD endpoints, converting one data format to another, writing tests) can be offloaded to the AI, freeing developers to tackle harder problems.

AI as a team member: Some startups treat the AI agent as an actual team member. You might have tasks in your sprint explicitly assigned to the AI (via a developer driving it)

• Continuous integration/deployment (CI/CD): Startups that deploy daily could have AI agents that automatically fix simple build errors or update config files when a deployment fails. Cursor and Windsurf are not quite CI tools, but they can already generate Dockerfiles, YAML configs, etc., on the fly when asked.
• Learning and skill: For junior developers in startups (who often have to climb the learning curve quickly), AI assistants are mentors. They can ask, “How do I implement OAuth login?” and get a code plus an explanation. This can accelerate onboarding. Startups can ramp up new hires faster with AI, bridging the knowledge gap of the codebase.

Ultimately, these AI tools are leveling the playing field. A tiny startup can implement features at a pace closer to a much larger team. The caveat is managing the AI’s outputs and ensuring quality. The startups that figure out the right balance between harnessing AI for productivity and avoiding its pitfalls will have an edge in delivering software faster and more continuously.

Developer experience trade-offs

10. AI model ownership and control

One fundamental trade-off between Windsurf and Cursor is ownership and control of the AI model and environment. With Windsurf, you effectively own the AI environment (especially with self-hosted deployment options), whereas, with Cursor, you subscribe to a service where the provider controls the model tuning, updates, and environment.

By running Windsurf, especially self-hosted, you can control which AI models are used (Codeium could choose between their own “Sonnet” model or an open-source model, etc.) and when updates are applied. This can be important for consistency: you wouldn’t want an update to the AI to suddenly change how it formats code in the middle of a project.

Some companies want an AI coding assistant, but on their terms. They might demand an air-gapped version for highly secure networks. Windsurf provides this option.

With Cursor, the company manages the model and platform. This has upsides: you always get the latest model improvements automatically and don’t have to maintain any AI infrastructure. However, you relinquish some control. For instance, Cursor might switch from one model provider to another for quality or cost reasons, as the user doesn’t directly control that (though you’ll notice changes in output style or capabilities).

That said, Cursor does let you configure some things, like model preferences, to a degree (in settings, you might choose GPT-4 instead of their model for some tasks if they offer options).

But you can’t incorporate a new model that Cursor doesn’t support. Windsurf’s design might allow, say, plugging in OpenAI API vs Anthropic API vs Codeium’s model as you prefer (Codeium has a concept of “Engines” where you can choose different AI backends).

Control also relates to data. With the Windsurf self-hosted option, any custom data (like internal documentation or code) you index stays local, and you “own” that derived data. Cursor’s cloud might be indexing your code, too (to answer questions faster, etc.), but that index lives on their servers, and you don’t have direct access to it.

11. Developer Productivity Implications

The ultimate measure of these tools is how they impact developer productivity. Both Cursor and Windsurf aim to make developers dramatically more productive, but the way they do so and how developers perceive the impact can differ.

Speed vs depth of productivity

Cursor’s focus on quick assistance means it excels at micro-productivity boosts: faster autocompletion, inline suggestions, and quick answers to questions.

Windsurf, by contrast, often helps with macro-productivity, tackling larger tasks that might span hours or days and compressing that timeline. For instance, performing a codebase-wide refactor or implementing a new module with multiple files and tests could be a day’s work. Windsurf can automate large parts: generating boilerplate, updating all references, and even writing tests.

Concrete productivity metrics are hard to quantify, but one can gauge productivity levels based on personal accounts of developers who have used these tools and shared their experiences online. Cursor’s team touts that engineers using it often see a >2x productivity improvement in coding tasks. Windsurf is similarly impressive.

With great power comes great responsibility. One trade-off in productivity is the risk of complacency or overreliance. A developer might not see huge productivity gains in the initial days until they adapt their workflow. Some challenges noted include the AI sometimes “getting ahead of itself” – e.g., Windsurf might try to solve more than you asked and go on a tangent, which can waste time if not managed.

Overall, Cursor and Windsurf demonstrably boost productivity, and most users largely agree. Cursor might make you faster in the small, day-to-day tasks, and Windsurf might enable you to take on tasks that would be infeasible or too time-consuming otherwise.

12. Code Privacy Considerations

From the developer’s perspective, code privacy is not just about what the tools promise but also about peace of mind and organizational policy. Many developers must ask: “Is it safe (and allowed) for me to use this AI tool on my company’s code?”

The primary concerns are:

• Could my proprietary code leak or be seen by unauthorized parties?

• Could snippets of my code end up in someone else’s suggestions (as with some past AI assistants)?

• Is using this tool compliant with my company’s policies or industry regulations?

Since Cursor is cloud-based, developers might initially be wary. Early on, some companies outright banned cloud AI code assistants (like Copilot) until privacy assurances were clearer. Cursor has tried to address these fears with Privacy Mode and clear policies. They advertise “Privacy Options —if you enable Privacy Mode, your code is never stored remotely”, which directly addresses the leak/storage concern.

Also, “Cursor is SOC 2 certified”, meaning it has been vetted for handling sensitive data. For many devs, that is enough reassurance to try it on non-extremely-sensitive code.

Windsurf is designed for privacy-sensitive use. It offers fully self-hosted deployment options, including air-gapped deployments with zero third-party dependencies. Your sensitive IP never leaves your network, which is a huge win for code privacy.

Enterprise policy

Developers in larger companies often have to follow policies. Many companies by 2025 have started approving tools like these under certain conditions (like “AI tool X can be used if it does not retain code and is SOC2 compliant” or “only use AI on non-secret projects”).

Intellectual property (IP) considerations

Another angle is IP ownership. If you use an AI to generate code, is there any issue? Normally, code generated is considered your code. Both Cursor and Windsurf likely have terms stating you own what you produce with them (Copilot had to clarify this too). But from a privacy standpoint, you wouldn’t want your unique code to help someone else inadvertently.

As a developer, you should also be transparent about using these tools with your team. If using Cursor, let your team know it’s configured not to store code. If using Windsurf, highlight that it’s self-hosted. This helps build trust that you’re not leaking secrets inadvertently.

In conclusion, code privacy need not be a blocker to enjoying AI assistance. Windsurf delivers privacy by design (especially in on-prem mode), and Cursor has robust privacy settings that approximate the same level of safety. The key is enabling those features and choosing the right tool for your privacy needs.

Further Reading and Resources

• Official

  • Cursor
  • Windsurf

• Related Comparisons

  • Cody vs Cursor: Choosing the Right AI Code Assistant

  • CodeRabbit vs Others: AI Code Review Tools

Further Reading

• Building Large Projects with Cursor AI – How Cursor scales with large Go project.