My main use case for Windsurf is developing various microservices in our domain, which are Java Spring-based microservices. I use Windsurf for generating code, writing unit test cases, and suggesting project creation from scratch, such as for Spring Boot. Moreover, we work heavily on the SQL side, where we frequently encounter slow-performing SQLs. I tune and fine-tune the SQLs with the help of Windsurf, and it gives good results to us.

Regarding my main use case with Windsurf, we try to adopt CI/CD as part of our current work protocol. We had an old repository of thirty to forty modules that needed to be migrated to CI/CD by updating the pom.xmls and the Jenkins build files, which involved very repetitive work that developers needed to do manually. I took the help of Windsurf for this CI/CD integration for one module and asked Windsurf to replicate the same steps in all the modules by adding the build XML file and making changes in the build and deployment aspects. It very smoothly replicated all those files in all the modules and saved a lot of time on this manual effort.

The best features Windsurf offers include the code generation part where I can suggest something for generating a code file, and it has the option of validating that file before it commits to my file system. I can either accept or reject those changes. It has the capability to generate code up to the mark, with good quality. Since I have multiple options to try different models, it provides me good flexibility to validate which model fits in which scenario, allowing me to decide and choose a model that helps get my work done.

Regarding the features, another important aspect is the understanding of the context of the codebase. Windsurf is very powerful in analyzing my source code and understanding the context against which I am asking it to generate code. It helps a lot when I open my code repository and give the context, and by searching the file, I can locate which file to change, and Windsurf can read the complete code. According to the context, it can suggest me the solution.

Windsurf has positively impacted my organization by saving significant time, which is a great feature I have observed. The redundant manual and repetitive work we used to do is now handled by Windsurf. Many old codes were developed by previous team members, and new team members find it hard to understand. Windsurf helps a lot in understanding the code and providing crisp details about what each part of the code does. It definitely reduces the developer's effort in coming up with solutions and understanding the features or functionalities written in the code.

I see an option for improvement regarding the platform that Windsurf is developed on, which is VS Code. We have projects in different technologies and languages, so if Windsurf can smoothly support running Java code, Spring Boot microservices, or enhance debugging capabilities, then it will be a one-stop shop for everything.

From a usability perspective, Windsurf should be more user-friendly. When code gets generated, I see room for improvement in copying, looking at old queries or prompts, and copying the output from the cascade window to other codebases.

I started exploring Windsurf since last year, so almost a year or so.

Windsurf so far looks stable to me; I have never seen it crash or fail to generate the required output. In the majority of cases, I see that Windsurf is working very stably.

Windsurf is able to handle larger workloads; I try to perform code generation for multiple modules, and Windsurf can manage that.

I have not talked to customer support for any issues.

I previously explored the Google Code Assistant once initially, and compared to that, I am really impressed by Windsurf's capability to understand the context and make code suggestions. That is why I prefer Windsurf. Before choosing Windsurf, I evaluated other options such as Google Code Assist.

I can comment on the time saved, but I do not have visibility on other aspects regarding return on investment.

We are more involved in the usability of Windsurf, so I am not sure about the pricing. The licensing is one aspect; we have a limited license for individual team members who can use Windsurf.

I would definitely suggest others to give Windsurf a try and start using all its features. It will help, and they will find some features that are very useful based on their requirements and what options they are looking for. Overall, Windsurf is a great tool, fitting the current AI journey that individual organizations are looking to join. It is really helping employees and developers accelerate their code generation lifecycle. I give Windsurf an eight out of ten overall.

My main use case for Windsurf is building end-to-end automation frameworks from scratch, which I primarily use in my work environment. I also use it to build personal projects, create and debug test cases, particularly automated ones, and I am currently exploring agentic QA architectures with multi-agent systems, which I have really enjoyed.

I can give you a concrete example of how I have recently used Windsurf in one of my automation projects. I have used it in its different working modes, whether Chat, Code, or Plan. These modes have really allowed me to transform my workflow. For example, Plan mode helps me design the architecture of complex solutions before implementing any automation in either of my two work projects and also in my personal projects. Chat mode lets me ask questions about what is going on with the code and allows me to do quick debugging sessions, which I have really appreciated. At the Code level, the fact that it generates code for me much faster, so that I only have to review and orchestrate, has been one of the things I have liked the most. I also find it very beneficial that I can use MCP to enhance my automation flows, such as Maestro MCP and Playwright MCP, which I currently use.

I can use different AI models and I particularly appreciate the system called Adaptive, which has allowed me to save tokens and lets Windsurf choose which model it should use for whatever task I ask it for. I have found that quite beneficial.

I consider the best features that Windsurf offers to be what I already mentioned: the ability to use MCPs, the working modes which include Chat, Code, or Plan, and the capabilities it has to use agents, including custom ones within Windsurf, and the support for multiple LLMs or AI models. This means I can use both free models and the more professional ones, such as Anthropic's Claude models like Opus or Sonnet or the Codex ones.

I can go deeper into how MCPs have helped me in practice. For example, with Playwright, the ability to use MCPs such as Playwright Clean has allowed me to create better automation tests. I am currently facing a bigger challenge, which is automating the native app from my job, built with React Native. The ability to use Maestro MCP, which has recently come out, and that Windsurf now allows me to use locally to find better selectors or debug what I need for the automation has been very helpful.

Windsurf has positively impacted my work. While I don't know if my organization uses all of Windsurf, in my case it has had a positive impact and has allowed me to work much faster and create higher-quality tests. It has allowed me to cover areas, especially at the backend level, to run tests, which has been beneficial. So it has allowed me to meet deadlines, work faster, and more efficiently.

In the automation of the app, Windsurf has allowed me to save time and improve the quality of the tests. I know that today there are many tools with which you can automate, but the ability to use Windsurf's agents plus the MCPs to move forward with the app's automation was a very good advantage.

I think Windsurf could be improved. Honestly, I see it as super competitive today with the vast majority of AI IDEs out there. It would be great, even though it already has the models, to be able to include Claude Code at the console level, which I think would be really cool.

I have been using Windsurf for approximately seven or eight months.

Windsurf is very stable in my experience.

I cannot share any specific return on investment metrics with Windsurf because I do not manage that, but I can tell you that I have reduced my time and that compared to other IDEs, Windsurf is very efficient.

I evaluated other options before choosing Windsurf. I evaluated Anti-gravity, which I also appreciated, but today it is very heavy and I did not prefer that. I also evaluated Cursor and spent some time with it, but I did not prefer it that much either. What I appreciate about Windsurf at this moment is that I can give it autonomy, and I also appreciate being aware of what it is doing without it doing everything automatically. I appreciate being able to review everything, and I think that is an advantage. I do not have to be creating rules for Windsurf for it to do that, but I think it is kind of cautious.

My advice to others who are considering using Windsurf is that they should use it. Right now there is a mode where they provide 14 days free. I think in those 14 days you realize that it is a tremendous code editor and that you will appreciate Windsurf.

I have no additional comments about Windsurf before we finish, except that it is very good, I have made quite a lot of use of it, and I hope it continues to improve and keep pace with other code IDEs. I would rate this product a 9 out of 10.

My main use case for Windsurf is building and iterating on AI-driven products faster, especially when working with multi-file codebases and agent-style workflows. At Ser AI, I use it heavily for exploring and understanding large codebases quickly, generating and modifying features across multiple files, debugging with context-aware suggestions, and prototyping AI workflows such as agents, memory systems, and integrations. What I appreciate is that it is not just autocomplete; it actually understands project-level context, so I can move faster from idea to working feature.

A recent example of where Windsurf helped me in my workflow was while I was working on an AI-driven feature in Ser AI where I was building an agent workflow that connects multiple parts of the system such as API calls, prompt handling, and response processing. Normally, this would involve jumping across multiple files, understanding existing logic, and stitching everything together manually. Getting back to work after long breaks always means losing context, which is when I use Windsurf so much to understand existing logic and stitch everything together. With Windsurf, I was able to quickly understand how different parts of the codebase were connected, generate and modify logic across multiple files, and debug issues with more context instead of isolated snippets. One specific moment was when I had to refactor how data was flowing between components; instead of rewriting everything manually, I used Windsurf to restructure the logic end to end, and it saved a lot of time. Overall, it helped me move faster from idea to working implementation, especially for complex multi-file changes.

In my day-to-day work, the biggest difference is speed at the system level, not just coding speed. Building a feature means understanding the codebase, writing logic, wiring things together, testing, and fixing. With Windsurf, especially using Cascade, a lot of that becomes one continuous flow. For example, when I add a new API flow or connect to front-end logic and update response handling, I can describe the intent, and Cascade actually executes changes across multiple files. I can give it prompts without typing, so it feels I am delegating a task instead of manually doing every step. That is where it stands out in daily work. I spend more time thinking about architecture and less time jumping between files. Browser-based testing has been useful when I am working on flows that involve UI and back-end together. Instead of writing code, switching to browser tests manually, and coming back to fix, I can stay in one loop where I build the feature, test behavior quickly, and identify issues faster, reducing context switching, especially when validating end-to-end flows. The real impact in workflow is faster iteration cycles, less manual glue work between components, and better focus on logic and product decisions. One important observation is that when working across longer sessions or switching models, sometimes the deeper context does not persist perfectly, so I have to realign the intent again.

At Ser AI, the biggest positive impact of Windsurf has been on speed of execution and iteration. Since we are building AI-driven features and experimenting on the creator marketing side, the ability to go from idea to working prototype quickly is critical. Windsurf, especially with Cascade, has helped us reduce the time it takes to build and ship features, handle multi-file changes without slowing down, and iterate faster on experiments. One clear outcome is that features that would normally take a couple of days to wire up end to end can now be done much faster because a lot of the repetitive glue work is handled. It also improves how we approach problems by breaking things down into very small coding tasks; we think more in terms of complete flows or systems because we know the tool can handle the level of execution. Another impact is onboarding and understanding the codebase. When jumping into a new part of the system, Windsurf helps quickly understand how things are connected, which reduces ramp-up time. The overall outcome is more experimentation in less time and better focus on product and logic instead of boilerplate work.

The best features Windsurf offers are Cascade, the agent system, full codebase awareness, multi-file editing and refactoring, and AI chat integrated within it. One drawback I personally see is the persistent context memory layer, which needs to be improved over time. One more best thing is that you can use the browser to actually see and sense the elements and test them.

The real impact in workflow is faster iteration cycles, less manual glue work between components, and better focus on logic and product decisions. One important observation is that when working across longer sessions or switching models, sometimes the deeper context does not persist perfectly, so I have to realign the intent again.

At Ser AI, the biggest positive impact of Windsurf has been on speed of execution and iteration. Since we are building AI-driven features and experimenting on the creator marketing side, the ability to go from idea to working prototype quickly is critical. Windsurf, especially with Cascade, has helped us reduce the time it takes to build and ship features, handle multi-file changes without slowing down, and iterate faster on experiments. One clear outcome is that features that would normally take a couple of days to wire up end to end can now be done much faster because a lot of the repetitive glue work is handled. It also improves how we approach problems by breaking things down into very small coding tasks; we think more in terms of complete flows or systems because we know the tool can handle the level of execution. Another impact is onboarding and understanding the codebase. When jumping into a new part of the system, Windsurf helps quickly understand how things are connected, which reduces ramp-up time. The overall outcome is more experimentation in less time and better focus on product and logic instead of boilerplate work.

Windsurf has become less of a tool and more of a core part of how I build. I do not think in terms of writing code line by line anymore; I think in terms of features, flows, and systems, and Windsurf helped me translate that into actual implementation across the codebase. It fits especially well when I am doing rapid prototyping, exploring new ideas or architectures, or iterating on existing features quickly. At the same time, one thing I have noticed in my workflow is around model switching. When I switch between models, the GPT generating agent models sometimes the deeper context regarding decision reasoning or intermediate steps does not fully carry over, so I end up re-establishing context manually every time. It is so much painfully manual; that is not a blocker, but since I work on fairly complex multi-step systems, having strong cross-model memory consistency would make it even more powerful.

One thing I would really appreciate is stronger cross-model memory and context continuity. Right now, when I switch between models, the surface-level context is there, but the deeper reasoning regarding why certain decisions were made or how a flow evolved does not always carry over fully. Since I work on complex and multi-step agents, I end up re-establishing the context manually. If Windsurf could maintain a kind of shared memory layer across models where intent, decisions, and intermediate steps persist, it would make the whole experience much more seamless. Improving the memory continuity and control would take it from powerful to extremely reliable at scale.

Overall, Windsurf is already a strong tool, but there are a few areas where improvements would make a big difference, especially for advanced workflows. The first is cross-model memory and context continuity. The second is better control over agent execution. Right now, when switching between models—for instance, if I am using a tier of models and then I reach a limit, and then I need to switch to a lesser limit model—the high-level context is there, but deeper reasoning is lost. A shared memory layer across models would make the experience much more seamless. Furthermore, while Cascade is powerful, for larger changes, it would help to have more visibility or control, such as previewing the execution plan and guiding steps before it runs.

The UI and documentation provided are pretty good, though I think there is room for true visibility and feedback during agent execution. While the amount of time put into the design and documentation is great, figuring out things with the documentation can often be done without any third-party help. Some advanced use cases are not fully explored in the documentation, but the best practices for using agents effectively are very clear, such as how to structure prompts for multi-file changes and how to guide Cascade for better outputs. Real-world advanced examples are already implemented in there; that could be very helpful for us.

The main advice I would give to others looking into using Windsurf is to not use it as a traditional code assistant. Windsurf really shines when you treat it as a feature-level or system-level tool, not just something for autocomplete or small snippets. So instead of thinking "write this function," think more toward "build this flow." Learn how to guide it properly. That is the main thing I would advise: learn how to guide it properly, how to prompt it properly, and start with real use cases, not toy examples.

I have been using Windsurf for around two to three years.

Windsurf is stable. Overall, performance has been quite strong, especially for the kind of work we do at Ser AI. In terms of speed and reliability, for most tasks such as code generation and debugging, it is pretty fast and keeps the flow uninterrupted, which is important when iterating on things such as creator analytics, matching logics, and building negotiation systems. I have not faced any major downtime that blocked work, which is a good sign. There is some variation during heavier tasks or longer complex prompts, where response time can increase a bit. Occasionally, in longer sessions, the context feels slightly less consistent, which can affect output quality more than speed, but these are more edge cases rather than frequent issues.

From what I have seen, Windsurf scales pretty well, especially at the codebase level. At Ser AI, we are working on systems such as creator analytics, matching injections, and multi-step workflows, which involve multiple services and files. Windsurf handles that complexity well because of its codebase awareness and multi-file execution. For larger projects, it understands and operates across bigger repositories, helps maintain consistency when making changes across connected components, and reduces the effort needed to navigate and manage complexity. For teams, it improves individual developer productivity significantly, makes it easier for team members to jump into different parts of the system, and reduces ramp-up time. Scalability can improve with stronger shared memory or context across team members and better ways to standardize how teams use agents.

From my experience, customer support has been good and responsive overall. I have not had to rely heavily on support for critical issues, which is a good sign in terms of product stability. Whenever I looked for help, especially through documentation and community resources, I have been able to find what I needed.

Before Windsurf, I was mainly using tools such as GitHub Copilot and Cursor alongside my IDEs. They were helpful for autocomplete, small code snippets, and quick fixes, but the limitation was that everything was still very fragmented. For instance, when I was building something regarding a creator scoring or matching system, I had to manually move across files, write logic piece by piece, and stitch everything together myself. The AI was helping, but only at a local level, not a system level. The main reason I switched to Windsurf is that it assists me while I code and helps me execute a full feature. The implementation and reasoning capabilities of Windsurf are much clearer than others.

I looked at and used a few other options before settling on Windsurf. I used GitHub Copilot, ChatGPT, Claude, Cursor, and some other AI-assisted editors. I did not do a very formal evaluation process, but I used them enough in real projects to understand their real strengths and limitations, and that is how I noticed these drawbacks and moved to Windsurf later.

It is not that I used something before and then switched; we actually switch between different tools and alternatives to find the best one, and we found Windsurf as the best.

The integration has been pretty seamless, especially with the core development stack. Since it works directly within the IDE environment, it fits into existing codebases, Git workflows, and typical dev tooling without needing extra setup. From a day-to-day perspective, I did not have to change how I work; it just enhanced the workflow. It also works well alongside back-end services and APIs we are building, front-end frameworks, and general cloud-based tooling. So it fits into the ecosystem rather than forcing a new one.

Adoption was actually pretty smooth at Ser AI. Since we are building in the creator marketing and AI space, our workflows already involve a lot of rapid experimentation, integration of APIs, and iterating on features such as analytics, matching systems, and automation. Windsurf fits into how we already work. The biggest advantage was that the team did not need heavy training. If you understand your system and can clearly describe what you want to build, Windsurf becomes useful almost immediately. Where it helped especially in our domain is quickly building and iterating, tying together multi-step flows regarding data injection and processing output. Onboarding felt more regarding starting to use and improve over time rather than formal training. We faced challenges learning how to structure prompts properly, guide the agent, and manage context across longer sessions or model switches.

We are using the hosted setup, which falls under another provider rather than directly using Amazon, Google, or Microsoft from our side. Windsurf manages the underlying infrastructure, and we access it as a cloud-based development environment without directly configuring AWS, GCP, or Azure for it.

We have definitely seen a clear return on investment at Ser AI. The biggest impact is on time and output, which directly translates to cost. In terms of time saved, we save roughly thirty to forty percent on future development time. The iteration cycles are about two times faster, especially for things regarding creator analytics, matching logic, and automation workflows. Because of that, we are able to ship around one and a half to two times more features or experiments per week. This means, instead of needing to scale the team early, we can do more with a smaller team. Realistically, it delays the need for additional hires because one developer can handle more system-level work. A simple example from Ser AI is where we were building a creator brand matching and scoring flow. It involved injecting creator data, applying scoring logic, connecting it to APIs, and generating output for brands. Earlier, this would take around one to two days to fully wire up across the back end and front end. With Windsurf, especially using Cascade, we are able to implement the flow across multiple files in a few hours instead of days.

I can give rough but realistic estimates based on my workflow at Ser AI. For future development, I would say we have seen around thirty to forty percent reduction in end-to-end implementation. Something that used to take maybe one to two days, especially involving multiple files and integrations, can now be done in a few hours. For iteration cycles, we are able to test and refine ideas about two times faster, mainly because we are not spending time on repetitive wiring and context switching. For onboarding and understanding new parts of the codebase, I would estimate around forty to fifty percent faster. Instead of manually tracing files and dependencies, Windsurf helps surface how things are connected pretty quickly. For overall output, it is not just more code; it is more completed features. I would say we were able to ship significantly more experiments per week, around one and a half to two times compared to before, especially for AI-related features. Before, we spent more time in navigation, wiring, and debugging across files; after, we spend more time in decision making, logic, and product thinking.

The overall experience with pricing has been straightforward and manageable. Since it is cloud-based and a managed tool, you do not have to spend time or money on setup or infrastructure. We could start using it almost immediately. The pricing feels aligned with the value it provides, especially considering the productivity gains. Since it helps us build features faster, reduce development time, and ship more experiments, the cost is justified from an ROI perspective. The licensing standpoint is simple and has not slowed us down. Windsurf fits well for a small, fast-moving team without adding operational overhead. As a startup working on creator marketing and AI systems, we are always conscious about cost, but tools regarding this make sense if they directly improve execution speed and output, which it does.

Everything is quite agile; but if I need to mention something, it would be the handling of longer or ongoing sessions and response consistency. My review rating for Windsurf is nine point five out of ten.

Compared to other IDEs and AI-powered development tools I have used, Windsurf operates at a system level, not just code snippet level. Most tools such as Copilot, Cursor, or basic AI assistants are great for autocomplete, small code generation, isolated fixes, and the reasoning is pretty weak in them. They still keep up in a file-by-file workflow. Windsurf, especially with Cascade, shifts that to feature level execution, multi-file understanding, and end-to-end changes across the codebase. That is a big jump in productivity. In terms of workflow, it reduces a lot for us, so instead of writing, switching, testing, coming back, and fixing, it becomes more regarding defining intent, executing, and refining. That is a much tighter loop. In terms of productivity, I would say other tools give incremental improvements, while Windsurf gives a more step-change improvement.

From my experience, Windsurf feels regarding a managed cloud service, so a lot of security and data handling is abstracted away, which is convenient from a development perspective. It integrates smoothly without exposing or breaking our existing workflow. We are not required to manually handle infrastructure or data pipelines, and for typical development use, it feels reasonably safe and controlled. We are mindful about not exposing highly sensitive credentials directly into prompts and keeping critical secrets managed through environment variables or backend systems. So we treat it similar to how we would use any cloud-based AI tool.

I am currently using Windsurf for my main use case of software development. I give prompts and check what Windsurf has done with colleagues as a specific example of how I use Windsurf in my work.

I am not certain what the best features Windsurf offers because I do not use it much. Windsurf has positively impacted my organization by speeding up my work. When I say it sped up my work, I mean there is quick development now.

I am not sure how Windsurf can be improved. There is nothing small that I wish Windsurf did differently or better.

I have been working in my current field for eleven years.

My main use case for Windsurf is coding. I do a lot of coding with Windsurf; for example, the first was an application for telephones in React Native, and more recently, I built a full transaction engine with Azure. I'm building new software every week. More recently, I've tackled a lot of MCP servers with Windsurf.

In my opinion, the best feature Windsurf offers is the adequation between price and quality, meaning I can switch from expensive AI engines for difficult tasks to cheaper ones for easier ones, allowing me to manage my invoices effectively.

Windsurf helps me manage invoices and switch between AI engines easily; when we ask the AI to work, we can just select the AI model we want to use at that time, and that's very cool.

Windsurf has positively impacted my organization by helping me start projects on technologies I wasn't mastering; for example, when I initiated the project in React Native for telephones, I didn't know the technology, just had a YouTube video, and I believe that without Windsurf, I couldn't manage this kind of project and coding activities, so the main idea for me is that Windsurf allows me to work on technologies that I don't master.

Windsurf helped me learn and adapt to new technologies and saved me time; especially for React Native, I wouldn't have started the project without the help of AI and the possibility of using Windsurf to do it, and it's the same for other projects where I worked, such as a recent project with Electron and other technologies I didn't use and wouldn't have tackled without Windsurf.

I think that recently there is some form of long-term memory based on folders, which is good, but I prefer the product as it is now; I'm used to working with the old way of the visual source code interface, and I'm comfortable with it, so I'm not waiting for something very new. My main point is perhaps having more models available for free or inexpensive, and perhaps reducing the price of Opus 4.6, which I use a lot, but otherwise, I feel good with the features of the product as of now.

I've started using Windsurf, and my first invoice is dated more than one year ago, something like February 25.

Windsurf is stable.

Windsurf's scalability is good, but as I am a one-person company, I don't have any serious scalability issues.

I didn't have to contact customer support.

I was using Claude Desktop, the ancestor of Claude Code, but I switched to Windsurf because I was limited to Anthropic models, and Windsurf is more agile with AI models.

My experience with pricing, setup cost, and licensing is good; I prefer the price of Windsurf, but it would be better if there were more free or low-budget models offered.

I am just a regular customer of Windsurf's solution; I am not a partner or reseller.

I've seen a return on investment because the best return is that I can work on technologies I didn't use before, making it difficult to set a figure for it, but definitely, I can work on projects I wouldn't have done before.

My experience with pricing, setup cost, and licensing is good; I prefer the price of Windsurf, but it would be better if there were more free or low-budget models offered.

Before choosing Windsurf, I evaluated Claude Desktop.

I would rate Windsurf a nine on a scale of one to ten. I choose a nine because it's always perhaps the offering in low-budget models that prevents a perfect score. I recommend Windsurf to others looking into it; I tell my friends they can start using it with the free models such as SWE 1.5, which is quite good, even if it's free, and later switch to a more expensive model when they have a real project. My overall review rating for Windsurf is nine.

My main use case for Windsurf is for coding, so this functions as an IDE, similar to a fork of Visual Studio Code, and it aids us in code development.

I appreciated that Windsurf had the same features as Visual Studio Code because it is a fork, so switching was seamless.

The best feature Windsurf offers is that it increases the speed of development, especially for repetitive tasks such as refactoring boilerplate. It is integrated with the terminal, so we can use AI features in the terminal. For example, if there is no module found, you can ask it, and it has deep context, so it can read your project and learn on its own if you ask it. It allows AI to act as an agent, which can see that some tests failed or check the terminal output and suggest fixes and apply them. It can edit multiple files, which is really valuable compared to chat mode in ChatGPT, which is much less capable.

Windsurf has positively impacted my organization by increasing the speed of development, which allowed us to prototype much faster and lowered the barrier of entry for developers. Junior developers can now be more productive, and it reduced context switching because you can ask AI something instead of leaving to search for answers online.

We use Scrum and we have noticed that story points per sprint increased by 30 percent on average per developer since adopting Windsurf.

Windsurf can be improved by using better models, especially state-of-the-art ones.

I have been using Windsurf for six months.

My advice to others looking into using Windsurf is to use the terminal and ask AI to debug terminal output rather than simply asking it to write code. Use your configuration from VS Code instead of setting it up from scratch. Windsurf is great technology, and this AI revolution is making me somewhat anxious about job security in the future, but currently, it is a great tool. I would rate this product 9 out of 10.

I used Windsurf for about three months. Once our Cursor license expired, we were given Windsurf to use.

I used Windsurf to generate test cases and update my test cases in Jira. I made an MCP connection with Windsurf and Atlassian Jira so that I could make test cases there, update them, and update the statuses. My second use case was to execute those test cases by using Playwright MCP and browser execution. My third use case was to write the automation of those test cases from Windsurf.

The process was smooth. I would copy-paste my test case into the chat option and tell Windsurf to write the automated test case for it. I would also give Windsurf the XPath, the browser name, and the credentials. I would write the detailed steps for my test case and Windsurf would write the test cases very smoothly.

I am using Windsurf for these three use cases: making the test case, automating them, and executing them.

Windsurf is very similar to Cursor. The feature I admired the most was its integrating capabilities. It can be integrated with Playwright MCP and Atlassian MCP very easily. The user interface was smooth and I really appreciate the types of themes they provided. There was also auto-complete functionality in Windsurf which helped me a lot. Windsurf gave a concise conclusion of what it did, the steps it took, and the conclusion of it. Instead of reading all the chat, I could rely on the conclusion part which is small, crisp, and very nice.

The auto-complete functionality helped me complete sentences automatically. It gets the instinct of what I am going to write and does that in a very smooth manner. A lot of times Windsurf hallucinates and completes it in a wrong way, but overall I like this functionality. In about 70% of the cases, it thinks the right thing that I was also thinking.

We were able to complete our deadlines and meet them before the scheduled time. If a project was for 15 days, we could complete the same project in 10 days by using Windsurf to automate the test cases, execute them, and make those test cases. Meeting the deadlines became really easy. It also automated a lot of our manual tasks.

Windsurf helps me fix flaky cases very easily. I would run my test case and it would start executing. Whenever there was an issue or the test case was failing, it would identify that place and Windsurf would fix it on its own.

Windsurf can be improved by increasing the context awareness. It sometimes loses context across files. If it had better repo-wide understanding and smarter navigation across test suites, APIs, and services, that would be great. Debugging and failure analysis is another area for improvement. Windsurf helped in writing the code but debugging is still a weak point. Stability is also something that should be improved. Most AI tools hallucinate and Windsurf also hallucinates. When it is given a larger amount of data, it hallucinates a lot and gives syntactically correct but logically wrong code sometimes.

I have been working here for the last 3.5 years in the same field of testing.

Windsurf is quite stable, but it is a little slower than Cursor.

Currently, one license is given to three users and we use those licenses very much. I haven't seen it slowing. Windsurf runs at its own pace, but we haven't tested it in a scalable manner. I haven't tested what would happen if one license were given to nine people. With one license for three people, it was going smooth.

I haven't had a chance to talk to customer support because I haven't had any sort of issue using Windsurf.

Previously, we were using Cursor but it was really costly, so our team moved to Windsurf.

Time saved is very evident. Because time has been saved, if I am completing a task in five days rather than ten days, I can pick another task in the remaining five days. The product team can now delve into many more features based on the feature flags and test them a lot. Previously, we would test one feature. Now, we can also experiment. For one feature we can have two builds and test them on the same timeline. This helps us choose better.

I wasn't given the opportunity to choose because these things were just given to us by my managers. I was not given a chance to explore other options.

I want to tell people that they should start using an AI-assisted IDE, whether it is Windsurf or Cursor. Windsurf is better because the price difference is really huge. Windsurf always provides a boilerplate for writing the code and test cases. As testers and automation testers, we can refrain from doing repetitive work. If one is really good with prompts, then they can have really good results. Prompts should be explicit and provide clear context around the use case. Always review your test cases and automation scripts before merging them because a review is a non-negotiable thing in AI tools. I would rate this review a 7 out of 10.

I have been using Windsurf for a few months now, mainly as part of my development and productivity workflow. During this time, I explored its capabilities for code assistance, faster debugging, and improving overall development efficiency. It has been particularly useful in speeding up routine tasks and helping me to focus on problem-solving rather than repetitive coding.

Our main use case for Windsurf is to make code changes and we are mainly focusing on development and productivity enhancement. We also use it to speed up coding, debugging, and understanding complex codebases, especially when working across multiple tools and systems. In my day-to-day work, I rely on Windsurf to generate code snippets, troubleshoot issues, and explain existing code logic.

Our main focus is generating code snippets, troubleshooting issues, and creating code logic. When dealing with errors or unfamiliar implementation, it helps quickly identify solutions and suggest improvements. This significantly reduced the time spent on debugging and research, allowing us to focus more on building optimization and workflows.

The best features in my experience include AI-driven code generation, which does not just autocomplete lines but understands the intent and can generate full functions and logic based on the context. Another standout feature is Cascade, an AI agent workflow tool. This allows the tool to handle multi-step tasks such as writing code, modifying files, and even running commands, almost like a junior developer assistant. I also find its deep codebase understanding and local indexing very powerful. It can analyze the entire project and give context-aware suggestions. Additionally, inline AI editing, built-in terminal support, and memory system make workflows smoother.

The feature I use the most is deep codebase understanding combined with AI code generation. It is incredibly helpful when working on existing projects or unfamiliar code because it can quickly understand the context and suggest accurate changes or additions. This saves a lot of time that would otherwise go into reading and figuring out the code manually. I also frequently use the code generation for writing functions, fixing bugs, or speeding up repetitive tasks. It helps me move faster without compromising quality. While Cascade is powerful, I use it more selectively for multi-step tasks.

One small but really valuable aspect I appreciate is how seamlessly it fits into the development workflow. It does not feel like a separate tool. I can interact with AI directly inside the editor without breaking my flow, which keeps productivity high. Another nice detail is its context memory. It remembers what I was working on and gives more relevant suggestions over time, so I do not have to repeat myself or re-explain things constantly. Additionally, the inline editing and quick fixes are very smooth. Instead of jumping between tools or rewriting code manually, I can apply changes instantly, which makes day-to-day development much faster and less disruptive.

The bigger improvement is faster development and debugging. Tasks that used to take hours, such as unfamiliar code or fixing issues, can now be done much quicker with AI assistance. We have also seen better efficiency and reduced repetitive work. Developers spend less time on boilerplate code and routine tasks and more time on solving real problems and improving systems. Another impact is faster onboarding. New team members can understand the codebase more easily with AI explanations.

We have seen around 30 to 40% reduction in development time for routine tasks such as writing boilerplate, debugging, and understanding existing code. Tasks that used to take hours can usually be done in minutes. We have also noticed a 20 to 30% decrease in debugging time since the AI helps quickly identify issues and suggest fixes, reducing trial and error efforts. Overall developer efficiency has improved by 25 to 35% as less time is spent on repetitive work and more on actual problem-solving. Additionally, new developers can become productive much faster, around 30% quicker, by using it to understand the codebase.

Overall, Windsurf is a powerful tool, but one key area is the accuracy and consistency of suggestions. While it is very helpful, sometimes the generated code or fixes need manual validation, especially for complex or production-level logic. Another improvement would be better control over AI actions, especially in agent workflows like Cascade. More transparency and fine-grained control would help developers trust and use it more confidently in critical tasks. Performance can be improved slightly.

One area for improvement is around context limits and memory handling. Sometimes when working on very large codebases or long sessions, the context can feel limited. Improving how it retains and prioritizes context would make suggestions even more accurate. Another area is documentation and onboarding guidance. Since Windsurf is a relatively new tool, having more structured documentation, best practices, and real-world examples would help teams adopt it faster. Additionally, enterprise-level controls could be enhanced, such as better security, audit logs, and usage tracking.

One additional area for improvement is offline and limited connectivity support. Since Windsurf relies heavily on AI capabilities, having a more robust fallback or partial offline functionality would be useful in restricted environments. Another point is the customization of AI behavior. It would be great to have more control over how the AI responds, such as tuning it for specific coding standards, project styles, or team preferences. Additionally, integration with more developer tools and ecosystems could be expanded. While it already fits well into the workflow, deeper integrations with CI/CD pipelines, issue tracking, and monitoring tools would make it more powerful.

I have been working for the past two years in my current field.

Windsurf is stable.

Scalability is very good. As of now, we have a team of 50 developers where we provided Windsurf to all of the teams and they are using it very well. When it comes to scalability, it is very easy for the integration as well. Windsurf is scaling very nicely.

Customer support is very good. Whenever we have any query, we can raise a simple ticket and the following up is very nice.

Onboarding is quick and straightforward.

We are using GCP.

From a practical standpoint, the biggest ROI comes from time savings and faster delivery. We have seen around 30 to 40% improvement in developer productivity, which directly translates into quick feature releases and reduced development cycles. This aligns with industry insights showing AI coding agents significantly improve speed and efficiency. We have also reduced development cycle time by 20 to 25% and increased throughput by 12%. The developer time saved with AI assistance handling repetitive tasks is also notable. The ROI is visible through faster feature delivery, reduced manual effort, improved developer productivity, and better utilization of the engineering team.

The pricing is fair and flexible. It offers a free tier which is great for getting started and testing the tool without any upfront cost. The Pro plan is around 15 dollars per month which includes access to premium models. It is very minimal since it is a cloud-based tool and does not require any infrastructure or heavy installation.

Overall, Windsurf is a very good tool for engineering productivity and coding sessions. It also enhances the time productivity of the SRE as well. Windsurf is a very good tool to work with.

It is a powerful tool for handling repetitive tasks so that the engineer can focus more on high-value work such as system design and optimization. Tools such as Windsurf even provide analytics to track time savings and productivity improvements across the team. Making Windsurf a part of your team is a good investment. I gave this review a rating of 8 out of 10.

My main use case for Windsurf is to write code and to optimize the code in my day-to-day tasks.

I worked with a database like SQLite on the mobile app side, where I had many queries. I wrote one query for selecting data from one table and I used Windsurf to optimize that. After that, Windsurf suggested optimizations for all the queries, and when I type a method name, it suggests everything inside that. I used it that way to accept the changes from Windsurf.

The best features that Windsurf offers, in my opinion, include optimizing the code, and I can also use it for documentation, in the sense that it explains the code.

When I mention documentation, I am talking about Windsurf's ability to help understand the code, rather than automatic documentation generation.

Windsurf has helped me a lot by reducing the development time. By using Windsurf, I have reduced my time by 30% to 40%.

I do not have anything to suggest for improving Windsurf at this time.

I have been using Windsurf for about two years, as I started working with Windsurf two years ago when it was Codium.

I rate Windsurf a perfect 10 because I used it extensively when I was working on a task, and I completed it before the deadline, earning some recognition from my organization.

Currently, I am using Windsurf in my VS Code. I did not purchase Windsurf; I just added the extension in VS Code.

I recommend that you check Windsurf based on your requirement to see if it is useful for your needs. My overall rating for this product is 10 out of 10.

My main use case for Windsurf is creating scripts that move data from Python scripts to transfer data from Teradata to Snowflake. I also used it for automating all the data loading processes, which pull data from the landing area of the data warehouse and push it to the integration layer. From there, it schedules an email to create a report from those integrated data, creates a view on a different semantic layer, and generates a chart—either a pie chart or bar chart report—to send to required stakeholders.

I have two other cases with Windsurf. The first one is an AI chatbot that we are in the process of building with the help of Windsurf. Basically, that will connect to Tableau.

The best feature Windsurf offers is the ability to plan the job or task first and then create an executable model. This gives us a clear picture of what I am going to do, what I will receive, what the outcome will be, and how it can benefit the end user.

The planning and executable model feature helps my team day to day by saving a lot of rework. Most of the time when you ask a particular question to Windsurf, you miss bits and pieces of where to begin and what to end, and what to skip and what to add. However, during the planning phase, you can collaborate with Windsurf to make your plan accurate. When execution happens, you get the desired result without going through the rework of returning to planning after getting the result. It breaks that chain. If your planning is perfect, execution does not need a lot of rework.

Windsurf has positively impacted my organization by helping us achieve at least ten to twenty percent improvement for each individual working in the data warehouse to use Windsurf instead of looking for help from any other team. For example, if a business stakeholder wants to get data about any report or any updates about any report, instead of asking a resource for an update, a business user can ask Windsurf to look into the tables and provide the report. This reduces the dependency on the front-end reporting team.

Windsurf can improve by making sure to ask the user if they are talking about the same context where the request started. Request number one might be related to creating a report, and request number two might be related to writing an email. Usually, Windsurf mixes those two requests because it does not ask the user if they are talking about the email or the first request. Windsurf takes it by default that both requests are related and continues. This sometimes creates rework.

I have been using Windsurf since one and a half years ago.

Windsurf is stable.

Windsurf's scalability is quite good. I think we went from a few hundred users to maybe four to five hundred users in our organization. There was no glitch or any issues while scaling across two different time zones and two different organizations.

I do not have any insight on customer support. However, whenever licensing is required, we always got a quick response from them. I am not directly involved in the communication with Windsurf support.

We have not used any other solution before now. Windsurf is our first starting point for AI.

At this point, I am not in a position to share the metric on return of investment. However, I can tell you right now the return of investment is mostly based on time and some part of money saved. At the employee level, we have not yet reached the point where we can purely say that we have actually gotten returns from Windsurf instead of an employee.

I do not have a clear understanding of how Windsurf pricing is set up in my organization. I am not part of the committee which took care of licensing across the organization. Right now, I think Windsurf is costing our organization differently than it started. We initially went with a bulk buy where the entire organization was available to use. Now it is ADFS login related, so every user can see their own number of ACUs, hours used, resources used, or credits used by Windsurf.

I am not in a position to give this answer because I am not a leader who decided on Windsurf. There might be a team which went through many other tools and compared them with Windsurf. That was my organization's management decision.

I give Windsurf a nine out of ten because you get automation done by Windsurf all the time. Not only automation, but whether writing an email, writing a document, or creating detailed information, it gives you detailed insights. I deducted one point because of the rework and training that needs to be provided to the Windsurf agent to make sure it is useful for your job. I feel a nine is already a very high number.

I can advise that Windsurf has almost all the available agents, starting from Claude or any other AI tool or AI LLM model being used. We have the highest level of agent to the lowest level of agent which can help you in day to day activity, whether writing an email, looking into PDF files, looking into an Excel sheet, or creating a Python script. Windsurf has a vast variety of AI models available, and that gives a lot of flexibility and cost savings.

My overall rating for Windsurf is nine out of ten.