Windsurf Reviews

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.

My main use case for Windsurf is to build our software, as our company has a software product and operational automations. I work in the software development team as well as in the automation team, so for both of those, I write code and use its agentic capabilities. We also use Cursor alongside Windsurf.

Recently, we had to create a presentation workflow where we have a reporting module in our product that includes a Google Slide-based presentation part. I used Windsurf to code the whole thing as the proof of concept, and I have continued using Windsurf extensively when coding.

Windsurf is not just a code editor for me. I really use its agentic features and the chat as well, and those are the main ways that I use Windsurf. I use it for normal coding and also for agentic workflows.

The best features Windsurf offers include code line suggestions through auto-completion and the coding agent, which tends to understand the whole codebase, which is a great point. Its speed is also good when compared to some of the other tools in the same category.

The coding agent understanding my whole codebase helps me day-to-day because if we need a feature done quickly, its understanding really helps. I only have to give it a minimal prompt, and it understands how things are done around here and just does it. Without that kind of understanding, I would need to mention the helper functions, models, repositories, and other details, but because it understands, it can pick how to do the work correctly without being incompatible with the other code.

Windsurf has positively impacted my organization by increasing productivity. Once we started using these AI-powered IDEs and code editors, our daily number of commits, the amount of code pushed daily, and release speeds have gone up at least by 200%. Developers can now work on a lot of business logic in a single day because of these AI agents, which has fastened our release cycles and improved our product performance.

The increase in release speeds and the amount of code pushed daily has gone up by 200% based on the number of commits and the amount of features we complete within a single release cycle.

Even though Windsurf has a good understanding of the codebase, sometimes when you ask it to do a complex task, it may complete it in the first try but may need improvements. When you prompt it again repeatedly, after the sixth or seventh prompt, it may start performing poorly. That is one of the key things I have noticed—its performance is not very consistent with continued prompts. Additionally, it sometimes hallucinates things. Although this previously happened a lot, it has improved, and now these checkers and linter functions ensure the code it writes is correct, reducing those hallucinations, but the performance inconsistency is still something I have noticed.

The UI is good as it is basically a Visual Studio fork, so it has a good user interface. Moreover, every Visual Studio integration works here, so all of those are good. One of the main issues is the agent hallucinating and the generations not being good; those are two main points.

I have been using Windsurf for the last 10 months.

Windsurf is stable in my experience, although occasionally the response times lag due to API availability and other factors. Other than that, it has been totally stable.

Windsurf's scalability is good. I believe it can handle growth effectively as our team expands or projects get bigger because it keeps the context of the codebase in a specialized, cached way. I have seen how it works in some videos, and if that is true, I think it can handle the growing number of codes and files in our codebase effectively.

We initially used GitHub Copilot, but it was not efficient and lacked most of the agentic capabilities it later developed. That is why we switched to Windsurf and Cursor.

Windsurf is deployed in my organization as a cloud SaaS, working on public cloud.

Windsurf has saved a lot of time, and productivity has gone up at least 200%. Those numbers are real and might be even higher. We have not been hiring anyone new for the last six months, possibly because of that. The scope of work has increased a lot, but we managed to keep up without hiring due to the power of these AI-powered coding agents like Windsurf and Cursor.

Our management set up Windsurf, but I do know they evaluated Google's Antigravity recently but chose not to go with it.

My advice for others looking into using Windsurf is to use it as an AI agent and a supportive tool, but not as a whole AI solution. Never accept everything it writes, as sometimes it can generate incorrect code, and it can hallucinate or produce harmful code. Always review everything, and although it saves time, spend a little more time refining it. The time you invest will save you a lot of trouble later. Try to understand how it thinks to give it context effectively, leading to better outcomes.

My main use case is to build the development product that I am working on, so I used Windsurf to build 90% of my features. I used the agent present in Windsurf and prompted it to explain my feature, after which Windsurf ensured the feature was being completed. If the output was not optimized, I asked Windsurf to optimize it again.

I have built a platform that helps users create no-code AI agents, where users can simply come and create AI agents. In addition to this, we also provide an API. Using that API, we call multiple LLM providers. Using Windsurf I was able to create the backend of this product. I made a common middleware using which for every different payload, Windsurf is able to convert the current payload into that particular service provider payload and we can call the particular provider. For our users, the user only has to send one payload and we can call any model or any service they want. We are not restricted to a particular payload. This way, we created a proper robust solution.

The best feature is that you can drag code into the chat section and talk to that code, plan according to your features, and update that code. This feature makes my workflow easier by simply dragging my code to the chat box and Windsurf updating it. Windsurf does not hallucinate by simply writing a prompt. If I do not select the code, then Windsurf hallucinates and tries to change different code present in different sections of my repository. After selecting the code, Windsurf gets the proper exact location where it needs to make changes.

When I am building a feature that requires multiple changes throughout the entire repository, I write a complete prompt by selecting the files in which the required changes are meant to be done. This way, Windsurf makes sure that all the changes are done in that particular file only, and it does not hallucinate. I do not need to repeatedly tell Windsurf what to do, as it completes everything in one go.

The development has become significantly faster. Previously, the development was very slow, and after using Windsurf, my development speed increased by 70 to 80 percent. We are able to debug fast, build features fast, and deploy fast. This way, we ensure that our customers get as many features as they want in a very short time. We also ensure that our code quality is properly optimized by using Windsurf. We prompt in such a way that we write the best code that is bug-free and has as low error rate as possible.

There should be an auto model feature just like Cursor. Cursor provides an auto model feature that detects your task and chooses the best model for your task accordingly. This is cost-saving compared to using the highest cost model and will help to achieve better accuracy.

There should be an option where Windsurf should be able to connect to different integrated platforms such as Slack in a very easy way. If I want to build a feature, then I can simply type on my Slack and the feature should be ready. This way we can ensure that more and more features are built and Windsurf can simply return the pull request of the particular feature. A proper streaming should exist between Slack and Windsurf or any other channel.

Windsurf can be improved by introducing a plan method in which the user is asked all the necessary questions and a proper plan is generated with user metrics provided.

Sometimes, Windsurf is not able to develop the feature that we want and hallucinates a lot. The hallucination should be very much less compared to now as the models are evolving day by day. Windsurf should evolve in such a way that it should take custom prompting. It should ensure that an agent.md file is already present in the code base where the user can write how the agent should work, how the agent should react, and what it was explicitly trained for. This way, there will be much less hallucination and more code generation with better quality, and Windsurf will work according to user requirements.

There should be a proper markdown folder specifically designed in Windsurf for each product.

I would like to suggest that there should be one more feature that I am not able to use. Currently, I am not able to work on two repositories at a single time. Suppose I want to develop a feature that requires changes in both my frontend and backend. Windsurf lags in this scenario. It can only have access to the backend or it can only have access to the frontend and not both. If this feature comes, then Windsurf will be top in the market.

I have been using Windsurf for two years.

Previously, Windsurf was not stable. It used to not change a particular file, but now it is stable.

Windsurf can handle a lot of users at a time. Our team is working around 10 to 12 members on a Windsurf account and they are easily able to do it. There is no lagging in that. Windsurf can ensure that multiple users can work simultaneously on a single product.

The customer support was not that good. It is mentioned on their website that you need to mail customer support to contact them. There is not an easy way to reach them. The experience was not so good as we have to wait for them to reply to our mail. I feel this is a very low rating.

Previously, I used Cursor, but Windsurf was better at that time. However, after Cursor evolved in a great way and now it works very fine.

Money is saved and time is saved up to 80 to 90 percent because we are able to deploy fast. Money is also saved as we require fewer developers to work on a particular task. Before using Windsurf, we used to have 20 employees on a team, and now we have 10 employees only.

I would say Windsurf is a better platform at low cost, around $15 per month. Compared to other costs, it is lower. It is a good value. If you know prompting, you can do any task with Windsurf.

I used Cursor as a solution because in Cursor there is a planning method and I can work on two repositories at the same time. Due to this, I would like to switch to Cursor. There are many more AI tools in the market now that are giving better responses compared to Windsurf. AIdrivity, Cursor, and Open Copilot are the top ones that are giving better responses.

There are multiple purposes for which I am using Windsurf. The accuracy of Windsurf agent was not directly good, but after I provided a few things, I received better results. We use it in a public cloud and have bought a plan for Windsurf for our team members, where we can communicate with the entire team using a plan. I would give this review an overall rating of 6.

I am a software developer with over two years of experience. I have been using Windsurf for approximately one to one and a half years since its introduction into my organization. I primarily work in enterprise application development and use Windsurf on a daily basis as my AI-assisted IDE to improve my development productivity and reduce the time required to understand large codebases.

Windsurf has been in use at my organization for one to one and a half years. Previously, we used GitHub Copilot as our AI assistant, but given the features that Windsurf brings to the table, my organization shifted to Windsurf, and I have much to share regarding its features and how it has made my life easier as a developer.

Windsurf is available as a separate IDE and also comes as a plugin. Developers use IDEs such as IntelliJ, and we can use it as a plugin there, but it is recommended to use Windsurf IDE itself, which provides many features. Understanding large codebases was a big time saver for me. Instead of opening forty Java classes, fifteen Angular files, and configuration files and APIs, I simply ask Windsurf to explain a module, how a request flows, or which classes are involved. It understands the relationship between files and explains the architecture.

Another feature I use on a daily basis is Cascade chat, which has two modes: Ask mode and write mode. I use the write mode seventy percent of the time because I can ask it to explain an implementation and also implement it side by side.

A favorite feature I find in Windsurf that is not present in other tools is Code Maps. As a developer, I sometimes need to understand the whole architecture and determine where to fit in a new feature or fix existing bugs. Understanding the architecture usually takes considerable time, but this feature allows me to visualize the current architecture of a very large codebase visually. When I click on the architecture, it takes me to the particular code where it is written. I can relate the architecture and the code side by side and understand the whole picture. This is one of my most favorite features because previously, I needed to rush through code in every file to understand what was happening behind the scenes. The overall architecture and that link with the code is a huge boost for developers.

Windsurf also has rules, which allow me to guide how AI generates responses. For example, if my organization follows a particular set of rules, I do not have to write them every time in the prompt. If we use Java seventeen and follow Spring Boot conventions, naming conventions, and should not use deprecated APIs, all of these repeating items can instead be written as rules inside Windsurf, and it will automatically take them into account in every response.

Similar to rules, there is another feature called skills, which allows me to use reusable prompts or workflows. For example, if there is a testing skill that generates test cases or edge cases using the Mockito framework, or a documentation skill that generates documentation or explains an API using something like Swagger, I can summarize a module. Instead of typing everything, skills make development much faster by allowing me to reuse prompts. Workflows is the bigger version, the superset of both these things. It helps me combine multiple AI actions together. For example, starting from requirement gathering and understanding, designing, coding, and then pushing to GitLab or GitHub, I can follow these procedures and later push to a specific branch. Rather than doing this every time, I can write these workflows separately and simply call them out in Windsurf. These are very specific features that Windsurf offers that I did not find in other AI tools such as GitHub Copilot.

Room for improvement is actually associated with every AI that exists. One thing is that if there is a very large codebase, such as a legacy codebase, sometimes the context window is a hindrance. There is a particular context window for every model, and for Windsurf in particular, if it is a very large legacy codebase, then you might have to pull in all of that code and only then maybe you will get the whole context. For very large projects, getting the context together intact is one thing that needs improvement.

Another thing that I feel needs to be improved is hallucination. Whenever the context is not set, AI occasionally needs validation. It is always recommended that human reviews are also required when Windsurf generates code. You need to review it always. Performance with large indexing operations or when using very large models and efficient models such as Claude Opus takes a lot of time. There is nothing much that can be done about it as it is all about the model, but sometimes large indexing operations do take time.

One more improvement that I feel is about documentation. There are a few other features I have not mentioned so far. There is one more feature called MCP, which is Model Context Protocol, present inside Windsurf. Instead of just chatting and asking it to generate code, I can use MCP to integrate it with external tools, whether it is Splunk, Veracode, or SonarQube. I can integrate it with all the external tools as well as internal tools and documentation, and it will do the work for me. It is not just that it will search in public and get details, which sometimes may hallucinate. MCP is one thing, and for advanced features such as MCP and workflows, sometimes I feel that documentation is not in detail. Windsurf could have better documentation for MCPs and workflows, and there are other niche and specific features that are present only in Windsurf.

I have been using Windsurf for one to one and a half years.

Stability is quite good. Only when the network is off or very rarely, Windsurf is almost 99.999 percent available and stable. I have no issues with that.

Windsurf works well for small projects, medium projects, and large enterprise repositories. The value actually increases as the project complexity grows. Features such as Code Maps prove to be of great value when project complexity increases, allowing me to make use of the features that Windsurf provides.

I have not had the need to contact customer service in my experience because the onboarding was smooth and the details and features that Windsurf provides are all good. We did not have any technical issues so far. Sometimes if there are any hiccups or if Windsurf does not respond, I simply quit it and restart my system, then start working again.

We had GitHub Copilot earlier, which we were using as our AI assistant.

The onboarding to Windsurf was easy because we had an internal document explaining how to onboard it into my organization. It was just adding a plugin to IntelliJ IDE and then downloading Windsurf from our own self-service portal. Once we downloaded it, we just had to set the proxy and we were all set and ready to go.

From the developer's end, maintenance involves the token allocation. There is a specific amount of tokens allotted to every developer monthly. I just have to make sure that the models I use in Windsurf, I do not keep using high-value and high-performance models every time so that I do not consume all tokens and be left with no credits. This is the only thing that developers have to take care of, but the maintenance is actually taken care of by the platform and the management.

Regarding pricing, I am a developer, and the pricing is handled by my management team and the platform team. As a developer, I was mainly focused on using the product rather than the procurement side.

I have also worked with SonarQube, and if that is an active product, I can provide a review for that as well. I have also been using Microsoft Teams very frequently.

A favorite feature I find in Windsurf that is not present in other tools is Code Maps. The onboarding to Windsurf was easy because we had an internal document explaining how to onboard it into my organization. From the developer's end, maintenance involves the token allocation, and there is a specific amount of tokens allotted to every developer monthly. Windsurf works well for small projects, medium projects, and large enterprise repositories. Room for improvement is associated with every AI that exists. My overall review rating for Windsurf is nine out of ten.

My main use case for Windsurf is developing features and asking it to develop code for my projects, debugging the projects, finding and fixing errors, creating a SQL schema for Supabase, connecting to Supabase, and creating the README.md file or a plan for the project.

A recent example of using Windsurf CASCADE agent was building an ideology analyzer where I had to provide requirements. It needed to build the pipeline using Python and connect the Grok API with the OLAMA model and perform the embedding to Supabase for the book while connecting with the embedding model of OpenAI.

Windsurf really helps me to fix bugs. I copy-paste the code or the error, and it helps me to find where the bug was and fixes it by itself. I mostly rely on starting projects from the ground up and working alongside it.

I think the most important feature that Windsurf offers is having different models connected to it. Second, it does not execute the code without permission, which Cursor and different IDEs do. I trust Windsurf because it does not have errors and no bugs like Cursor.

Some of the models are not good at some tasks. If I have a bug in my program that I want to get fixed, sometimes Codex could not fix that bug, and when I switch to Claude 4.6 Sonnet, it fixed it right away. I use different models for different tasks, especially for debugging and fixing complex errors. Sonnet 4.6 sometimes works really well, while Codex or GPT-5.2 is effective for complex feature building. The flexibility of using different models really helps. The permission feature is actually really important because every time I implement a feature, it informs me that it has executed the code, and I need to accept the changes. I can revert it back as well, which really helps.

Windsurf has a good feature that assists me in reverting changes if I have made them. If I accepted a change and then it does not work because it often happens that it implements functionality but does not solve the issue, I can revert those changes back, which restores the code to its original state before the changes were made.

Windsurf has positively impacted my organization by increasing the productivity of my project and software development by 100 percent. We have deployed and pushed new features into production much faster compared to what we used to do before using Windsurf. I would say it increases 90 percent of our productivity, helping us not to get stuck on small or big issues, which are handled well by Windsurf.

We have increased our productivity through deploying and releasing more features for our product compared to what we used to do. What used to take months to accomplish is now done in just days, and we deploy more features in a week than we used to do in two weeks or sometimes a month.

Currently, they just introduced the Agent, and there are not many guidelines on how the agent is going to work. The UI does not look very friendly and is not self-explanatory. I think that is an area for improvement. Also, the pricing models have been recently changed regarding credit usage, adding a weekly and daily quota limit, which hinders our production and productivity. I believe they should provide a more transparent pricing model and credit usage structure so a customer or user can understand how many credits are used daily. If we produce good features weekly and hit our daily or weekly quotas, we cannot continue without buying additional credits. This really hampers our productivity and capability to build more quickly.

I think they should improve their pricing and remove the daily and weekly quota limits on production so we can build without the fear of hitting those quotas.

I have been using Windsurf for the past eight months.

Windsurf's stability is a good question. I do not know if it is due to my computer configuration or Windsurf itself, but Windsurf CASCADE agent pane on the right side does not open most of the time. CASCADE does not initialize when I open Windsurf, causing it to crash repeatedly, requiring me to close and reopen it. Therefore, I would say it is not stable for the most part.

Windsurf can handle larger projects and teams easily.

Their customer support is pretty fast in answering questions. I had an issue with billing, and the support team reached out to me about that issue quickly.

Previously, we just had to rely on experience to go through each file or identify the root cause of the error. If we could not fix it with our experience, we searched Google and Stack Overflow, taking the traditional route to resolve issues, which sometimes takes days to fix problems.

The installation process is pretty easy and is not complicated, so I think they are doing well in that aspect.

We use Windsurf IDE in our organization as a private solution on our systems, on-premises, or on machines. Every developer downloads and uses Windsurf on their respective machines.

I cannot recall exactly, but we have saved a lot of time. I would say 20 hours of our work by using Windsurf and solving complex bugs and errors in our software without spending excessive hours on those bugs.

My experience with pricing, setup cost, and licensing was pretty simple, and it was very positive.

I have not considered any other options when choosing Windsurf.

I advise others looking into using Windsurf to plan properly before even giving any prompt to CASCADE. Providing a proper plan ensures that it will give you a proper response to solve your problem much faster compared to an incomplete or out-of-context prompt. The more context and a well-organized plan you give to Windsurf, the better the response you will receive. I would rate my overall experience with Windsurf as an 8 out of 10.

I use Windsurf mainly to code projects that take less time to build based on prompting alone, which is similar to whiteboard coding.

I would not call it prototyping; I would call it complete end-to-end projects that I have built with Windsurf. For example, in my current work job, I built a delivery automation platform in Node.js using Windsurf. This involved integrating a Telegram bot into a Windsurf project to create a bot where delivery personnel can retrieve their daily delivery list. Everything was coded in Node.js with the help of Windsurf AI. For this project, I used the Claude Opus 2.7 model that Windsurf provides. It was quite quick for me to build this complete end-to-end solution and deploy it.

The time consumption has reduced drastically. If I had to build any project that would take at least a month or so with the required effort, I think it has been reduced to approximately a week, and the effort has also been reduced drastically.

As I explained, the delivery automation project that I am currently building would have taken easily at least 1.5 months minimum, considering I also had to deploy it. Now it only took me at most two weeks to build this from scratch, test it, and deploy it. Time consumption has truly been the most useful benefit.

In terms of cost savings, if I am building projects, it is saving more time, and I can utilize that extra time to build something else that is more important to the company. I would not say it is particularly about cost. The quality has obviously improved. Even though I come from a technical background, I think with the new models that Windsurf is providing access to, such as the latest ones from Claude or OpenAI models, their output and efficiency is much better than a person coding it. The quality is also better.

The thing I appreciated about Windsurf is the free AI model called Cascade, and it also provides SWE 1.5. Both of these, especially SWE 1.5, are very good at coding. Initially when I was in my college days, I did not have to jump onto the paid plan to build projects. Instead, I could use it with the SWE 1.5 model and build my blockchain projects for my college coursework. It was also quite quick. To elaborate, I was interning as well as studying in college. In that small amount of time that I had to quickly code and build this entire blockchain project for my coursework, I had to depend on Windsurf AI, for which I am very grateful, especially for SWE 1.5. Since I was still in college, I did not have to jump into the paid plan, and the free plan that included the SWE model worked out very well for me.

The best features Windsurf offers include the ability to handle prompts very maturely, as if I am explaining it to an engineer, so the model's understanding capabilities are very high. I would say even the free models that provide the SWE model are superb with understanding and planning out the entire approach. For example, if I have to start with a project, I only provide a brief about what I want, and Windsurf has already planned out step by step how and what to do. That is one significant feature. Second, as I mentioned, it takes the extension from VS Code, so the user interface is something I am already accustomed to. Furthermore, because the core objective of Windsurf is that you give prompts and it will do the coding, that core objective itself stands out. Nowadays AI is very involved in the work, so Windsurf has done a great job for coders where the coding is handled by the AI, and I just have to prompt. This is helpful even for non-technical people. They do not have to think or understand deeply into the code. Even the basics are fine, but not being deep into the code is acceptable if they do not know. By prompting and improving the prompts for Windsurf, I think they will get things done. I am grateful to Windsurf for this.

In summary, with the help of all these features that Windsurf provides, what is happening is the time consumption has been reduced drastically. If I had to build any project that would take at least a month or so with the required effort, I think it has been reduced to approximately a week, and the effort has also been reduced drastically.

Windsurf could be improved as it is lagging now compared to a competitor platform like Claude. It could have integrations with multiple MCP servers, compared to what Claude could offer. That is one area for improvement. Another area is the local hosting of Windsurf rather than just using it and downloading it. I think if I could remotely access Windsurf through prompts and such, it would also improve the features in this aspect.

Support for Windsurf could be better. I have faced the issue of having a paid plan but only being given free trial access. Even though I was on the paid plan and I had already paid, I needed that access instantly instead of being locked up with the limitation that the free trial had. The free trial only gave 100 credits, whereas my usage was even more than that. I had to be limited for that 10 to 14-day period that exists in the free trial, and only after that could I fully utilize it to its potential. That is one issue. This is something that most people have faced. I actually went through Reddit and other platforms, and this is something that most people have commented on, but it is still not yet corrected by Windsurf. That is one concern. However, the performance is good.

I have used Windsurf periodically for months. Since my college years, I have also used Windsurf to build projects and coursework. In addition, I use Windsurf in my current work job.

I think others looking into using Windsurf should start with the free plan and focus more on prompting. The best thing you could do is use the free models. You can use ChatGPT or Claude to get a proper understanding. First, explain what you want to those tools, get a prompt for it, and then use that prompt in Windsurf so that the output is much better than randomly saying things to Windsurf and asking it to get the job done. If you have a better understanding, you can go to ChatGPT or Claude, get a prompt from them, and then use that prompt in Windsurf so that the efficiency and output is much better. I have given this product a rating of 9 out of 10.

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 to speed up code generation and improve development efficiency. I use it as an AI-assisted tool to generate code faster, especially for backend APIs, data ingestion pipelines, and AI workflow agents. This has allowed me to reduce manual coding effort and accelerate development by 25%.

A specific example of one of the best use cases was when building an automated CSV ingestion service. Windsurf helped me generate boilerplate code and data validation logic quickly to onboard new vendors and product categories, reducing manual coding time and improving development velocity by more than 25%, which is critical in meeting tight deadlines for product catalog updates.

One of the best features Windsurf offers is AI-assisted code generation to write boilerplate and repetitive code, along with support for multiple programming languages and frameworks. Integration with development environments provides real-time code suggestions and completions, enhancing productivity by allowing developers to focus on complex problem-solving rather than repetitive coding.

Windsurf integration with my development environment helps my workflow by providing real-time code suggestions and completions directly within the IDE, reducing context switching and speeding up coding by offering relevant code snippets and boilerplate as I type. For example, I use it mainly with Visual Studio Code, supporting multiple languages such as JavaScript, Python, and TypeScript, allowing me to generate backend API code and AI workflow agents quickly without leaving the editor.

Customization options allow tailoring of the generated code to fit specific project standards and architecture. Integration with various frameworks and libraries ensures consistency across all application parts. Windsurf has a significant impact on my organization by improving development and code quality, contributing to smoother, faster, and more reliable software delivery.

Windsurf can be improved by enhancing support for more languages and frameworks. Improving context awareness to better understand complex project structures and generate more accurate code suggestions would be beneficial. Adding advanced debugging and testing assistance, including unit tests and integration cases, would enhance its effectiveness.

Optimizing performance to reduce latency and adding more intelligent error detection suggestions during code generation would also be beneficial. There is room for improvement in context awareness, better debugging capabilities, and reducing latency issues.

I have been using Windsurf for approximately two years.

Windsurf is stable approximately 95% of the time, but there are occasional latency issues that occur.

Windsurf can be scaled in a better way at the organization level.

Windsurf customer support, from my experience, is good. It compares favorably to community forums and other direct support channels. Technical issues are handled very well when they arise.

Previously, we used direct LLMs such as ChatGPT and Claude. With Windsurf, we use an AI-assisted tool that automatically generates code and helps significantly. We evaluated options between Cursor and Windsurf, and we found Windsurf to be better than Cursor.

Time saving is one of the significant benefits with Windsurf. It has saved a substantial amount of time compared to previous methods, with a 25% time reduction in code generation and completion of projects.

Pricing and setup costs are handled at the higher organization levels. We use Windsurf provided to us.

We evaluated options between Cursor and Windsurf, and we found Windsurf to be better than Cursor.

From my experience, I would advise taking time to explore AI-assisted code generation features to boost development speed. Use it alongside traditional coding practices to ensure code quality and maintainability. Leverage community forums and support channels for debugging and best practices. I would rate this product an 8 out of 10.

Our main use case for Windsurf is accelerating the development for all the client projects that we handle, especially when we are building websites, AI agents, and automations.

For example, when we need to create a landing page or a workflow for a client, we use Windsurf to quickly generate and refine the code, test ideas faster, and then reduce the time spent on repetitive development tasks.

We majorly use Windsurf to speed up the coding for client work and especially for all the websites that we design day in and day out. For the AI agents and automation projects, we use it extensively.

Windsurf has positively impacted our organization by helping us work faster and more efficiently.

Since we started using it, we have been able to move from an idea to implementation more quickly, reduce the repetitive coding, and spend more time on higher-value work such as refining client solutions and testing different approaches.

This has helped our small team stay productive across multiple projects.

The main improvement has been time savings, and we can move faster on websites, automations, and AI agent workflows so that we can take on more work and spend less time on repetitive development.

In terms of metrics, we are an eight-person team, and we were earlier handling a couple of projects because we had to do a lot of coding from scratch.

Now that Windsurf is in place, we are able to handle 14 different projects.

The prototyping has been remarkably quick.

When it comes to time-saving, it has saved a significant amount of time for us, and the initial effort has been substantially reduced.

The best features Windsurf offers for us are the fast code generation and intelligent suggestions.

They help us build faster, reduce repetitive work, and keep momentum.

The code it generates is of high quality.

With fast code generation and intelligent suggestions, I find the suggestions generally accurate enough to be useful and the code it generates usually gets us most of the way there.

We still refine it, but it reduces a lot of time and a lot of initial effort that we had to do previously.

What I appreciate the most about the features is that it keeps us moving.

For agency work, where we juggle multiple projects, that smooth workflow is really valuable because it reduces context switching and helps us stay productive.

The main improvements I would suggest for Windsurf are stronger context handling for bigger projects and a bit more control over the code it generates.

This would make it even smoother and faster for our agency work.

I would also appreciate a cleaner UI for larger projects, especially when there are many files and moving parts.

That would be a valuable addition.

Better integrations with our existing tools would help too, so we can move between coding, testing, and deployment more smoothly.

Overall, these improvements would make it even better for agency-style work where speed and clarity matter the most.

Regarding Windsurf's AI capabilities, it seems solid for general use, but because we work on client projects, we stay cautious with sensitive information.

More visibility into security controls, permissions, and data handling would make it even better for us.

It is adequate for our current needs, but stronger governance controls and clearer security options would be beneficial.

Beyond what we have discussed, a small improvement would be more consistency in the output on complex prompts and better context retention across longer tasks.

I have been using Windsurf for about 15 months now, mainly for development and AI-related workflows.

Windsurf has been stable for our agency work overall, with no major reliability issues.

In our experience, Windsurf has been scalable for day-to-day use cases and larger tasks.

It should support growth reasonably well, though performance and consistency would need to be monitored as usage increases.

It has scaled well for our current needs and appears suitable for large projects as well, with some attention needed as usage grows.

We have not needed to reach out to support very often, but when we did, the experience was generally positive and responsive.

We did use other tools before Windsurf, but we switched because Windsurf fit our workflow better and felt more efficient for day-to-day use cases.

The onboarding process was smooth overall.

New team members usually became very comfortable with Windsurf very quickly, and we only needed a brief introduction to get them started.

Windsurf integrated reasonably well with our existing tools and workflows.

It fit into our development process without much disruption, and we were able to use it alongside our normal setup.

It has improved collaboration by making work more consistent and reducing back and forth during the development.

We have seen a lot of positive return on investment, mainly through the time savings and improved productivity.

Earlier we were handling two projects, but now we can handle 14 projects.

It helped us reduce a lot of manual effort and speed up the development and support of multiple projects.

It reduced the initial effort, improved our productivity, and helped us save a significant amount of time.

In our case, Windsurf's pricing and licensing were reasonable and straightforward to work with, so we did not face any major setup complexity and the process was smooth from a procurement standpoint.

We evaluated other tools as part of the selection process, but Windsurf gave us the best balance of usability, integration, and productivity.

We went with Windsurf because of these advantages.

My advice would be to start with a small pilot project first so that the team can get comfortable with the workflow before rolling it out more broadly.

It is also worth setting clear guidelines on when to use it and having someone review outputs for more complex tasks.

Start small, define usage guidelines, and review outputs clearly at the beginning and you will see significant improvements.

I would rate this review 8 out of 10.

I have been using Windsurf for two years. My main use case for Windsurf is code building. I use Windsurf for code building on a day-to-day basis. We started with Windsurf as the first platform we used for Agentic coding.

Windsurf offers an easy place to engage with LLMs inside an IDE. What I find most valuable about engaging with LLMs inside the IDE is both the speed and the integration.

Windsurf has positively impacted my organization by allowing us to experiment and adopt Agentic coding practices before major platforms such as VS Code, Bard, and Codex jumped on board. It was one of the first that allowed us to use whatever LLMs we wanted inside an IDE to write code, and so it was where we started our frontier two years ago. Since adopting Windsurf, I have been able to downsize my staff and increase my output by 5x.

Windsurf needs to be improved because the entire coding harness needs to be rethought. We need a bigger IDE that is outside of what just a VS Code fork is at this point in time. To do that, we need something different. What that is, I'm not certain, but things such as inboxing models and inboxing on different tasks are needed. One of the big things that I'm using right now is Conductor.build, which satisfies a lot of these boxes, but even that doesn't feel total because what Windsurf has now is just the table stakes of what it is to be in Agentic coding.

I wish Windsurf would break down things to using things that we're used to and take us away and abstract away from the codebase more. I don't review code; I don't look at code anymore. My team members don't look at code; I have agents that review code, and that needs to be part of the UI. Looking at code is now almost obsolete.

Windsurf is stable.

Windsurf's scalability is fine.

The customer support for Windsurf is good.

Prior to Windsurf, we were using GitHub Copilot because it was the only possible AI solution around coding at that point. However, that game has significantly changed over the last few years.

I have seen a return on investment; I was able to reduce my staff, and I've watched my shipping metrics increase by 5x.

My experience with pricing, setup cost, and licensing is fine.

Before choosing Windsurf, we looked at Cursor and we looked at GitHub Copilot.

My advice to others looking into using Windsurf is to think big and dream big. I appreciate Windsurf and where it was before it was acquired by Droll and Antigravity came out. We just need to think bigger about the IDE and change the developer experience. I have rated this review an 8.