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.

Public Cloud

Other

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.

Hybrid Cloud

Google

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.

Public Cloud

Google

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.

Private Cloud

Other

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.

On-premises

Other

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.

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.

On-premises

Other

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.

Public Cloud

Amazon Web Services (AWS)

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.

Public Cloud

Other