Amazon Q Reviews

In our development tools such as Visual Studio or Visual Studio Code, we use Amazon Q. I have installed that tool in my IDE. Based on our code or daily requirements, if we want to write or develop business logic, we use Amazon Q based on the requirement. If we get stuck somewhere, we also use it.

What I appreciate best about Amazon Q is the development tool which helps to recognize the code that is written and explain the logic. I'm not too aware of what all the recent features are. If there is any old code from an old technology that we don't have the skills for and want to know the logic behind it, I can select a particular part of the code and Amazon Q will explain what it is. If we want to write any kind of code, we can prompt it to Amazon Q and it will provide that code.

The support team from Amazon provides comfort because previously we used to think and implement, which took considerable time. If you wanted to create any project, it used to take around six months to one year, depending on the human resources. If your team required 10 members, since using the AWS tools we are now installing, I observed that we can deliver the project much earlier than before. We can complete our projects and requirements in three months or two months.

For improvement, there are a few things with Amazon Q as of now. If we give it complex prompts or complex logic and ask Amazon Q to write it for us, sometimes it will not give an exact result. At that time, we have to use ChatGPT or Gemini. For that kind of functionality, improvements are still needed.

Another thing I observe is that if you give any legacy application to Amazon Q and ask it to migrate to the latest technology, the conversion or migration will not be complete. Some things we have to do manually. Out of 100%, Amazon Q will complete 80% and the remaining 20% of the errors, including build or runtime errors, you have to resolve manually. The references will not be included.

I have been using Amazon Q for almost one year as I'm working as a developer now.

Amazon Q is stable. The only concern is the 20% manual requirement.

Regarding scalability, Amazon Q performs well.

The support team from Amazon provides comfort because previously we used to think and implement, which took considerable time. If you wanted to create any project, it used to take around six months to one year, depending on the human resources. If your team required 10 members, since using the AWS tools we are now installing, I observed that we can deliver the project much earlier than before. We can complete our projects and requirements in three months or two months.

Positive

The initial setup and deployment of Amazon Q are among the easiest, it's not too difficult. Anyone can install it if they have the experience or if they just go through the installation document. You can find the document online anywhere, and the steps are the easiest to download.

I think anyone can use Amazon Q, but they should know what exactly it is. They have to do some initial hands-on with basic documents because when I started using Amazon Q, I had gone through some small demo videos. Then I installed it and created one small project with Amazon Q. Once you do that hands-on, then you will understand how to use Amazon Q. It's really helpful if you're using Amazon Q.

I would recommend it to other people. I want to tell people that as many people start using the latest technologies, now everything is upgraded and everyone has to upgrade their skills. If they start using the new technologies and the new tools, it will be better for them to know the new things that have come in the tool. It is a good experience.

On a scale of 1-10, I rate Amazon Q an 8.

I'm using Amazon Q developer section and generative AI, and mostly I'm using AWS as my primary day-to-day tool.

I just got certified two to three months prior, and I've been using Amazon Q since then.

I'm pursuing my masters and currently working on different projects related to machine learning, AI, and model developing, so I need a lot of help in coding sometimes. Amazon Q really helps in those types of situations, and when I get stuck, when I don't know what the code is supposed to mean, or if I'm just stuck in a loop and can't go forward, Amazon Q develops and gives me a better solution of all the possibilities and scenarios I could run.

I appreciate its prompt because I have been using ChatGPT and Gemini, and Amazon Q is unique in that sense. It gives me exactly what I am looking for; if I want a code that is summarized and I want to know what the code means, it gives me a two-sentence summary, which helps me to learn about the code and then use it in my code offline of work and then develop further on.

I really appreciate Amazon Q. I'm trying to transition from ChatGPT to Amazon Q for my coding day-to-days and it has been very helpful.

It's very hard for me to comment because I've just been using it for three to five months, but what I could say is if they could somehow generate or collaborate with OpenAI, that would be a very big plus.

They could improve more. I'm a person who believes that everybody is imperfect, so for me, nobody is a 10 on 10. The maximum they can reach is nine, so that's the thought process I go by.

I got certified two to three months prior, and I've been using Amazon Q since then.

They're really helpful. Whenever I call the support system for any types of issues, when I'm really stuck or if it's not working, if the prompt isn't working, they're really helpful in that case.

I would give their support team a solid eight.

Initially, as I wasn't aware of Amazon Q, I discovered it while browsing the website. I was using Copilot and wanted a better user experience, which while searching online gave me the idea of Amazon Q, and I looked it up. It wasn't very difficult; it's very beginner-friendly, so any normal person can get into Amazon Q and really develop with it.

I haven't gone for the pro pricing, but I'm still using the free version of Amazon Q and it really works for me. If I'm in a work environment, I think I need to update to a pro version, but the pro version isn't that expensive to me because of my needs. It's really worth the money I'm paying.

I have logged in. I'm an Amazon user and order their products, but in terms of their technological side, I haven't subscribed to their pro versions yet.

On a scale of 1-10, I rate Amazon Q an 8 out of 10.

I have been associated with contact center business operations for over a decade. In the early stages of my career, we used traditional contact center solutions such as Avaya and Cisco. Over time, we transitioned to AWS in my previous organization, adopting Amazon Connect as a SaaS-based solution for our end clients and customers.

With Amazon Connect, we integrated various third-party tools to enhance functionality across different applications, including workforce management, real-time and post-call recordings, and advanced analytics. These integrations enabled us to deliver more, which were critical for quality management and performance improvement processes.

We actively use Amazon Q within Amazon Connect, primarily for business applications related to contact center operations. Our setup includes real-time call recordings and data collection that support workforce management activities such as forecasting and scheduling as well.

While we haven’t yet integrated Amazon Q AI, we are well-versed in its capabilities, including real-time AI features. As a client, we are planning to implement Amazon Q AI features soon, particularly in analytics. We expect to work with real-time use cases in the coming months, which will provide deeper insights and help refine our approach.

Our focus remains on optimizing contact center operations through end-to-end solution integration. Our goals include enhancing quality monitoring, leveraging speech and text analytics, deploying bots and GenAI techs, and enabling seamless cross-platform integration with CRM tools. Additionally, we are exploring AI-powered workforce management solutions offered by other platforms to further boost operational efficiency.

Amazon Q offers a wide range of valuable features that set it apart from other tools and technologies in the market. One of its standout advantages is the built-in redundancy, which ensures reliability and consistent performance. I particularly appreciate the agility and speed that Amazon Q delivers—especially when compared to other platforms offering similar solutions. Its openness to GenAI-based text capabilities and support for extensive cross-platform integrations are also unique strengths that many competitors lack.

A feature that has significantly helped us attract and retain clients is Amazon Q’s robust recording solution. In scenarios where real-time recording fails, Amazon Q automatically stores the recording in an S3 bucket. This allows us to retrieve and push the recording to our desired solution later—an invaluable capability that is not readily available with most other market offerings.

One of the greatest advantages of Amazon Q is the simplicity and speed of deployment. We can onboard new customers in just a few hours, which is exceptional. The product’s uptime is outstanding, enabling us to confidently promise maximum availability to our clients. The interface is user-friendly and intuitive, making it easy for both our teams and our clients to use without extensive training.

From a reliability standpoint, Amazon Q is nearly flawless. I have never experienced downtime, thanks to its redundant backend infrastructure that ensures consistent availability. Its performance in terms of service-level optimization has been highly consistent, which is vital when making commitments to end clients.

Moreover, the brand value of Amazon plays a crucial role. The strong reputation behind the Amazon name significantly simplifies the upselling process. When clients recognize they are using an Amazon product, it builds immediate trust. The combination of product quality and brand recognition makes Amazon Q a compelling and persuasive choice for our customers.

While Amazon Q offers many powerful capabilities, there is room for improvement—particularly in its speech analytics component. Currently, the accuracy of speech recognition and transcription lags behind some competing solutions in the market. Enhanced accuracy in this area is essential, and we are hopeful that continued product development will close this gap over time.

There are also notable features available in tools like Microsoft Copilot and Microsoft Teams that are currently missing in Amazon Q. One such feature is the ability to generate real-time AI summaries or wrap-up notes during or immediately after a conversation. For example, after a recorded interaction between two parties, it would be extremely beneficial to receive a real-time AI-generated summary along with live transcription. This would streamline documentation for agents, enabling them to quickly copy and paste relevant information into CRM or record-keeping systems.

Another area where Amazon Connect could improve is in its knowledge management integration. In today’s contact center environment, having robust and accessible knowledge bases integrated with live calls is critical for delivering accurate and timely information to both agents and customers. We would like to see more seamless and intelligent integration of knowledge management systems within Amazon Connect, especially during real-time interactions.

We have been using this solution for four to five years.

One of the standout strengths of Amazon Q is its exceptional scalability. When it comes to infrastructure management and data provisioning, Amazon Q offers clear advantages over traditional on-premise systems.

Unlike legacy solutions that often demand specific hardware, lengthy procurement processes, and complex configurations, Amazon Q enables rapid and seamless scalability. Infrastructure can be expanded within minutes or hours, not days or weeks, to meet growing data demands.

For example, if data requirements increase significantly—from handling 100 bits to a much larger volume—Amazon Q has the capability to scale instantly and maintain performance without disruption. This level of responsiveness is crucial in dynamic environments like contact centers, where data loads can change unpredictably.

This flexibility allows organizations to stay agile, reduce operational overhead, and focus more on business outcomes rather than infrastructure limitations.

The customer service provided by Amazon has been consistently positive. The support team is knowledgeable, responsive, and helpful, making interactions both efficient and pleasant.

I’ve had the opportunity to work with Amazon support teams across different regions, including India, Australia, and New Zealand. In every case, the teams have demonstrated a high level of professionalism and expertise, contributing to a smooth and reliable support experience.

Their commitment to resolving issues and providing guidance reinforces confidence in Amazon Q as a dependable solution—not just in terms of technology, but also in post-deployment support.

Positive

My recommendation is to avoid blindly adopting any product, whether it’s Amazon Q or another contact center solution. It's critical to thoroughly assess whether the tool aligns with your specific operational needs and solves real-time business challenges.

While many vendors offer basic functionalities like call recording at minimal licensing costs, the true value lies in how well the solution addresses everyday operational issues, supports resource management, and integrates into your existing workflows.

Before adopting any platform, organizations should consider the following key factors:

• Problem-Solving Capability: Does the solution effectively address your day-to-day and real-time challenges?

• Return on Investment (ROI): Are you getting measurable value compared to the cost of implementation and maintenance?

• Team Adaptability: How quickly and efficiently can your current team adopt and utilize the new technology?

In my experience, Amazon Q scores an 8 out of 10. It’s a powerful tool with excellent potential, but like any solution, it requires a thoughtful evaluation to ensure it fits your unique business needs.

My team and I were the end users of Amazon Q, and we used it for all our tasks including coding, testing, upgrading, troubleshooting, and security scanning. We primarily used it for code generation use cases where we were converting legacy applications into modern stack applications, though it was not generating the complete solution.

Amazon Q significantly reduced the time we spent on testing. It served as a great tool where we could ask questions, get answers, and complete testing efficiently. The solution provided cost optimization benefits because it was more cost-effective compared to other available solutions. Many tasks that were previously handled manually were managed by Amazon Q, resulting in savings of time and resources.

The completion feature of Amazon Q proved most valuable as it worked seamlessly and brought structure to our work. It was particularly useful for completing lines of code, specifically for docstring and for if, while, and try code blocks.

Amazon Q could be improved if it could generate full functions for certain use cases. While it did not provide all the answers in some cases, it still accomplished a great deal of work for us. I was expecting it to function more as a co-pilot for AWS environment, but despite not reaching that level, it still proved highly useful.

The solution was quite stable and never experienced major breakdowns. Though I haven't tested their VS code extension for potential malicious forms, I believe it is properly protected.

Amazon Q demonstrated strong scalability, earning an eight out of ten rating in this aspect. It was particularly helpful as an AI assistant for AWS infrastructure, where few alternatives exist. While not completely perfect, it provided satisfactory results for our services and requirements, including valuable cost optimization suggestions.

The technical support for Amazon Q was hassle-free with minimal wait times. When my team member interacted with support, all queries were resolved promptly, and questions about capabilities were answered clearly.

Positive

We had previously used Microsoft Co-pilot, but it wasn't fully aligned with AWS. Amazon Q proved easier to use on the cloud for AWS, giving it a clear advantage.

Amazon Q is absolutely worth recommending because it is stable and works effectively with AWS cloud, providing good value for money. The overall rating for this solution is 8.5 out of 10.

I use Amazon Q to find keywords related to Python and data engineering regarding pulling and pushing data from an API to AWS S3 bucket. I used Amazon Q for code suggestions in the script that I developed and in the improvement of the script.

The important feature that stood out when I used Amazon Q is that, when integrated with Visual Studio Code or common IDEs, it provides code suggestions while typing the code, which is the case with most of the intelligence agents right now. Amazon Q, as it integrates with Amazon services, provides a better context of which particular service, without knowing the actual properties of the service, can be utilized to enhance the operation.

The best feature Amazon Q offers is the code security scan, which actually depicts the threat assessment of the code or whether a particular code snippet can be utilized by attackers to find loopholes. This is something new that I found with this particular agent and LLM model it uses.

Amazon Q definitely increases productivity because there are certain scenarios where certain keywords or certain ways of process are unknown to individual developers. With a small hint or small shot of snippet that is shared with the developers, they can extrapolate the idea and find out which particular AWS service to be used. It is also more useful when working on a project that deals with AWS services. As AWS is the leading service provider in the cloud industry right now, it can be utilized with multiple services that AWS hosts, which is definitely beneficial.

I can think of a specific example that shows how Amazon Q made a positive difference; it definitely saved time. For one of the tasks that I had been assigned in my project, which I cannot disclose right now, I was given a time limit of three days. I had some ideas in my mind and some ways in process by which that particular task could be achieved. With the help of Amazon Q, the three days time limit that was given to me initially was reduced to 1.5 days.

One of the key points I feel could improve Amazon Q is the integration with the major IDE environments that are available in the market. The second thing would be its open-source availability to most of the IDEs which are open-source and free to use. That will definitely enhance the audience for Amazon Q users.

It would be better if Amazon Q worked with the IDEs that are developed by JetBrains.

I have been working in my current field for more than four years.

I have not experienced any issues with reliability or downtime regarding Amazon Q.

I have not experienced any issues with reliability or downtime regarding Amazon Q.

I don't have clear visibility for Amazon Q's scalability for handling increased workloads or more users smoothly.

I never reached out to the customer support for Amazon Q, but I believe it is an Amazon product, so it would be good only.

Positive

I previously used GPT-3.0, but as with the increasing data for GPT, the answers started becoming redundant and not accurate. The project also dealt with AWS services, so I switched to Amazon Q, which provided more accurate and implementable answers and suggestions.

I evaluated just the one option, GPT-3.0, before choosing Amazon Q.

My experience with the pricing, setup cost, and licensing for Amazon Q is that it was straightforward.

I don't have company-related infrastructure details with me regarding which cloud provider my organization mainly uses.

My experience with the pricing, setup cost, and licensing for Amazon Q is that it was straightforward.

I previously used GPT-3.0, but as with the increasing data for GPT, the answers started becoming redundant and not accurate. The project also dealt with AWS services, so I switched to Amazon Q, which provided more accurate and implementable answers and suggestions.

I evaluated just the one option, GPT-3.0, before choosing Amazon Q.

My advice to others looking into using Amazon Q is to go ahead, integrate it with your IDEs. I would suggest starting with VS Code and then see the wonders of Amazon Q. The free trial is available for all. On a scale of 1-10, I rate Amazon Q an 8.

I am using Amazon Q for the purpose of development of various applications such as MERN stack applications, as well as applications based on Java, Python, and related to machine learning and OpenCV applications. For that purpose of development, I'm using Amazon Q, as well as for deployment purposes on Docker images and Docker containers.

Amazon Q supports my AI-driven tasks, and I integrate AI within Amazon Q.

In my day-to-day tasks, AI integration means it is not directly supporting AI integration into that particular application. When working with Amazon Q, there are many models supported by Amazon itself. Amazon Q only helps to integrate those models into your application; it will not directly create an AI agent if we have the capability.

The most valuable features of Amazon Q include integrated services for development and deployment purposes, as well as documentation and testing. All these features are provided in one form. It's not necessary to give the full prompt; it will not only provide the prompt but simultaneously make changes in the real files. It's also easy to use and has extensive knowledge of languages.

Amazon Q supports multiple languages.

There is only one problem or issue we can discuss. Amazon Q does not have an option to integrate images in the prompt. For example, ChatGPT has an option to put an image and then ask questions about it. Additionally, Blackbox AI has a feature that if you put the image, it will create or generate the code as mentioned in the image. This is one of the features they should add.

I have been working with Amazon Q for almost a year now, since it was launched.

I participated in the initial setup and deployment of Amazon Q.

The setup of Amazon Q is very easy to implement. It doesn't have many complex steps to perform, as I'm using Amazon Q with VS Code. For that purpose, you just need one builder ID; if you are a subscription user, you need to buy a subscription. After that, there is a marketplace in VS Code, as well as Eclipse ID, and many more IDs are supported by Amazon Q. We just need to go to the marketplace, search for Amazon Q, then install that particular plugin. After that, it will show a prompt to log in. You can log in via a skill builder ID or if you want to buy a professional license, you can do that too. If we go through the skill builder ID, a pop-up will open on Chrome and you need to sign in.

The installation took me almost five minutes only.

If your VS Code is not up to date, then it will take much more time than usual.

I had the prerequisites handled.

Amazon Q is highly stable. It only has an issue of scalability when it is not accepting the traffic. The accuracy of that particular model provides high assurance that the result will be as the user wants it to be. The only requirement from the user end is the accuracy of the prompt; you need to ensure the prompt is correct. After that, the stability is good.

There was one issue that occurred two to three days ago. The model is not capable of handling the incoming traffic. The model is not able to give answers properly with the traffic it is facing, so it needs to be scaled more.

Higher scalability is important for me because the application is not able to handle all the traffic at the same time, as it has been giving an error for the last two to three days that the model is not available to give answers and is facing traffic. For that purpose, scalability is needed so it will be able to give answers properly to my questions, as well as solve problems. The problem has been occurring for about a week.

Before Amazon Q, I was using ChatGPT, but it was complex to do those tasks.

I stopped using ChatGPT because it was too complex. Amazon Q provides direct file access and it will integrate the things which you want into the files. With ChatGPT, we need to provide the prompt; after that, it will create the code for us, and then it is needed to integrate that code into that particular directory. Amazon Q simplifies it by reading all your directories and files in that directory and after that, working on top of that. It simplifies the work.

Pricing for Amazon Q is much higher than others. However, it is providing all the things under one platform, which makes it worthwhile.

Before choosing Amazon Q, I evaluated other options such as Cursor AI, which is similar to Amazon Q, but it only has the capabilities to generate code. Amazon Q provides documentation. There are four to five modes, including /dev mode, deployment mode, test mode, and documentation mode. Those features are provided by Amazon Q, not by Cursor AI. Cursor AI will support you only in the generation of code, while Amazon Q will deploy your application. That is the significant difference.

Amazon Q is multifunctional and can adapt to different needs.

I don't have information about features that have been especially useful for ensuring data integrity.

I am using the subscription of one of my friends. Usually, many features are provided under the skill builder ID.

I haven't used any documentation, guides, or manuals for Amazon Q.

I rate Amazon Q a 9 out of 10.

I have completed a project where the company required testing R&D on Kubernetes. I tested it locally by installing MiniKube, Kubernetes, and all the containers. I configured the Kubernetes MCB server to Amazon Q. Once the configuration was done, I could manage all the Kubernetes clusters using Amazon Q prompt. When I want to check any pods in the Kubernetes cluster, I do not need to use commands. I can simply write a prompt such as 'Please show me the Kubernetes pods and namespace and all.' Amazon Q automatically provides all the required details.

Once the configuration is complete, users can manage all Kubernetes clusters using Amazon Q prompt. When checking pods in the Kubernetes cluster, there is no need to use commands. Users can simply write a prompt such as 'Please show me the Kubernetes pods and namespace and all.' Amazon Q automatically provides all the required details.

While using Amazon Q, I faced some challenges, such as navigating the interface initially.

I have been using Amazon Q for a while now.

In terms of stability, Amazon Q is stable and performs reliably without major issues.

My impressions on the scalability of Amazon Q are positive; it efficiently handles increased loads.

The initial setup and deployment of Amazon Q are easy to do, and I didn't encounter any difficulties during this process.

I find Amazon Q easy to use, and I believe anyone can use it without needing extensive technical knowledge.

I would definitely recommend Amazon Q to other people; it's a great tool. I find it quite satisfactory.

On a scale of 1-10, I rate Amazon Q an 8.

I have been working with Amazon Q for six months; I have an experience of 1.6 years overall, but with Amazon Q specifically, I have six months of experience. I basically work on building dashboards in QuickSight, and during that time, I'm using Amazon Q.

I have been working in several projects which involve building dashboards using Amazon QuickSight, so we have lots of datasets which need to be visualized under a single dashboard.

For example, I used a use case in which I used the ARIMA algorithm; I gave the prompt requesting that with this dataset, using the ARIMA algorithm, could it create me a simple script to transform the data? At that time it was very useful for me.

One thing which I find beneficial in Amazon Q is that even if I don't have much knowledge, such as when using Lambda or Glue in AWS, I only need to have theoretical knowledge of how to use these services and what steps need to be followed.

The main benefit in Amazon Q is that it is easy to ask questions and get results.

We can easily integrate with AWS services, whereas if I'm using ChatGPT or Copilot, I can't integrate it with my AWS services directly.

If we give the prompt correctly and specify what is necessary, we get the desired output or result, but if someone is giving input in simple English or not providing an elaborate definition or explanation of what they need, Amazon Q's results are significantly lower in quality.

I have not yet experienced the data management output.

The data speed is actually normal; it is not too fast or too slow. It takes significant time when using large amounts of datasets to be loaded and creating topics.

It could provide more accurate results because sometimes the numerical values get mismatched, so it should give more precise results.

It is actually lacking transparency with the data.

Regarding the scalability aspect, we can improve how large numbers of data can be fed into the system.

I have been working with Amazon Q for six months; I have an experience of 1.6 years overall, but with Amazon Q, I have six months of experience.

It was not much of a challenge for me; it was actually easy to work on it.

I've used the AWS support case system by raising questions for any queries.

Positive

Compared to Amazon Q developer, I find that I'm getting more accurate results in Cursor AI and ChatGPT.

I mostly work on projects in data engineering, but my role is aligned with being a developer. I am fully working on the data engineering side and I also work on development, both front end and back end.

The data speed is normal; it is not too fast or too slow. It takes significant time when using large amounts of datasets to be loaded and creating topics.

I have worked with Copilot to get programming assistance.

On a scale of 1-10, I would rate Amazon Q a 7.