Amazon Q Reviews

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 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.

I have been working with Amazon Q for three years. In Amazon Q, I work with what we call the client's CMS, Client Management System, whereby we input information from clients and process this information to have tracking dates of when items were created and their types. I work in the financial world dealing with hedge funds. We process new accounts, purchase subscription items, and redemption items. We obtain particular information concerning how it is being processed from the information we receive. It entails information that deals with trade types and the amount which investors would invest. It deals with account names or account numbers where we input this information online so that in the future someone can easily track this information using the tracking source key number and get the particular information needed.

I can tell you about a situation where we had new investors using Amazon Q. We were able to create new accounts for each of them with items appended and uploaded into their various systems. With this, we had tasks where we could monitor each investor with each document appended on the queue for easy reference rather than going back to our share folder or SharePoint to get various documents individually. We were able to go individually to various documents and check for each investor. The action we get is that we process these investors promptly because of the appended documents. It is easier to trace the amount, date, and type of investor investing in the company. We ensure that we get the right document for the right investor. The result is we avoid escalations and issues where different investors are processed incorrectly. We can analyze and pinpoint which document belongs to each investor and process the trade accordingly, reducing stress by approximately 99%.

Prio in Amazon Q is an application where we process client information. We go into this particular section to process and create new accounts. We input client information including contact information, email address, phone number, and residential address. We also process trades based on what investors want, whether subscription documents or redemptions. It helps us monitor the amount of trades booked and pending at month-end. It also helps generate statements to track how much investors have invested or remain with the company. The platform helps us track various funds in which different investors have invested.

What I appreciate about Amazon Q is the prompt information received when using the source key. It is easier to navigate through and track the amount of trades booked in a month or particular period. If an item is not completed, one can always return to complete it. Whether it has been moved to the QC side for second checking or remains in the processing queue, users can analyze it, make corrections, and fix it before sending to the final archive position.

The annotation feature in Amazon Q is particularly valuable. The annotation section deals with inputting information of what has been processed and helps checkers understand what processors have done. With the annotation part, users can trace and check if particular trades or information have been processed correctly. If there are missing items or documents, the annotation part reminds second checkers that specific information is missing, allowing processors to request particular documents before appending them to the queue. The ability to track and trace data needed for future reference ensures that information can be retrieved quickly.

What I would suggest for improvement in Amazon Q relates to feedback handling once processes are concluded. While the automation and information input capabilities are excellent, there should be better mechanisms for making changes after archival. Currently, if an item has been archived and requires changes for audit purposes, retrieving it requires creating a new item and linking it to the previous one. If archived processes could be retrieved with manager approval, it would help make corrections without creating multiple source keys. This would reduce queue workload and make processing time faster and more efficient.

Three years.

There were no other options. I chose this solution because it helps complete tasks efficiently.

I worked with the IT team to set up Amazon Q via MS Teams calls with screen sharing. We input the information together, and I received access about a week before starting use. The setup was straightforward, requiring only learning the features and understanding how to process items.

It was provided by the company.

Amazon Q has been very helpful in improving efficiency. When I started with the company, processing an item would take 30-40 minutes. Now, within 10 minutes, tasks are completed. It has made the job easier and faster.

For those considering Amazon Q, I recommend always learning and upgrading with it. It makes the job easier and faster, helps monitor queues, and provides reference points for future processes. It is really worth implementing and can help motivate employees. This solution deserves a rating of 9 out of 10.

I work with my company's technology team, who handle interactions with the Amazon team. When information, upgrades, or queries are needed, I communicate through my company's tech team to get tasks completed.

Regarding pricing and additional features, I am not part of the pricing team and cannot provide specific details, but research into these aspects could be beneficial for potential users.

I have been using Amazon Q with Bedrock. Force Group engineering, the last company I was at, was trialing it, but it turned out to be too expensive in the end. They were going to use it with their Connect call center, but it was too expensive for what it did. This was because they needed to have the developer version, which was the most expensive option. I cannot remember the actual cost, but it was quite a lot of money when added up per user.

I do migrations and architecting and engineering. I design the migration and then implement it myself. I migrate all the client services from their in-house data center onto the AWS cloud, stage by stage.

The best feature of Amazon Q is the voice chat, where it talks back to you in normal language and you can query it. You can ask it a question and it will give you a normal answer.

Amazon Q is pretty stable since it is in general availability now. I used part of it when it was in preview, and it was somewhat unstable then. You can use it with Quicksight, which is quite a good combination. You can add Lambda functions that talk to Bedrock and Amazon Q and ask Amazon Q to do a task. It can summarize meeting minutes and create executive summaries.

Sometimes Amazon Q takes a while to get the data from the silo, from S3 or wherever it is stored, or from DynamoDB database. That could be improved by making it faster. This is one area that lets it down a little bit, as it takes a while to get the answer sometimes to the client's questions.

I have been using Amazon Q for about six months.

The initial setup and deployment of Amazon Q is not complex. I learned it and taught myself.

My impressions of the stability of Amazon Q are positive as it is quite stable now that it is in general availability. I used part of it when it was in preview, and it was somewhat unstable then.

Amazon's support team is impressive because they have 70% of the market. Microsoft Azure has about 21% and Google has about 11%, which speaks for itself. It is also the most secure network with approximately 70 security features, and there are additional features available for purchase from the AWS marketplace.

Positive

The initial setup and deployment of Amazon Q is not complex. I learned it and taught myself.

Amazon Q is good to have experience with, and I could teach it to others. I have been teaching AWS for about a year and a half through the AWS Restart course, which is a 12-week program. I am an accredited instructor for this course, and teaching someone Amazon Q and Bedrock would not be difficult.

My advice for others regarding Amazon Q would be to explore all the features, including voice and chat capabilities, executive summaries, meeting minutes, creating bullet points from meetings, and voice and chat activations.

I would definitely recommend this product to other people. On a scale of one to ten, I would give Amazon Q a rating of nine.

I use it for personal tasks and work. For personal projects, when I'm working on a weekend hobby, I can use it to create APIs, which helps to bootstrap. If I have a data set which I want to analyze, I give it some prompts, and it's able to analyze.

Regarding work, I'm able to use it to correct my code. In regards to security compliance, I ensure I follow the best practices.

Most of the time I use it for technical tasks in regards to programming. It's able to actually understand human language regarding the context. For instance, I have an existing folder. I can give it a prompt for it to scan through and generate whether it's following the best practices, or if the code is not following the formatting and linting requirements. Additionally, if there's data I want to analyze, it's able to understand the natural language from the human perspective.

I use Amazon for cloud service operations, which is why I opt for Amazon Q.

Amazon Q is a valuable tool to boost productivity and helps deliver quality and value to customers.

Amazon Q is able to understand human language regarding the context and scan through folders to ensure best practices are followed. This ability to interpret natural language from a human perspective is quite valuable. Amazon Q helps boost productivity, enabling the delivery of quality and value to customers. Its capabilities in correcting code and analyzing data are commendable.

I currently use the free tier, and in all my use cases, it's been able to deliver. Even though machine learning models have errors, regarding its benchmark, it's able to perform well. The paid version would likely do more compared to the free version.

Sometimes feedback is needed immediately. It takes a bit of time because there is a workload. You'll get your feedback and a listening ear, but it can improve in this aspect.

I've been using it for over a year now.

The service is very stable. The only caveat is that it depends on internet connectivity. If you have a stable internet connection, you'll be able to interact with the service.

In regards to scalability, it's good because other people are hitting the service to perform their operations. The only issue would be having a stable internet connection to be able to use the tool or service to its maximum potential.

The support team is really on point. Anytime you have an issue, you reach out to them, and they are willing to understand the issue you're facing. They'll get back to you on time.

Positive

It's very easy. AWS comes with the documentation. You just go to the website, follow the documentation, and you are able to install depending on your base environment, whether you're using Linux, Windows, or macOS, depending on the operating system.

I will recommend this solution. With the pricing, it's acceptable based on what you want to achieve or your priorities at a particular time. They have options for businesses. Depending on your workload or what you intend to achieve, you can talk to the customer service team of AWS and they'll help you out. On a scale of 1-10, I rate Amazon Q a 9.

I used Amazon Q in my hackathon, and after that, I have not used it. For automating coding tasks and implementing AWS services in my project, I used Amazon Q Developer. I did not use Amazon Q Developer for my organization but rather for one of my hackathon projects.

Amazon Q saved my time more than other products, such as GitHub Copilot, because it is conscious about particular AWS services. If I encounter any error, it provides a perfect solution that I can follow to resolve the issue.

The best feature of Amazon Q is that it has the data of all AWS services which it processes using RAG. By performing RAG on the particular data, it is specialized for AWS services. If you want to integrate AWS services in your project, then Amazon Q Developer is the best product available.

One of the best features of Amazon Q is the inline coding, which I appreciate.

Amazon Q saved a considerable amount of time.

Amazon Q Developer is only personalized for AWS, but I hope it can be personalized for other areas, such as Google Cloud service or Azure Cloud service. Amazon Q Developer provides only one LLM model, which is Amazon's LLM. However, GitHub Copilot provides a list of LLMs, allowing users to choose their preferred LLM, which is not possible in Amazon Q Developer.

In coding terms, sometimes I do not trust Amazon Q Developer's coding suggestions.

I used Amazon Q in my hackathon, and after that, I have not used it.

We completed the project within one month. If we had not used Amazon Q Developer, it would have taken three or four months. On a scale of 1-10, I rate Amazon Q an 8.