One most important improvement would be having deeper domain-specific intelligence for Amazon Q. While it can pull enterprise data sources, adding more domain-tuned models, for example in pharma, would be beneficial so the answers are not that generic. I believe it can be more explainable and proactive. When it comes to the data itself, we can have stronger multi-data reasoning. Additionally, deeper developer workflow automation could improve the user experience.
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.
Innovation Strategist at a insurance company with 5,001-10,000 employees
Real User
Top 20
2025-08-07T14:41:06Z
Aug 7, 2025
When updates occur in Anthropic models after implementing guard rails or instruction-based flows, we must revisit everything from scratch due to hallucination issues or unexpected outputs. Additionally, Amazon Q sometimes fails to follow specific output format instructions, instead providing summarized information in a different format. The multimodal capabilities of Amazon Q are limited, lacking the ability to process or generate images. When attempting to generate enterprise-level reports requiring both text and images, it struggles to combine elements in the specified format despite clear instructions. The integration costs are considerable, making the pricing structure less feasible for scalable solutions. After successful POCs, scaling at an enterprise level becomes cost-prohibitive. Developers have reported that Amazon Q plugins in IDEs consume significant CPU resources, necessitating high-end hardware that may not be available to all employees. Data analytics and querying capabilities show limitations, particularly when handling Excel files and performing operations requiring accurate, non-hallucinated output. Advanced code generation features are lacking, including the inability to generate comprehensive unit tests or handle complex refactoring across multiple files. The platform doesn't automatically identify security or performance bugs in code. Data cleaning and ETL tools are insufficient for enterprise-level analytics or visualization. Amazon Q struggles with parsing tables and understanding complex documents containing combinations of charts, numbers, and text. There is also no session awareness, meaning context is lost when sessions are closed and reopened. Downtime has been minimal, with one notable instance last year when the LLMs were not functioning. The main ongoing challenge is managing updates from AWS or Anthropic, as there is no proactive solution to assess impact on production deployments before implementation.
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.
Site reliability engineer at a tech services company with 201-500 employees
Real User
Top 20
2025-07-16T17:54:10Z
Jul 16, 2025
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.
Amazon Q is an innovative tool designed to streamline business processes and enhance data management efficiency, empowering enterprises to make informed decisions with ease.It offers a comprehensive range of functionalities tailored to meet complex business requirements. Leveraging advanced algorithms, Amazon Q facilitates seamless data analysis and integration into existing infrastructures, driving productivity and performance optimization across industries. Its user-centric design ensures...
One most important improvement would be having deeper domain-specific intelligence for Amazon Q. While it can pull enterprise data sources, adding more domain-tuned models, for example in pharma, would be beneficial so the answers are not that generic. I believe it can be more explainable and proactive. When it comes to the data itself, we can have stronger multi-data reasoning. Additionally, deeper developer workflow automation could improve the user experience.
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.
When updates occur in Anthropic models after implementing guard rails or instruction-based flows, we must revisit everything from scratch due to hallucination issues or unexpected outputs. Additionally, Amazon Q sometimes fails to follow specific output format instructions, instead providing summarized information in a different format. The multimodal capabilities of Amazon Q are limited, lacking the ability to process or generate images. When attempting to generate enterprise-level reports requiring both text and images, it struggles to combine elements in the specified format despite clear instructions. The integration costs are considerable, making the pricing structure less feasible for scalable solutions. After successful POCs, scaling at an enterprise level becomes cost-prohibitive. Developers have reported that Amazon Q plugins in IDEs consume significant CPU resources, necessitating high-end hardware that may not be available to all employees. Data analytics and querying capabilities show limitations, particularly when handling Excel files and performing operations requiring accurate, non-hallucinated output. Advanced code generation features are lacking, including the inability to generate comprehensive unit tests or handle complex refactoring across multiple files. The platform doesn't automatically identify security or performance bugs in code. Data cleaning and ETL tools are insufficient for enterprise-level analytics or visualization. Amazon Q struggles with parsing tables and understanding complex documents containing combinations of charts, numbers, and text. There is also no session awareness, meaning context is lost when sessions are closed and reopened. Downtime has been minimal, with one notable instance last year when the LLMs were not functioning. The main ongoing challenge is managing updates from AWS or Anthropic, as there is no proactive solution to assess impact on production deployments before implementation.
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.
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.