For operational data engineering, this can definitely be applied. We are currently doing a POC, and it is not in production right now. When it comes to AWS training material, I find that compared to GCP, GCP has very good demo-style training material with videos from start to end and workshops. Their demos are like workshops with a sandbox, instructions, and video. AWS has material in readable format where I have to read the document. I would prefer a demo of thirty minutes or one hour where I can implement something end-to-end so that I get an idea to implement in my project as well. It may not be exactly the same, but it would cover the end-to-end part. I will recommend Amazon Bedrock to other users because of the Strand SDK. I am not sure what Google has with them, but it is easy. If I use LangGraph or any other framework, they change because they are open source, so if they bring a new version, we may not know about it. I think Strand SDK is good. It is like LangGraph only. I will recommend it, definitely. I would rate my overall experience with Amazon Bedrock as a seven out of ten.
Since that time, I have worked on agentic AI, using LangGraph and LangChain, mainly tools for agentic AI. I also used LangSmith. I have experimented with many tools and frameworks related to agentic AI, including Crew AI. There is a not very well-known framework for agentic AI which is Agemo. I found some papers or the website of this framework cited that they are very fast. This framework was agentic AI focused. I do not recall using some of these other tools. I am using Amazon Bedrock. I would rate this product an 8 out of 10.
For content creation, it is necessary to use another model for that purpose. Amazon Bedrock can be integrated with other services such as Amazon Lex. It can also be integrated with knowledge bases, and these integrations can be incorporated into code and deployed to the EC2 server or any serverless platform. The main benefits derived from Amazon Bedrock are its customization capabilities, model variety, and the ability to integrate into UI systems. On a scale of 1-10, I rate Amazon Bedrock an 8.
For data analysis, it depends on the client's decision. For this specific case, we are working only on this solution because the data analysis for this company's corporate solution is done with Power BI, not Amazon Bedrock. The impact of Amazon Bedrock's sophisticated natural language processing on our company's ability to predict future outcomes is very interesting because, before we were using some Python codes, we created server instances to upload it, and we had some difficulty integrating it with the ecosystem because all the features we were creating were manually based. If I want to create a specific agent to connect to the client architecture, I have to do it manually. Once we use Amazon Bedrock as a composed architecture, it is easier to not only connect and provide the infrastructure as a service, but you can easily deploy it in the production environment because if you are on your machine or laptop, you can run these Python codes to do what we are testing or trying to solve, but it is not scalable. The integration with other AWS services contributes to cost savings. For example, you can use a Lambda to create that specific solution; the solution we create to read the emails is from Lambda, and we connect through the Outlook component to get this message and send it in a JSON file to the SNS queue. I do not have to create any server or anything else related; I just create the Lambda service connected to the SNS services, and this integrating environment is all serverless. Regarding Amazon Bedrock's pricing, for this specific case with our client, before they had a machine that worked for about $500 per month, and once we evolved it to this new architecture, they paid around $2,000 for the same solution. If you compare it only with the Python code we were running before, it is three times the price, but once you have it on scale, you can share it with other solutions. Once you decide to use this in a corporate way that will scale with other areas and have a well-defined architecture for your company, you can share it, and it becomes a fair price to pay for this kind of right solution. I rate Amazon Bedrock an 8 out of 10.
Overall, I rate Amazon Bedrock a seven out of ten. It is slightly difficult to integrate with our product. A good knowledge of back-end development is necessary. If users have this, they can proceed. Otherwise, it may not be as user-friendly compared to other services.
Based on my experience with Amazon Bedrock, I would recommend this solution to other customers. I would rate Amazon Bedrock overall as an eight because it is quite a good solution.
AWS cloud AI & data scientist at a tech services company with 51-200 employees
Real User
Top 10
Nov 25, 2024
I recommend Amazon Bedrock due to its wide range of models and quality. Reading the documentation thoroughly can ease the setup process. On a scale of one to ten, I would rate Bedrock as an eight because some attributes were not very flexible.
Monitor your usage carefully with tools like Cost Explorer and Amazon CloudWatch to avoid unexpected charges. Understanding the pricing model thoroughly can prevent unforeseen expenses. I would advise new users to read the documentation fully to ensure they understand the service they are using. I rate Amazon Bedrock a nine out of ten overall.
I recommend Bedrock specifically if you are using other AWS products within your application, as it consolidates workflows and remains within the AWS ecosystem. If not, OpenAI might be a simpler choice. I'd rate the solution six out of ten.
Amazon Bedrock offers comprehensive model customization and integration with AWS services, making AI development more flexible for users. It streamlines content generation and model fine-tuning with a focus on security and cost efficiency.Amazon Bedrock is engineered to provide a seamless AI integration experience with a strong emphasis on security and user-friendliness. It simplifies AI development by offering foundational models and managed scaling, enhancing both trust and operational...
For operational data engineering, this can definitely be applied. We are currently doing a POC, and it is not in production right now. When it comes to AWS training material, I find that compared to GCP, GCP has very good demo-style training material with videos from start to end and workshops. Their demos are like workshops with a sandbox, instructions, and video. AWS has material in readable format where I have to read the document. I would prefer a demo of thirty minutes or one hour where I can implement something end-to-end so that I get an idea to implement in my project as well. It may not be exactly the same, but it would cover the end-to-end part. I will recommend Amazon Bedrock to other users because of the Strand SDK. I am not sure what Google has with them, but it is easy. If I use LangGraph or any other framework, they change because they are open source, so if they bring a new version, we may not know about it. I think Strand SDK is good. It is like LangGraph only. I will recommend it, definitely. I would rate my overall experience with Amazon Bedrock as a seven out of ten.
Since that time, I have worked on agentic AI, using LangGraph and LangChain, mainly tools for agentic AI. I also used LangSmith. I have experimented with many tools and frameworks related to agentic AI, including Crew AI. There is a not very well-known framework for agentic AI which is Agemo. I found some papers or the website of this framework cited that they are very fast. This framework was agentic AI focused. I do not recall using some of these other tools. I am using Amazon Bedrock. I would rate this product an 8 out of 10.
For content creation, it is necessary to use another model for that purpose. Amazon Bedrock can be integrated with other services such as Amazon Lex. It can also be integrated with knowledge bases, and these integrations can be incorporated into code and deployed to the EC2 server or any serverless platform. The main benefits derived from Amazon Bedrock are its customization capabilities, model variety, and the ability to integrate into UI systems. On a scale of 1-10, I rate Amazon Bedrock an 8.
For data analysis, it depends on the client's decision. For this specific case, we are working only on this solution because the data analysis for this company's corporate solution is done with Power BI, not Amazon Bedrock. The impact of Amazon Bedrock's sophisticated natural language processing on our company's ability to predict future outcomes is very interesting because, before we were using some Python codes, we created server instances to upload it, and we had some difficulty integrating it with the ecosystem because all the features we were creating were manually based. If I want to create a specific agent to connect to the client architecture, I have to do it manually. Once we use Amazon Bedrock as a composed architecture, it is easier to not only connect and provide the infrastructure as a service, but you can easily deploy it in the production environment because if you are on your machine or laptop, you can run these Python codes to do what we are testing or trying to solve, but it is not scalable. The integration with other AWS services contributes to cost savings. For example, you can use a Lambda to create that specific solution; the solution we create to read the emails is from Lambda, and we connect through the Outlook component to get this message and send it in a JSON file to the SNS queue. I do not have to create any server or anything else related; I just create the Lambda service connected to the SNS services, and this integrating environment is all serverless. Regarding Amazon Bedrock's pricing, for this specific case with our client, before they had a machine that worked for about $500 per month, and once we evolved it to this new architecture, they paid around $2,000 for the same solution. If you compare it only with the Python code we were running before, it is three times the price, but once you have it on scale, you can share it with other solutions. Once you decide to use this in a corporate way that will scale with other areas and have a well-defined architecture for your company, you can share it, and it becomes a fair price to pay for this kind of right solution. I rate Amazon Bedrock an 8 out of 10.
Overall, I rate Amazon Bedrock a seven out of ten. It is slightly difficult to integrate with our product. A good knowledge of back-end development is necessary. If users have this, they can proceed. Otherwise, it may not be as user-friendly compared to other services.
Based on my experience with Amazon Bedrock, I would recommend this solution to other customers. I would rate Amazon Bedrock overall as an eight because it is quite a good solution.
It is the best solution in this category and is rated a nine out of ten. There is always room for improvement, however, it is a world-class ecosystem.
You should be well-versed in AI ML to use Bedrock properly. Overall, I rate Amazon Bedrock ten out of ten.
I recommend Amazon Bedrock due to its wide range of models and quality. Reading the documentation thoroughly can ease the setup process. On a scale of one to ten, I would rate Bedrock as an eight because some attributes were not very flexible.
Monitor your usage carefully with tools like Cost Explorer and Amazon CloudWatch to avoid unexpected charges. Understanding the pricing model thoroughly can prevent unforeseen expenses. I would advise new users to read the documentation fully to ensure they understand the service they are using. I rate Amazon Bedrock a nine out of ten overall.
I recommend Bedrock specifically if you are using other AWS products within your application, as it consolidates workflows and remains within the AWS ecosystem. If not, OpenAI might be a simpler choice. I'd rate the solution six out of ten.