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 20
2024-11-25T16:50:27Z
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.
Full-stack Developer - Node | Android | AWS | Java at a tech company with 11-50 employees
Real User
Top 5
2024-10-31T11:20:12Z
Oct 31, 2024
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 enhances AI integration by providing a suite of foundational models with customization options. It simplifies data integration and offers security, traceability, and cost-efficiency through its serverless architecture.
Amazon Bedrock empowers users by offering models from multiple providers, ensuring model flexibility and ease of use. It supports quick development for applications such as vector search and SQL query generation. While the system is beneficial for AI integration...
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.