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reviewer2592177 - PeerSpot reviewer
AWS GenAI Data Engineer at a computer software company with 51-200 employees
Real User
Top 10
Plays a vital role in building a strong foundation for data pipelines by adding reasoning capabilities
Pros and Cons
  • "Bedrock offers various foundational models in one place."
  • "I would appreciate a greater focus on agentic Gen AI applications in Bedrock."

What is our primary use case?

Amazon Bedrock is used as a bridge between an application and a foundational Gen AI model. It enables me to use publicly available models from companies like Anthropic or Meta through Amazon or AWS. These models are hosted or borrowed by Amazon through APIs, providing a centralized place to utilize different foundational models. 

Bedrock allows comparison of these models for assessing performance and effectiveness for specific use cases. In my projects, Bedrock is used in multiple stages, including data pipeline processes like data cleaning or formatting, sentiment analysis, and creating chatbots for end users. The main strength of Gen AI, which Bedrock leverages, is reasoning, significantly aiding data pipelines.

How has it helped my organization?

Bedrock plays a vital role in building a strong foundation for data pipelines by adding reasoning capabilities, often missing from backend workflows. It offers productivity enhancements by providing a playground to compare models and experiment with Gen AI applications. It also aids in clean data preparation and sentiment analysis, leading to better internal workings of applications.

What is most valuable?

First, Bedrock offers various foundational models in one place. Second, it provides customization options for these models through techniques like fine-tuning, retrieval augmented generation, and continual pre-training, which are innovative features not seen in other managed platforms. Experimentation with Gen AI using Bedrock is notably user-friendly.

What needs improvement?

I would appreciate a greater focus on agentic Gen AI applications in Bedrock. While Bedrock includes agents in its toolkit, the feature lacks complexity compared to open-source frameworks. 

Additionally, the user interface for the playground could be more refined. While Bedrock is powerful, it lacks markdown formatting features seen in interfaces like ChatGPT.

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For how long have I used the solution?

I have been using the Bedrock solution for approximately six months.

What do I think about the stability of the solution?

Bedrock manages scalability and reliability effectively as a serverless solution, ensuring AWS handles scalability and security.

What do I think about the scalability of the solution?

Bedrock automatically scales, with AWS handling scalability concerns. Users are responsible for data security at the application level, but AWS provides features to support this.

How are customer service and support?

The AWS technical support was quick to respond, even under a basic support plan, and deserves an eight rating.

How would you rate customer service and support?

Positive

Which solution did I use previously and why did I switch?

I have worked with LangChain and its agentic framework, LandGraph. However, these do not provide managed services like Bedrock.

How was the initial setup?

Initially, navigating the AWS console for Bedrock was somewhat confusing with multiple clicks needed to reach the required features. However, after regular use, it has become part of my routine and is no longer an issue.

What was our ROI?

Bedrock offers a very high return on investment due to its cost-efficiency. It uses a pay-for-what-you-use model, allowing experimentation with foundation models at a low cost.

What's my experience with pricing, setup cost, and licensing?

Using Bedrock is inexpensive for experimenting with foundation models compared to managed services from other companies hosting these models.

Which other solutions did I evaluate?

LangChain and LandGraph, although not providing managed services like Bedrock.

What other advice do I have?

I recommend Bedrock to anyone entering the Gen AI field or considering experimenting with Gen AI. Its cost-effectiveness makes it ideal for experimentation.

I'd rate the solution nine out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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Architect at IGT Solutions
Real User
Top 5
Flexible and comprehensive solution enhances AI integration
Pros and Cons
  • "The valuable feature of Bedrock is its flexibility and comprehensiveness in what it's offering, providing parameters that we can change."
  • "One area for improvement is in cost—it tends to be a bit on the higher side, especially for enterprise versions."

What is our primary use case?

We use Bedrock primarily for its LLM (Large Language Model) capabilities. It serves our needs related to artificial intelligence solutions, including capabilities related to LLM.

How has it helped my organization?

Bedrock has been stable and works seamlessly as part of AWS services. Collaborating with it means we can easily integrate solutions however needed, and it's useful as we understand AWS's standard way of operating.

What is most valuable?

The valuable feature of Bedrock is its flexibility and comprehensiveness in what it's offering, providing parameters that we can change. This differs from Kendra, which doesn't allow parameter adjustments.

What needs improvement?

One area for improvement is in cost—it tends to be a bit on the higher side, especially for enterprise versions. Furthermore, it lacks certain AI capabilities, such as supporting voice and images.

For how long have I used the solution?

We have been using Bedrock for two to three months.

What do I think about the stability of the solution?

Over the past three months, the solution has been absolutely stable with no issues.

What do I think about the scalability of the solution?

Bedrock is scalable. However, there are inherent limitations such as rate per limits and token limits which are standard for LLMs, and we are aware of them.

How are customer service and support?

Technical support from Amazon is satisfying, though Text and Kendra could use improvements. Both are like black boxes with limitations in parameters that could be controlled.

How would you rate customer service and support?

Positive

How was the initial setup?

The initial setup of Bedrock is straightforward, and no significant issues were encountered during deployment.

What about the implementation team?

I can manage the setup independently and do not require an extensive team for installation.

What's my experience with pricing, setup cost, and licensing?

The licensing and overall pricing of Bedrock are competitive compared to other providers like Azure. However, LLM solutions can be expensive when opting for enterprise versions.

What other advice do I have?

The integration is seamless, and given the LLM limitations are known, it allows us to plan and manage accordingly. Awareness of cost implications might be necessary.

I'd rate the solution ten out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: My company has a business relationship with this vendor other than being a customer: Integrator
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Amazon Bedrock
April 2025
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Senior Software Developer at a tech vendor with 10,001+ employees
Real User
Top 5
Efficiently handles generative AI tasks but requires improvement in prompt engineering
Pros and Cons
  • "Amazon Bedrock offers an environment where we only pay for the model we use, and AWS handles the scaling."
  • "Overall, I rate Amazon Bedrock a seven out of ten. It is slightly difficult to integrate with our product."

What is our primary use case?

We are using Amazon Bedrock for generative AI-related tasks. We utilize Anthropic Claude LLM to obtain appropriate answers for user questions.

What is most valuable?

Amazon Bedrock offers an environment where we only pay for the model we use, and AWS handles the scaling. There is no need to maintain any environment, scaling, or DevOps-related tasks. Currently, we use Amazon Bedrock's LLM model to convert questions into answers by providing some context. It provides us with appropriate answers based on that context, which makes data processing tasks efficient.

What needs improvement?

AWS could add prompt engineering methods to its services. Currently, there are no prompt methods, so we have to experiment on our own. If AWS provided methods, like five or six prompts that yield specific results, it would ease development.

For how long have I used the solution?

We have been using it for approximately 1.5 to 1.7 years.

What do I think about the stability of the solution?

I have not experienced any stability issues. Amazon Bedrock is highly scalable with AWS Lambda, AWS Transcribe, and Polly.

What do I think about the scalability of the solution?

I have not faced any scalability issues. It scales well with AWS Lambda, AWS Transcribe, and Polly.

Which solution did I use previously and why did I switch?

We have not faced the scenario of migrating from a different warehouse. We created a project from scratch on Amazon Bedrock.

How was the initial setup?

After retrieving text from Transcribe, we pass it to Amazon Bedrock using a Lambda. Amazon Bedrock summarizes the text messages and provides appropriate answers, returning questions like the purpose of the meeting or the issues users face.

What's my experience with pricing, setup cost, and licensing?

The pricing depends on the LLM model in use, such as the 1.2 Anthropic Claude. Costs are based on the number of characters obtained in return. It follows a pay-as-you-go model, with different pricing for context and versions like 1.2, 2.3, and 3.1.

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Updated: April 2025
Buyer's Guide
Download our free Amazon Bedrock Report and get advice and tips from experienced pros sharing their opinions.