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Amazon Bedrock vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon Bedrock
Ranking in AI Infrastructure
2nd
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
13
Ranking in other categories
Infrastructure as a Service Clouds (IaaS) (12th)
Google Vertex AI
Ranking in AI Infrastructure
1st
Average Rating
8.4
Reviews Sentiment
7.1
Number of Reviews
11
Ranking in other categories
AI Development Platforms (2nd)
 

Featured Reviews

RodrigoBassani - PeerSpot reviewer
Advanced integration and flexible architecture drive efficient business solutions
I have to gain more maturity to provide some improvements to Amazon Bedrock. I have a lot to do with the environment they already provided. For example, they are able to connect to any LLM solution such as Llama, Meta, Gemini, or ChatGPT. It is open; you just choose your favorite LLM solution, and you can integrate it into Amazon Bedrock. We have a lot of possibilities to do this integration at this moment; we just need to work on it, create more maturity, and then we can provide some enhancements that we can see on the solution as a whole. For companies in general, the main pain point or main issue related to Amazon Bedrock is security because they are not confident that all information is hidden by this kind of architecture. They wonder if they are providing some company information that can run away, and I think that is the challenge we have now. We need to find ways to work on it and make our clients' data secure. They are looking for that to guarantee that this is a great solution for companies that is also secure.
Hamada Farag - PeerSpot reviewer
Customization and integration empower diverse AI applications
We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The integration with pre-trained AI models has been very beneficial, allowing me to quickly access powerful machine learning models without the need to build them from scratch."
"The no-code application of the service is beneficial since it allows creating solutions without extensive coding knowledge."
"Bedrock proved beneficial for generating SQL queries, which helped in delivering precise information from the database."
"Bedrock offers various foundational models in one place."
"Amazon Bedrock offers an environment where we only pay for the model we use, and AWS handles the scaling."
"It is the best solution in this category and is rated a nine out of ten."
"Amazon Bedrock is an artificial intelligence engine that allows me to upload analyses of the quality of customer support calls."
"One of the best features of Amazon Bedrock is that it is easy to use, and users do not have to worry about the infrastructure."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"It provides the most valuable external analytics."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"Vertex comes with inbuilt integration with GCP for data storage."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
 

Cons

"The advantage of Bedrock is not as an amazing enabler of AI platforms, yet we utilize it to deploy application services and microservices within Bedrock ecosystem and leverage prequalified foundation models like Claude and others."
"The user interface of Amazon Bedrock on the management console needs improvements. It's very bland at the moment."
"For companies in general, the main pain point or main issue related to Amazon Bedrock is security because they are not confident that all information is hidden by this kind of architecture."
"Overall, I rate Amazon Bedrock a seven out of ten. It is slightly difficult to integrate with our product."
"Amazon native models could proliferate Bedrock in the future. We would welcome Amazon native models to Bedrock since, if they are natively built by Amazon, they are tuned to SageMaker and other Amazon service layers."
"Bedrock could be improved by having an API that allows for easy integration with services outside of Bedrock."
"I would appreciate a greater focus on agentic Gen AI applications in Bedrock."
"For companies in general, the main pain point or main issue related to Amazon Bedrock is security because they are not confident that all information is hidden by this kind of architecture."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"I'm not sure if I have suggestions for improvement."
"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"I think the technical documentation is not readily available in the tool."
"The tool's documentation is not good. It is hard."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
 

Pricing and Cost Advice

"The cost of using Amazon Bedrock is quite high, as I incurred unexpected charges amounting to $130 USD within two weeks without actually deploying the model."
"One customer paid around $100 to $200 per month, which was significant given their overall infrastructure costs."
"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."
"The solution's pricing is moderate."
"The price structure is very clear"
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Top Industries

By visitors reading reviews
Manufacturing Company
14%
Computer Software Company
12%
Financial Services Firm
11%
University
9%
Computer Software Company
13%
Financial Services Firm
12%
Manufacturing Company
9%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What is your experience regarding pricing and costs for Amazon Bedrock?
The pricing depends on the LLM model in use, such as the 1.2 Anthropic Claude ( /products/claude-reviews ). Costs are based on the number of characters obtained in return. It follows a pay-as-you-g...
What needs improvement with Amazon Bedrock?
AWS ( /products/amazon-aws-reviews ) 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...
What is your primary use case for Amazon Bedrock?
We are using Amazon Bedrock ( /products/amazon-bedrock-reviews ) for generative AI-related tasks. We utilize Anthropic Claude ( /products/claude-reviews ) LLM to obtain appropriate answers for user...
What do you like most about Google Vertex AI?
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...
What is your experience regarding pricing and costs for Google Vertex AI?
They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.
What needs improvement with Google Vertex AI?
I'm not sure if I have suggestions for improvement. Based on my comparison between the two, Vertex has various additional functionalities that Azure doesn't provide.
 

Overview

Find out what your peers are saying about Amazon Bedrock vs. Google Vertex AI and other solutions. Updated: June 2025.
860,168 professionals have used our research since 2012.