Amazon Bedrock vs Google Vertex AI comparison

Amazon Web Services (AWS) and Google are both solutions in the AI Infrastructure category. Amazon Web Services (AWS) is ranked #2 with an average rating of 8.3, while Google is ranked #1 with an average rating of 8.4. Additionally, 100% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Amazon Bedrock

Read 13 Amazon Bedrock reviews

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154 Comparison Views

100% willing to recommend

Google Vertex AI

Read 11 Google Vertex AI reviews

879 Views
688 Comparison Views

100% willing to recommend

Amazon Bedrock

Google Vertex AI

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Executive Summary

We performed a comparison between Amazon Bedrock and Google Vertex AI based on real PeerSpot user reviews.

Find out in this report how the two AI Infrastructure solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon Bedrock vs. Google Vertex AI Report (Updated: June 2025).

Buyer's Guide

Amazon Bedrock vs. Google Vertex AI

June 2025

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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

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

Ranking in other categories

AI Development Platforms (2nd)

Featured Reviews

RodrigoBassani

Diretor at Hat Thinking

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.

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Hamada Farag

Technology Consultant at Beta Information Technology

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.

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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."

"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."

"Data encryption while in transit and at rest is managed through Bedrock account."

"The valuable feature of Bedrock is its flexibility and comprehensiveness in what it's offering, providing parameters that we can change."

"Amazon Bedrock is easy to use and practical, allowing for quick development."

"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."

"Overall, I rate Amazon Bedrock ten out of ten."

More Amazon Bedrock pros

"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."

"The support is perfect and fantastic."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"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."

"Vertex comes with inbuilt integration with GCP for data storage."

More Google Vertex AI pros

Cons

"The user interface of Amazon Bedrock on the management console needs improvements."

"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."

"It would be beneficial if Amazon Bedrock could provide multiple responses to a query, allowing users to choose the best option."

"There is a need for improved documentation, smoother integration, and possibly reduced prices given the competition."

"One area for improvement is in cost—it tends to be a bit on the higher side, especially for enterprise versions."

"Amazon Bedrock is quite highly scalable, but there are some limitations they impose on the accounts, which could be an area for improvement."

"I would appreciate a greater focus on agentic Gen AI applications in Bedrock."

"Bedrock could be improved by having an API that allows for easy integration with services outside of Bedrock."

More Amazon Bedrock cons

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"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'm not sure if I have suggestions for improvement."

"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."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"The tool's documentation is not good. It is hard."

"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."

More Google Vertex AI cons

Pricing and Cost Advice

"One customer paid around $100 to $200 per month, which was significant given their overall infrastructure costs."

"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."

"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 price structure is very clear"

"The solution's pricing is moderate."

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Top Industries

By visitors reading reviews

Manufacturing Company

14%

Computer Software Company

12%

Financial Services Firm

11%

University

Computer Software Company

13%

Financial Services Firm

12%

Manufacturing Company

Educational Organization

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...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

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.

See all answers

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.

See all answers

Comparisons

Oracle Cloud Infrastructure (OCI) vs Amazon Bedrock

Compared 27% of the time

GroqCloud Platform vs Amazon Bedrock

Compared 14% of the time

NVIDIA DGX Cloud vs Amazon Bedrock

Compared 10% of the time

Alibaba Cloud vs Amazon Bedrock

Compared 9% of the time

Microsoft Azure vs Amazon Bedrock

Compared 7% of the time

More Amazon Bedrock Competitors

Azure OpenAI vs Google Vertex AI

Compared 35% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 16% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 8% of the time

Replicate vs Google Vertex AI

Compared 3% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Amazon Bedrock

June 2025

Download Amazon Bedrock product report

Buyer's Guide

Google Vertex AI

June 2025

Download Google Vertex AI product report

Overview

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 and analytics enhancement, there is a desire for improved documentation, smoother integration, and more competitive pricing. Additional integration points, markdown features, and support for voice and images could enhance its use. Users also seek to optimize for hyperscale use and receive multiple responses for creative tasks.

What are the key features of Amazon Bedrock?

Security: Provides robust measures to ensure data protection.
Model Flexibility: Allows customization of foundational models.
Serverless Architecture: Offers cost-efficient, scalable resources.
Provider Access: Connects with models from different vendors.

What benefits should be considered?

Model Modification: Enables quick adjustments to suit needs.
Development Speed: Facilitates fast application development.
AI Integration: Integrates seamlessly into existing offerings.

In industries like data analytics and software development, Amazon Bedrock is implemented for tasks such as deploying large language models, performing sentiment analysis, and creating chatbots. It's used for generating AI-driven text and images, and enhancing data retrieval via SQL query generation.

Amazon Web Services (AWS)

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Buyer's Guide

Amazon Bedrock vs. Google Vertex AI

June 2025

Free Report: Amazon Bedrock vs. Google Vertex AI

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