Amazon Bedrock vs Google Vertex AI comparison

The compared Amazon Web Services (AWS) and Google solutions aren't in the same category. Amazon Web Services (AWS) is ranked #10 in ISI , with an average rating of 8.1, and holds a 1.8% mindshare in the category. Google is ranked #3 in AIDP , with an average rating of 8.1, and holds a 8.1% mindshare. Additionally, 94% 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 17 Amazon Bedrock reviews

1,458 Views
816 Comparison Views

94% willing to recommend

Google Vertex AI

Read 14 Google Vertex AI reviews

6,731 Views
5,991 Comparison Views

100% willing to recommend

Amazon Bedrock

Google Vertex AI

Comparison Buyer's Guide

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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 Infrastructure as a Service Clouds (IaaS) 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: September 2025).

Buyer's Guide

Amazon Bedrock vs. Google Vertex AI

September 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

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Infrastructure as a Service Clouds (IaaS) (10th), AI Infrastructure (1st)

Google Vertex AI

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI Development Platforms (3rd), AI-Agent Builders (6th)

Mindshare comparison

While both are Artificial Intelligence (AI) solutions, they serve different purposes. Amazon Bedrock is designed for Infrastructure as a Service Clouds (IaaS) and holds a mindshare of 1.8%, up 0.2% compared to last year.
Google Vertex AI, on the other hand, focuses on AI Development Platforms, holds 8.1% mindshare, down 17.0% since last year.

Infrastructure as a Service Clouds (IaaS) Market Share Distribution
Product	Market Share (%)
Amazon Bedrock	1.8%
Amazon AWS	17.2%
Microsoft Azure	10.8%
Other	70.2%

Infrastructure as a Service Clouds (IaaS)

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Google Vertex AI	8.1%
Hugging Face	7.9%
Azure OpenAI	6.5%
Other	77.5%

AI Development Platforms

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.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"Amazon Bedrock is very easy to configure and launch, and it takes care of the creation of Docker images with CodeBuild and putting the images into the ECR repository very simply and easily with the help of Agent Core and Amazon Bedrock Agent Core."

"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."

"The most valuable feature of Bedrock is its security and the model's ability to modify vector dimensions easily."

"Amazon Bedrock offers an environment where we only pay for the model we use, and AWS handles the scaling."

"Amazon Bedrock is an artificial intelligence engine that allows me to upload analyses of the quality of customer support calls."

"It is the best solution in this category and is rated a nine out of ten."

"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."

"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."

More Amazon Bedrock pros

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

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

"The support is perfect and fantastic."

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

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

"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."

"The integration of AutoML features streamlines our machine-learning workflows."

"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."

More Google Vertex AI pros

Cons

"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."

"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 Bedrock were optimized for hyperscale use to avoid needing a mixed approach with SageMaker."

"As I recall now, I found some limitations or some models are not present in Amazon Bedrock sometimes while they are present in other platforms."

"The initial setup of Amazon Bedrock is somewhat complex as it requires integration with two to three services."

"While working with Bedrock, I incurred charges that were not explicitly mentioned in the pricing documentation."

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

More Amazon Bedrock cons

"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."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"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."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

More Google Vertex AI cons

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."

"The price structure is very clear"

"The solution's pricing is moderate."

"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."

"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."

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Top Industries

By visitors reading reviews

Manufacturing Company

11%

Financial Services Firm

10%

Computer Software Company

10%

University

Computer Software Company

13%

Financial Services Firm

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	1
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

Questions from the Community

What is your experience regarding pricing and costs for Amazon Bedrock?

The price of invoking the model is considerably better compared to hosting the model with our local resources. This is an advantage for Amazon Bedrock.

See all answers

What needs improvement with Amazon Bedrock?

Currently, I do not have any negative points in mind about Amazon Bedrock because I think Amazon Bedrock and other services are good. We have to use OpenSearch as well. We have not implemented RAG ...

See all answers

What is your primary use case for Amazon Bedrock?

I am currently working on Amazon Bedrock Agent Core. We have created a data pipeline where we are using Amazon Bedrock Agent Core primarily for transformation. We use the agent for custom rules, tr...

See all answers

What is your experience regarding pricing and costs for Google Vertex AI?

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

See all answers

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

See all answers

What is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

See all answers

Comparisons

Oracle Cloud Infrastructure (OCI) vs Amazon Bedrock

Compared 24% of the time

GroqCloud Platform vs Amazon Bedrock

Compared 10% of the time

Alibaba Cloud vs Amazon Bedrock

Compared 9% of the time

NVIDIA DGX Cloud vs Amazon Bedrock

Compared 8% of the time

Microsoft Azure vs Amazon Bedrock

Compared 7% of the time

More Amazon Bedrock Competitors

Azure OpenAI vs Google Vertex AI

Compared 21% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 14% of the time

Hugging Face vs Google Vertex AI

Compared 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Amazon Bedrock

January 2026

Download Amazon Bedrock product report

Buyer's Guide

Google Vertex AI

January 2026

Download Google Vertex AI product report

Overview

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 efficiency. With its versatile model customization and ease of integration, Bedrock reduces the need for extensive infrastructure management. It supports businesses in deploying pre-trained models, performing generative AI tasks, and improving analytics through AI technologies such as chatbots, sentiment analysis, and data formatting.

What are the key features of Amazon Bedrock?

Security: Ensures robust data protection and confidentiality.
Versatile Model Customization: Allows tailoring of AI models to specific needs.
Integration with AWS Services: Enhances operational processes with seamless AWS integrations.
Managed Scaling: Offers automatic scaling without the need for extensive infrastructure management.
User-Friendliness: Simplifies AI development processes for users.

What benefits should users look for in the reviews?

Cost Efficiency: Reduces expenses while maximizing output.
Operational Efficiency: Streamlines AI processes and reduces workload.
Enhanced Security: Increases trust with strong data protection measures.
Ease of Use: Minimizes the complexity of AI implementation.

Amazon Bedrock is applied across industries for implementing AI-driven solutions like enhancing customer service with chatbots and improving data analysis with sentiment analysis tools. Businesses create knowledge bases, automate business processes, and utilize pre-trained models for tasks such as invoice processing and customer call analysis. Its integration with large language models assists in text and image generation, offering diverse AI capabilities adaptable to industry needs.

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

September 2025

Free Report: Amazon Bedrock vs. Google Vertex AI

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