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

Download the report

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

Download the complete report

Helped 880,844 peers since 2012

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

"It was absolutely useful and we found that we are getting 90 to 95% plus success rate while extracting the data from unstructured documents."

"The no-code application of the service is beneficial since it allows creating solutions without extensive coding knowledge."

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

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

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

"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 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."

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

More Amazon Bedrock pros

"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."

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

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

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

"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."

"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."

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

More Google Vertex AI pros

Cons

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

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

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

"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."

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

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

"What could be improved for Amazon Bedrock to make it more mature is that AWS needs to consider bringing their platforms together, and not having different ML and AI platforms."

"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."

More Amazon Bedrock cons

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

"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."

"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."

"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."

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

"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."

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."

"The price structure is very clear"

"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."

"The solution's pricing is moderate."

See which vendors are best for you

Use our free recommendation engine to learn which Infrastructure as a Service Clouds (IaaS) solutions are best for your needs.

See recommendations

880,844 professionals have used our research since 2012.

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 10% 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

Find out what your peers are saying about Amazon Bedrock vs. Google Vertex AI and other solutions. Updated: September 2025.

DOWNLOAD NOW

880,844 professionals have used our research since 2012.

See our Amazon Bedrock vs. Google Vertex AI report.

We monitor all Infrastructure as a Service Clouds (IaaS) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.