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 #9 in ISI , with an average rating of 8.1, and holds a 2.1% mindshare in the category. Google is ranked #1 in AIDP , with an average rating of 8.1, and holds a 8.4% 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

2,118 Views
1,228 Comparison Views

94% willing to recommend

Google Vertex AI

Read 15 Google Vertex AI reviews

6,949 Views
6,115 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 884,873 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) (9th), AI Infrastructure (1st)

Google Vertex AI

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

AI Development Platforms (1st), AI-Agent Builders (4th)

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 2.1%, up 0.5% compared to last year.
Google Vertex AI, on the other hand, focuses on AI Development Platforms, holds 8.4% mindshare, down 14.9% since last year.

Infrastructure as a Service Clouds (IaaS) Mindshare Distribution
Product	Mindshare (%)
Amazon Bedrock	2.1%
Amazon AWS	17.4%
Akamai Connected Cloud (Linode)	10.2%
Other	70.3%

Infrastructure as a Service Clouds (IaaS)

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Google Vertex AI	8.4%
Hugging Face	6.9%
Azure OpenAI	6.5%
Other	78.2%

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

"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."

"Bedrock proved beneficial for generating SQL queries, which helped in delivering precise information from the database."

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

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

"The most valuable features of Amazon Bedrock include scalability, ease of access, having 20 to 25 plus pre-trained models, and scaling capabilities."

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

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

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

More Amazon Bedrock pros

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

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

"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."

"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."

"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."

"It provides the most valuable external analytics."

"The features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good."

More Google Vertex AI pros

Cons

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

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

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

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

"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."

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

"The end-to-end application setup integration was very difficult."

"It would be beneficial if Bedrock were optimized for hyperscale use to avoid needing a mixed approach with SageMaker."

More Amazon Bedrock cons

"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."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful."

"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."

"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."

"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

"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"

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

884,873 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Manufacturing Company

13%

Financial Services Firm

11%

Computer Software Company

Outsourcing Company

Computer Software Company

11%

Financial Services Firm

10%

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	4
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?

Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful. To ...

See all answers

What is your primary use case for Google Vertex AI?

Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.

See all answers

Comparisons

Oracle Cloud Infrastructure (OCI) vs Amazon Bedrock

Compared 19% of the time

Alibaba Cloud vs Amazon Bedrock

Compared 10% of the time

Modal Labs vs Amazon Bedrock

Compared 8% of the time

GroqCloud Platform vs Amazon Bedrock

Compared 7% of the time

NVIDIA DGX Cloud vs Amazon Bedrock

Compared 7% of the time

More Amazon Bedrock Competitors

Azure OpenAI vs Google Vertex AI

Compared 22% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 12% of the time

Azure AI Foundry vs Google Vertex AI

Compared 8% of the time

Hugging Face vs Google Vertex AI

Compared 7% of the time

Amazon SageMaker vs Google Vertex AI

Compared 5% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Amazon Bedrock

March 2026

Download Amazon Bedrock product report

Buyer's Guide

Google Vertex AI

March 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

884,873 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.