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 #5 with an average rating of 8.4, while Google is ranked #1 with an average rating of 8.3. 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 8 Amazon Bedrock reviews

55 Views
37 Comparison Views

100% willing to recommend

Google Vertex AI

Read 10 Google Vertex AI reviews

850 Views
744 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: November 2024).

Buyer's Guide

Amazon Bedrock vs. Google Vertex AI

November 2024

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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

5th

Average Rating

8.4

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

Infrastructure as a Service Clouds (IaaS) (14th)

Google Vertex AI

Ranking in AI Infrastructure

1st

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

AI Development Platforms (2nd)

Featured Reviews

Mehul Raj

Assistant Manager - Sales at Velocis Systems

Customization and no-code capabilities empower image generation and chatbot solutions

I work with an AWS partner, and we offer cloud managed services to our clients as well as reselling services. I've worked with Amazon Bedrock to create solutions, including an image generation solution and a chatbot for an ERP application for schools The ability to make changes in the…

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Serge Dahdouh

CEO at Les Roches International School of Hotel Management

A user-friendly platform that automatizes machine learning techniques with minimal effort

We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.

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

"The most beneficial aspect of Bedrock is its pool of models to choose from, catering to specific needs."

"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."

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

"Bedrock offers various foundational models in one place."

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

"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

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

"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 feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

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

"It provides the most valuable external analytics."

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

"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

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

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

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

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

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

"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."

"The user interface of Amazon Bedrock on the management console needs improvements. It's very bland at the moment."

More Amazon Bedrock cons

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

"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."

"I think the technical documentation is not readily available in the tool."

"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 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."

"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."

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

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 solution's pricing is moderate."

"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."

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

13%

Computer Software Company

13%

Manufacturing Company

Retailer

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 price of Amazon Bedrock is a little costly, rated six or seven out of ten. One customer paid around $100 to $200 per month, which was significant given their overall infrastructure costs.

See all answers

What needs improvement with Amazon Bedrock?

The user interface of Amazon Bedrock on the management console needs improvements. It's very bland at the moment.

See all answers

What is your primary use case for Amazon Bedrock?

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

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Compared 21% of the time

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Compared 15% of the time

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Compared 40% of the time

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Compared 20% of the time

Hugging Face vs Google Vertex AI

Compared 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 8% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Amazon Bedrock

January 2025

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Buyer's Guide

Google Vertex AI

January 2025

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Amazon Web Services (AWS)

Google

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.

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

Buyer's Guide

Amazon Bedrock vs. Google Vertex AI

November 2024

Free Report: Amazon Bedrock vs. Google Vertex AI

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