Amazon Bedrock vs Gemini Enterprise Agent Platform comparison

The compared Amazon Web Services (AWS) and Google solutions aren't in the same category. Amazon Web Services (AWS) is ranked #7 in ISI , with an average rating of 8.1, and holds a 2.0% mindshare in the category. Google is ranked #1 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

3,933 Views
2,556 Comparison Views

94% willing to recommend

Gemini Enterprise Agent Pla...

Read 15 Gemini Enterprise Agent Platform reviews

8,392 Views
7,049 Comparison Views

100% willing to recommend

Amazon Bedrock

Gemini Enterprise Agent Pla...

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon Bedrock and Gemini Enterprise Agent Platform based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Amazon Web Services (AWS), Google and others in Infrastructure as a Service Clouds (IaaS).

To learn more, read our detailed Infrastructure as a Service Clouds (IaaS) Report (Updated: June 2026).

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Infrastructure as a Service Clouds (IaaS)

June 2026

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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) (7th), AI Infrastructure (1st)

Gemini Enterprise Agent Pla...

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

AI Development Platforms (1st), AI Agent Builders (5th)

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 2.0%, up 1.4% compared to last year.
Gemini Enterprise Agent Platform, on the other hand, focuses on AI Development Platforms, holds 8.1% mindshare, down 11.9% since last year.

Infrastructure as a Service Clouds (IaaS) Mindshare Distribution
Product	Mindshare (%)
Amazon Bedrock	2.0%
Amazon AWS	15.0%
Microsoft Azure	8.7%
Other	74.3%

Infrastructure as a Service Clouds (IaaS)

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Gemini Enterprise Agent Platform	8.1%
Azure OpenAI	7.1%
Hugging Face	4.7%
Other	80.1%

AI Development Platforms

Featured Reviews

Manoday Ahire

AWS Cloud Infrastructure Engineer at Reg-X Innovations

Customization and integration drive impactful business-specific solutions

The most valuable features of Amazon Bedrock include scalability, ease of access, having 20 to 25 plus pre-trained models, and scaling capabilities. It can adjust its scalability as needed. The valuable features that have helped in leveraging generative AI for operational efficiency improvements include customization capabilities, various types of models suitable for specific use cases, and the integration of knowledge bases. It has the capability to convert normal documents to vectorized documents, and can provide answers based on these documents.

Read full review

Pethuru Chelliah

Chief Architect at a energy/utilities company with 10,001+ employees

Developed and deployed AI agents through a unified platform that supports integration with enterprise systems

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 other optimizations to enhance the performance of the AI models. We are not fully leveraging the automation being provided by Google Vertex AI for building AI models. Google Vertex AI has to be enhanced to support agentic AI system development. AI agents have to be developed through Google Vertex AI. Additionally, RAG support, vector database support, knowledge graph support, integration with enterprise systems such as ERP, CRM, PLM, MES, and other enterprise systems need improvement. Automated workflow generation and automation could also be enhanced. At this point, we are completely satisfied with the features and functionalities of Google Vertex AI platform, but as we move towards autonomous systems through agentic AI paradigm, Google Vertex AI platform needs to be improved to facilitate agentic AI system design, development, deployment, monitoring, observability, governance, and security.

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

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

"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."

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

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

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

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

"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

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

"The support is perfect and fantastic."

"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 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."

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

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

More Gemini Enterprise Agent Platform pros

Cons

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

"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."

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

"Overall, I rate Amazon Bedrock a seven out of ten. It is slightly difficult to integrate with our product."

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

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

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

"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."

More Amazon Bedrock cons

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

"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."

"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'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 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."

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

"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."

More Gemini Enterprise Agent Platform 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."

"The solution's pricing is moderate."

"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

13%

Financial Services Firm

11%

Outsourcing Company

Computer Software Company

Manufacturing Company

Financial Services Firm

Outsourcing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	1
Large Enterprise	8

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 11% of the time

Alibaba Cloud vs Amazon Bedrock

Compared 10% of the time

Google Cloud vs Amazon Bedrock

Compared 10% of the time

Microsoft Azure vs Amazon Bedrock

Compared 9% of the time

Amazon AWS vs Amazon Bedrock

Compared 7% of the time

More Amazon Bedrock Competitors

Azure OpenAI vs Gemini Enterprise Agent Platform

Compared 26% of the time

Microsoft Foundry vs Gemini Enterprise Agent Platform

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Gemini Enterprise Agent Platform

Compared 7% of the time

Hugging Face vs Gemini Enterprise Agent Platform

Compared 4% of the time

Amazon SageMaker vs Gemini Enterprise Agent Platform

Compared 4% of the time

More Gemini Enterprise Agent Platform Competitors

Product Reports

Buyer's Guide

Amazon Bedrock

July 2026

Download Amazon Bedrock product report

Buyer's Guide

Gemini Enterprise Agent Platform

July 2026

Download Gemini Enterprise Agent Platform product report

Also Known As

No data available

Vertex, Google Vertex AI

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)

Gemini Enterprise Agent Platform offers advanced AI capabilities with large language and vision models, a seamless Google ecosystem integration, and low-code/no-code options, enhancing AI agent development, deployment, and monitoring.

Gemini Enterprise Agent Platform empowers businesses with its comprehensive AI development environment, focusing on generative and agentic AI applications. It streamlines tasks like chatbots, document summarization, and data workflows integration. Despite its complexity and high cost, it offers advantages in feature centralization, end-to-end integration, external analytics, and multi-model capabilities. Challenges include navigation issues, setup complexity, and documentation accessibility. Users wish for improved model efficiency, system integration, customer support, and a more user-friendly experience for non-technical users. The platform's potential in optimizing machine learning modules and managing fintech tasks makes it valuable for organizations seeking robust AI model management.

What are Gemini Enterprise Agent Platform's Key Features?

Large Language and Vision Models: Enhance AI capabilities across diverse applications.
Google Ecosystem Integration: Seamlessly integrates within Google's platforms.
Feature Store: Centralizes features for better team communication and model management.
Low-Code/No-Code Options: Facilitates AI development with minimal coding.
End-to-End Integration: Offers comprehensive tools for agent deployment and monitoring.

What Benefits and ROI Should Users Look For?

Feature Centralization: Streamlines communication and coordination across teams.
Integration Ease: Simple integration with existing systems enhances operational efficiency.
AI Optimization: Improves machine learning modules, reducing costs and boosting efficiency.
Advanced Language Analytics: Enables better data insights via keyword detection and document processing.

Gemini Enterprise Agent Platform finds application in industries focusing on generative AI and agentic tasks. Businesses use it for chatbots, document summarization, and fintech-related tasks, benefiting from its capabilities in handling large documents, keyword detection, and language analytics. Its ease of integration with current systems is a significant advantage for many organizations.

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June 2026

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