Amazon Bedrock vs Gemini Enterprise Agent Platform comparison

Why PeerSpot?

The compared Amazon Web Services (AWS) and Google solutions aren't in the same category. Amazon Web Services (AWS) is ranked #5 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 8.0% 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

5,024 Views
3,467 Comparison Views

94% willing to recommend

Gemini Enterprise Agent Pla...

Read 15 Gemini Enterprise Agent Platform reviews

9,692 Views
8,044 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: September 2026).

Buyer's Guide

Infrastructure as a Service Clouds (IaaS)

September 2026

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Helped 912,517 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) (5th), 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 2.1%, up 1.6% compared to last year.
Gemini Enterprise Agent Platform, on the other hand, focuses on AI Development Platforms, holds 8.0% mindshare, down 10.7% since last year.

Infrastructure as a Service Clouds (IaaS) Mindshare Distribution
Product	Mindshare (%)
Amazon Bedrock	2.1%
Amazon AWS	13.3%
Microsoft Azure	8.4%
Other	76.2%

Infrastructure as a Service Clouds (IaaS)

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Gemini Enterprise Agent Platform	8.0%
Azure OpenAI	7.2%
Hugging Face	3.9%
Other	80.9%

AI Development Platforms

Featured Reviews

Amal Amami

Data Scientist Team Lead at Venarisecurity

Access to large language models has transformed how our team invokes APIs and creates reports

I may not be the right person to provide this insight or recommendation. I am not really following the progress in it. Some new features may have been implemented and I am not aware of it. I would have to go back to Amazon Bedrock for that information. Working on agentic tools and providing more capability on agentic AI is what was missing at the last time I explored Amazon Bedrock. 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. There are new models that are coming, and sometimes I cannot find them in Amazon Bedrock. The limitation of region sometimes also caused some problems. Some models are found in regions and not in others, and the customers or the clients do not want the model to be hosted in a region different than their own. These are some limitations that we encounter sometimes while working on a product for a specific customer.

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

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

"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."

"Bedrock offers various foundational models in one place."

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

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

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

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

"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

"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."

"The support is perfect and fantastic."

"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."

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

"It provides the most valuable external analytics."

"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 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."

More Gemini Enterprise Agent Platform pros

Cons

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

"However, their product is not as mature as Snowflake or Databricks."

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

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

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

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

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

More Amazon Bedrock cons

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

"Some of the tools should have more advanced settings available. Some are very locked into certain features and settings, and there is no customization."

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

"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 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."

"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."

"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."

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

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%

Comms Service Provider

Outsourcing Company

10%

Financial Services Firm

Manufacturing 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

Microsoft Azure vs Amazon Bedrock

Compared 11% of the time

Google Cloud vs Amazon Bedrock

Compared 10% of the time

Alibaba Cloud vs Amazon Bedrock

Compared 10% of the time

Oracle Cloud Infrastructure (OCI) vs Amazon Bedrock

Compared 10% of the time

Modal Labs vs Amazon Bedrock

Compared 6% of the time

More Amazon Bedrock Competitors

Azure OpenAI vs Gemini Enterprise Agent Platform

Compared 28% 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 6% 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

September 2026

Download Amazon Bedrock product report

Buyer's Guide

Gemini Enterprise Agent Platform

August 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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September 2026

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