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DataRobot vs Gemini Enterprise Agent Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 23, 2026

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

DataRobot
Ranking in AI Development Platforms
11th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
6
Ranking in other categories
Predictive Analytics (5th), AIOps (14th), AI Observability (18th), AI Finance & Accounting (5th)
Gemini Enterprise Agent Pla...
Ranking in AI Development Platforms
1st
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI-Agent Builders (5th)
 

Mindshare comparison

As of April 2026, in the AI Development Platforms category, the mindshare of DataRobot is 2.0%, up from 1.4% compared to the previous year. The mindshare of Gemini Enterprise Agent Platform is 8.2%, down from 14.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Google Vertex AI8.2%
DataRobot2.0%
Other89.8%
AI Development Platforms
 

Featured Reviews

Naqash Ahmed - PeerSpot reviewer
Senior Data Reporting Analyst at University of Bradford
Automation has improved efficiency and decision-making while big data handling and transparency still need work
Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.
Hamada Farag - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model."
"DataRobot is highly automated, allowing data scientists to build models easily."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate."
"DataRobot can be easy to use."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"Tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours."
"The integration of AutoML features streamlines our machine-learning workflows."
"It provides the most valuable external analytics."
"Vertex comes with inbuilt integration with GCP for data storage."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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 most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"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."
 

Cons

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"There is a lack of transparency in the models; sometimes it feels like a black box."
"We dropped the plan to use DataRobot because we found the pricing to be on the higher side."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"There are some performance issues."
"DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality."
"All the others can use it, but not to the maximum."
"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."
"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 good in machine learning and AI, but it lacks optimization."
"I'm not sure if I have suggestions for improvement."
"I think the technical documentation is not readily available in the tool."
"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."
"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."
 

Pricing and Cost Advice

"The price of DataRobot is good because if you take the price of the solution which is approximately $65,000, it is less than a data scientist. There are very few data scientists available."
"We dropped the plan to use DataRobot, because we found the pricing to be on the higher sise. We liked DataRobot a lot, but due to the pricing, we dropped that idea."
"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."
"The price structure is very clear"
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Manufacturing Company
13%
Educational Organization
8%
Construction Company
7%
Financial Services Firm
10%
Manufacturing Company
10%
Computer Software Company
9%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise1
Large Enterprise5
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for DataRobot?
My experience with pricing, setup cost, and licensing reveals that the price points can be improved and DataRobot is not so cost-effective, especially for smaller organizations.
What needs improvement with DataRobot?
To improve DataRobot, I suggest enhancing model accuracy metrics and improving automation. The price points can also be improved. Another improvement that DataRobot needs is integrating the capabil...
What is your primary use case for DataRobot?
DataRobot serves as our data science platform for building machine learning models and the development environment for running models. We also use the best practice processes and governance that Da...
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.
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 ...
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.
 

Also Known As

No data available
Vertex, Google Vertex AI
 

Overview

 

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
Information Not Available
Find out what your peers are saying about DataRobot vs. Gemini Enterprise Agent Platform and other solutions. Updated: April 2026.
892,287 professionals have used our research since 2012.