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DataRobot vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 4, 2024
 

Categories and Ranking

DataRobot
Ranking in AI Development Platforms
12th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
4
Ranking in other categories
Predictive Analytics (5th), AIOps (17th)
Google Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
10
Ranking in other categories
AI Infrastructure (1st)
 

Mindshare comparison

As of December 2024, in the AI Development Platforms category, the mindshare of DataRobot is 1.7%, up from 1.0% compared to the previous year. The mindshare of Google Vertex AI is 23.7%, up from 16.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Raviteja Guna - PeerSpot reviewer
Highly automated solution allowing data scientists to build models easily
Based on your similar requirements, V10.0 has very cool features related to AI Generation. I would suggest the team, too. This is very good for data scientists or people who don't want to code. Even the documentation is well-maintained in terms of its capabilities. It's easy to navigate. The support documents are good. Even someone with basic IT knowledge can easily navigate. If you're an IT engineer, you can efficiently perform operations using it. We have deployed eight to nine use cases on DataRobot and have seen a tremendous response in accuracy and performance. We are pleased because we conducted a comparison. We took a model we built using a sample Python on a local machine and applied the same data and process using DataRobot Autopilot. The results were pretty amazing, with promising accuracy and recall. The accessibility is so easy. Even a college graduate with essential experience can use it. Suppose I do the same model in Databricks and want to monitor my MLOps pipeline. So, I need to use a third-party framework again, like MLflow, Kubeflow, Airflow, or whatever. I need to build my dashboards and everything, customization dashboards. However, everything is available in DataRobot. I can use it directly. They have a new option called DataRobot apps. So, on the predictions, we can even create customized apps. I can build my dashboard, and I can develop my applications. Overall, I rate the solution an eight out of ten.
Serge Dahdouh - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"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 can be easy to use."
"DataRobot is highly automated, allowing data scientists to build models easily."
"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."
"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."
"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 integration of AutoML features streamlines our machine-learning workflows."
"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 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."
"It provides the most valuable external analytics."
 

Cons

"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."
"There are some performance issues."
"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"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."
"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."
"The tool's documentation is not good. It is hard."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"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."
"I think the technical documentation is not readily available in the tool."
"I'm not sure if I have suggestions for improvement."
"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."
 

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 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
Educational Organization
25%
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
7%
Financial Services Firm
13%
Computer Software Company
13%
Manufacturing Company
9%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What needs improvement with DataRobot?
There are some performance issues when it comes to improvements. They also offer storage-related services compared to other tools like Admin, Azure, or AWS. It is easy to plug and play. Third-party...
What is your primary use case for DataRobot?
We work on AI and ML use cases related to technology and IT.
What advice do you have for others considering DataRobot?
Based on your similar requirements, V10.0 has very cool features related to AI Generation. I would suggest the team, too. This is very good for data scientists or people who don't want to code. Eve...
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...
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.
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.
 

Learn More

 

Overview

 

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
Information Not Available
Find out what your peers are saying about DataRobot vs. Google Vertex AI and other solutions. Updated: December 2024.
824,053 professionals have used our research since 2012.