DataRobot vs TensorFlow comparison

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DataRobot

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Executive SummaryUpdated on Dec 4, 2024

DataRobot and TensorFlow are competing in the AI and machine learning space, each targeting different needs. DataRobot offers an edge in ease of use and rapid deployment, while TensorFlow provides robust features and scalability for technical experts seeking advanced customization.

Features:DataRobot features automated model building, user-friendly operation without requiring extensive programming skills, and strong predictive analytics. TensorFlow provides a flexible framework, broad applicability for deep learning tasks, and scalability for handling large datasets and complex models.

Room for Improvement:DataRobot could enhance its features for comprehensive data processing, expand its customization options, and improve open-source integration. TensorFlow might focus on simplifying its deployment process for non-technical users, enhancing its user interface, and improving efficiency in training on smaller datasets.

Ease of Deployment and Customer Service:DataRobot facilitates a quick deployment process with extensive support appealing to non-technical users. TensorFlow requires more technical expertise for deployment but offers abundant community resources and detailed documentation.

Pricing and ROI:DataRobot generally has higher initial setup costs but offers rapid ROI through its automated features and predictive tools. TensorFlow, being open-source, has low initial costs, shifting ROI focus to the development expertise required. It remains cost-effective for those investing in technical resources for custom model development.

To learn more, read our detailed DataRobot vs. TensorFlow Report (Updated: September 2025).

DataRobot vs. TensorFlow

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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

DataRobot

Ranking in AI Development Platforms

14th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Predictive Analytics (5th), AIOps (16th)

TensorFlow

Ranking in AI Development Platforms

7th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of DataRobot is 1.6%, up from 1.3% compared to the previous year. The mindshare of TensorFlow is 6.6%, up from 4.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
TensorFlow	6.6%
DataRobot	1.6%
Other	91.8%

AI Development Platforms

Featured Reviews

SagarYadav

Staff Specialist Data Scientist at BMC Software, Inc.

Automating model comparison speeds up development and reduces timelines

DataRobot is equipped with a GUI-based approach that simplifies the process of feature engineering and model training. It provides AutoML capabilities, which allow for comparing thousands of models and selecting the best-suited one based on business requirements. By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.

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Dan Bryant

CEO at II4Tech

A strong solution for providing insight into machine learning strategies

I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist. In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation. Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant. I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.

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Quotes from Members

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

Pros

"DataRobot can be easy to use."

"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."

"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."

"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."

"DataRobot is highly automated, allowing data scientists to build models easily."

"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."

"The available documentation is extensive and helpful."

"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."

"TensorFlow provides Insights into both data and machine learning strategies."

"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."

"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."

"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."

"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."

More TensorFlow pros

Cons

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."

"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."

"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."

"There are some performance issues."

"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."

"The solution is hard to integrate with the GPUs."

"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."

"We encountered version mismatch errors while using the product."

"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."

"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."

"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."

"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."

"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."

More TensorFlow cons

Pricing and Cost Advice

"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 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."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"The solution is free."

"I am using the open-source version of TensorFlow and it is free."

"We are using the free version."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

"I rate TensorFlow's pricing a five out of ten."

"I did not require a license for this solution. It a free open-source solution."

"TensorFlow is free."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Manufacturing Company

11%

Computer Software Company

11%

Retailer

Manufacturing Company

13%

Computer Software Company

11%

Financial Services Firm

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	3

Questions from the Community

What needs improvement with DataRobot?

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.

What is your primary use case for DataRobot?

In our day-to-day use, I utilize DataRobot to speed up our development process through its GUI capability. Once I set up our connection with a back-end data set, whatever the project I work on next...

What advice do you have for others considering DataRobot?

I would recommend DataRobot because if there is something not included in the UI, I have the freedom to use its Python API, which extends the capability for different use cases. Additionally, I wou...

What do you like most about TensorFlow?

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

What is your experience regarding pricing and costs for TensorFlow?

I am not familiar with the pricing setup cost and licensing.

What needs improvement with TensorFlow?

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...

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Product Reports

Predictive Analytics

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Overview

DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.

DataRobot

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant

Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm

DataRobot vs. TensorFlow