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DataRobot vs IBM Turbonomic comparison

 

Comparison Buyer's Guide

Executive Summary

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 AIOps
16th
Average Rating
8.6
Reviews Sentiment
7.2
Number of Reviews
5
Ranking in other categories
Predictive Analytics (5th), AI Development Platforms (14th)
IBM Turbonomic
Ranking in AIOps
7th
Average Rating
8.8
Reviews Sentiment
7.4
Number of Reviews
205
Ranking in other categories
Cloud Migration (7th), Cloud Management (5th), Virtualization Management Tools (4th), IT Financial Management (1st), IT Operations Analytics (4th), Cloud Analytics (1st), Cloud Cost Management (1st)
 

Mindshare comparison

As of October 2025, in the AIOps category, the mindshare of DataRobot is 0.5%, up from 0.5% compared to the previous year. The mindshare of IBM Turbonomic is 0.6%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AIOps Market Share Distribution
ProductMarket Share (%)
IBM Turbonomic0.6%
DataRobot0.5%
Other98.9%
AIOps
 

Featured Reviews

SagarYadav - PeerSpot reviewer
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.
Dan Ambrose - PeerSpot reviewer
Helps visibility, bridges the data gap, and frees up time
We use IBM Turbonomic in a hybrid cloud environment. Although it supports multi-cloud capabilities, we currently operate in a single-cloud setting. Turbonomic offers visibility into our environment's performance, spanning across applications, underlying infrastructure, and protection resources. The visibility and analytics help to bridge the data gap between disparate IT teams such as applications and infrastructure. This is important for awareness collaboration, cost saving, and helping to design and improve our application. Enhanced visibility and data analytics have contributed to a significant reduction in our mean time to resolve. Tools like Turbonomic provide crucial visualization and insights, empowering us to make data-driven decisions instead of relying on assumptions as we did before. This newfound transparency translates to a massive improvement, going from complete darkness to having a clear 100 percent view of the situation. Although our applications are not optimized for the cloud we have seen some improvement in response time. IBM Turbonomic empowers us to achieve more with fewer people thanks to automation. Previously, customers frequently contacted us requesting resource increases to resolve issues. Now, we have a tool that allows us to objectively assess their needs, leading to a deeper understanding of our applications. This solution also generates significant cost savings in the cloud and optimizes hardware utilization within our data centers. Its AI algorithm intelligently allocates servers on hosts, maximizing efficiency without compromising performance. By fine-tuning resource allocation without causing performance bottlenecks, Turbonomic extends the lifespan of existing hardware, postponing the need for new purchases. This effectively stretches our capital expenditure budget. We started to see the benefits of IBM Turbonomic within the first 60 days. IBM is a fantastic partner. Their tech support has been outstanding, and the product itself is excellent - a very solid offering. By automating resource management with Turbonomic, our engineers are freed up to focus on more strategic initiatives like innovation and ongoing organizational projects. Previously, manually adding resources was a time-consuming process that interrupted workflows. Now, automation handles scaling efficiently, saving us thousands of man-hours and significant costs. It has illuminated the need for SetOps. It has highlighted areas of overspending, and the actions we've taken have demonstrated significant cost savings. IBM Turbonomic has positively impacted our overall application performance. IBM Turbonomic has helped reduce both CAPEX and OPEX. It has also significantly reduced cloud build times.

Quotes from Members

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

Pros

"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."
"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."
"DataRobot is highly automated, allowing data scientists to build models easily."
"DataRobot can be easy to use."
"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."
"I like Turbonomic's built-in reporting. It provides a ton of information out of the box, so I don't have to build panels for the monthly summaries and other reports I need to present to management. We get better performance and bottleneck reporting from this than we do from our older EMC software."
"The ability to monitor and automate both the right-sizing of VMs as well as to automate the vMotion of VMs across ESXi hosts."
"We like that Turbonomic shows application metrics and estimates the impact of taking a suggested action. It provides us a map of resource utilization as part of its recommendation. We evaluate and compare that to what we think would be appropriate from a human perspective to that what Turbonomic is doing, then take the best action going forward."
"It helps us get a consolidated view of all customer spending into a single dashboard, allowing us to identify opportunities to improve their current spending."
"The tool provides the ability to look at the consumption utilization over a period of time and determine if we need to change that resource allocation based on the actual workload consumption, as opposed to how IT has configured it. Therefore, we have come to realize that a lot of our workloads are overprovisioned, and we are spending more money in the public cloud than we need to."
"I like the analytics that help us optimize compatibility. Whereas Azure Advisor tells us what we have to do, Turbonomic has automation which actually does those things. That means we don't have to be present to get them done and simplifies our IT engineers' jobs."
"We've saved hundreds of hours. Most of the time those hours would have to be after hours as well, which are more valuable to me as that's my personal time."
"The solution has a good optimization feature."
 

Cons

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"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."
"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."
"I do not like Turbonomic's new licensing model. The previous model was pretty straightforward, whereas the new model incorporates what most of the vendors are doing now with cores and utilization. Our pricing under the new model will go up quite a bit. Before, it was pretty straightforward, easy to understand, and reasonable."
"If they would educate their customers to understand the latest updates, that would help customers... Also, there are a lot of features that are not available in Turbonomic. For example, PaaS component optimization and automation are still in the development phase."
"They could add a few more reports. They could also be a bit more granular. While they have reports, sometimes it is hard to figure out what you are looking for just by looking at the date."
"Some features are only available via changes to the deployment YAML, and it would be better to have them in the UI."
"Remove the need for special in-house knowledge and development."
"The deployment process is a little tricky. It wasn't hard for me because I have pretty in-depth knowledge of Kubernetes, and their software runs on Kubernetes. To deploy it or upgrade it, you have to be able to follow steps and use the Kubernetes command line, or you'll need someone to come in and do it for you."
"The old interface was not the clearest UI in some areas, and could be quite intimidating when first using the tool."
"It would be nice for them to have a way to do something with physical machines, but I know that is not their strength Thankfully, the majority of our environment is virtual, but it would be nice to see this type of technology across some other platforms. It would be nice to have capacity planning across physical machines."
 

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."
"You should understand the cost of your physical servers and how much time and money you are spending year over year on expanding your virtual farm."
"In the last year, Turbonomic has reduced our cloud costs by $94,000."
"It's worth the time and money investment if you can afford it."
"Licensing is per socket, so load up on the cores rather than a lot of lower core CPUs."
"I don't know the current prices, but I like how the licensing is based on the number of instances instead of sockets, clusters, or cores. We have some VMs that are so heavy I can only fit four on one server. It's not cost-effective if we have to pay more for those. When I move around a VM SQL box with 30 cores and a half-terabyte of RAM, I'm not paying for an entire socket and cores where people assume you have at least 10 or 20 VMs on that socket for that pricing."
"It was an annual buy-in. You basically purchase it based on your host type stuff. The buy-in was about 20K, and the annual maintenance is about $3,000 a year."
"We see ROI in extended support agreements (ESA) for old software. Migration activities seem to be where Turbonomic has really benefited us the most. It's one click and done. We have new machines ready to go with Turbonomic, which are properly sized instead of somebody sitting there with a spreadsheet and guessing. So, my return on investment would certainly be on currency, from a software and hardware perspective."
"It is an endpoint type license, which is fine. It is not overly expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Manufacturing Company
11%
Computer Software Company
11%
Retailer
8%
Financial Services Firm
12%
Computer Software Company
12%
Manufacturing Company
9%
Insurance Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business41
Midsize Enterprise57
Large Enterprise147
 

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 is your experience regarding pricing and costs for Turbonomic?
It offers different scenarios. It provides more capabilities than many other tools available. Typically, its price is set as a percentage of the consumption of some of our customers' services. The ...
What needs improvement with Turbonomic?
The implementation could be enhanced.
What is your primary use case for Turbonomic?
We use IBM Turbonomic to automate our cloud operations, including monitoring, consolidating dashboards, and reporting. This helps us get a consolidated view of all customer spending into a single d...
 

Also Known As

No data available
Turbonomic, VMTurbo Operations Manager
 

Interactive Demo

Demo not available
 

Overview

 

Sample Customers

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant
IBM, J.B. Hunt, BBC, The Capita Group, SulAmérica, Rabobank, PROS, ThinkON, O.C. Tanner Co.
Find out what your peers are saying about DataRobot vs. IBM Turbonomic and other solutions. Updated: September 2025.
869,202 professionals have used our research since 2012.