DataRobot vs IBM Turbonomic comparison

DataRobot and IBM are both solutions in the AIOps category. DataRobot is ranked #15 with an average rating of 8.7, while IBM is ranked #5 with an average rating of 8.6. DataRobot holds a 0.5% mindshare in AIOP, compared to IBM’s 0.5% mindshare. Additionally, 100% of DataRobot users are willing to recommend the solution, compared to 98% of IBM users who would recommend it.

DataRobot

Read 5 DataRobot reviews

1,973 Views
217 Comparison Views

100% willing to recommend

IBM Turbonomic

Read 205 IBM Turbonomic reviews

5,682 Views
170 Comparison Views

98% willing to recommend

DataRobot

IBM Turbonomic

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between DataRobot and IBM Turbonomic based on real PeerSpot user reviews.

Find out in this report how the two AIOps solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed DataRobot vs. IBM Turbonomic Report (Updated: July 2025).

Buyer's Guide

DataRobot vs. IBM Turbonomic

July 2025

Download the complete report

Helped 865,384 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

DataRobot

Ranking in AIOps

15th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Predictive Analytics (5th), AI Development Platforms (14th)

IBM Turbonomic

Ranking in AIOps

5th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

205

Ranking in other categories

Cloud Migration (4th), Cloud Management (4th), Virtualization Management Tools (3rd), IT Financial Management (1st), IT Operations Analytics (4th), Cloud Analytics (1st), Cloud Cost Management (1st)

Mindshare comparison

As of August 2025, in the AIOps category, the mindshare of DataRobot is 0.5%, up from 0.3% compared to the previous year. The mindshare of IBM Turbonomic is 0.5%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AIOps

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.

Read full review

Dan Ambrose

Infrastructure Engineer 4 at Hyland

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.

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

"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."

"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."

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

"DataRobot can be easy to use."

"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."

"The biggest value I'm getting out of VMTurbo right now is the complete hands-off management of equalizing the usage in my data center."

"My favorite part of the solution is the automation scheduling. Being able to choose when actions happen, and how they happen..."

"The automated memory balancing, where it looks at whether it's being used in the most efficient way and adds or takes away memory, is the best part. If it didn't do that, it would be something that I would have to do. We have too many machines for one person to do that. The automation helps me in that it is done in a really efficient way and a balanced way because of the policies. It really helps."

"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."

"We have seen a 30% performance improvement overall."

"Rightsizing is valuable. Its recommendations are pretty good."

More IBM Turbonomic pros

Cons

"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."

"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."

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

"The planning and costing areas could be a little bit more detailed. When you have more than 2,000 machines, the reports don't work properly. They need to fix it so that the reports work when you use that many virtual machines."

"There is an opportunity for improvement with some of Turbonomic's permissions internally for role-based access control. We would like the ability to come up with some customized permissions or scope permissions a bit differently than the product provides."

"There is room for improvement [with] upgrades. We have deployed the newer version, version 8 of Turbonomic. The problem is that there is no way to upgrade between major Turbonomic versions. You can upgrade minor versions without a problem, but when you go from version 6 to version 7, or version 7 to version 8, you basically have to deploy it new and let it start gathering data again. That is a problem because all of the data, all of the savings calculations that had been done on the old version, are gone. There's no way to keep track of your lifetime savings across versions."

"Turbonomic doesn't do storage placement how I would prefer. We use multiple shared storage volumes on VMware, so I don't have one big disk. I have lots of disks that I can place VMs on, and that consumes IOPS from the disk subsystem. We were getting recommendations to provision a new volume."

"We don't use Turbonomic for FinOps and part of the reason is its cost reporting. The reporting could be much more robust and, if that were the case, I could pitch it for FinOps."

"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."

"The issue for us with the automation is we are considering starting to do the hot adds, but there are some problems with Windows Server 2019 and hot adds. It is a little buggy. So, if we turn that on with a cluster that has a lot of Windows 2019 Servers, then we would see a blue screen along with a lot of applications as well. Depending on what you are adding, cores or memory, it doesn't necessarily even take advantage of that at that moment. A reboot may be required, and we can't do that until later. So, that decreases the benefit of the real-time. For us, there is a lot of risk with real-time."

"I like the detail I get in the old user interface and will miss some of that in the new interface when we perform our planned upgrade soon."

More IBM Turbonomic 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."

"Price is a big one. VMTurbo was very competitively priced."

"I know there have been some issues with the billing, when the numbers were first proposed, as to how much we would save. There was a huge miscommunication on our part. Turbonomic was led to believe that we could optimize our AWS footprint, because we didn't know we couldn't. So, we were promised savings of $750,000. Then, when we came to implement Turbonomic, the developers in AWS said, "Absolutely not. You're not putting that in our environment. We can't scale down anything because they coded it." Our AWS environment is a legacy environment. It has all these old applications, where all the developers who have made it are no longer with the company. Those applications generate a ton of money for us. So, if one breaks, we are really in trouble and they didn't want to have to deal with an environment that was changing and couldn't be supported. That number went from $750,000 to about $450,000. However, that wasn't Turbonomic's fault."

"When we have expanded our licensing, it has always been easy to make an ROI-based decision. So, it's reasonably priced. We would like to have it cheaper, but we get more benefit from it than we pay for it. At the end of the day, that's all you can hope for."

"It is an endpoint type license, which is fine. It is not overly expensive."

"It's worth the time and money investment if you can afford it."

"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."

"In the last year, Turbonomic has reduced our cloud costs by $94,000."

"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."

More IBM Turbonomic pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which AIOps solutions are best for your needs.

See recommendations

865,384 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

15%

Manufacturing Company

11%

Computer Software Company

Retailer

Financial Services Firm

12%

Computer Software Company

12%

Manufacturing Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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.

See all answers

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...

See all answers

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...

See all answers

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 ...

See all answers

What needs improvement with Turbonomic?

The implementation could be enhanced.

See all answers

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...

See all answers

Comparisons

Amazon SageMaker vs DataRobot

Compared 36% of the time

Microsoft Azure Machine Learning Studio vs DataRobot

Compared 9% of the time

Alteryx vs DataRobot

Compared 8% of the time

Altair RapidMiner vs DataRobot

Compared 7% of the time

Datadog vs DataRobot

Compared 7% of the time

More DataRobot Competitors

VMware Aria Operations vs IBM Turbonomic

Compared 15% of the time

Azure Cost Management vs IBM Turbonomic

Compared 10% of the time

VMware vSphere vs IBM Turbonomic

Compared 6% of the time

Cloudability vs IBM Turbonomic

Compared 6% of the time

Apptio One vs IBM Turbonomic

Compared 5% of the time

More IBM Turbonomic Competitors

Product Reports

Buyer's Guide

Predictive Analytics

August 2025

Download DataRobot product report

Buyer's Guide

IBM Turbonomic

August 2025

Download IBM Turbonomic product report

Also Known As

No data available

Turbonomic, VMTurbo Operations Manager

Interactive Demo

Demo not available

DataRobot

IBM

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

IBM Turbonomic offers automation, planning, and right-sizing recommendations to streamline resource management, improve efficiencies, and optimize costs across virtualized environments and cloud platforms.

IBM Turbonomic is valued for its capability to optimize resource allocation and monitor virtual environments efficiently. It facilitates automated decision-making in VM sizing, load balancing, and cost optimization for both on-premises and cloud deployments. Users can leverage insights for workload placement, ensure peak performance assurance, and effectively right-size across VMware and Azure. The ongoing transition to HTML5 aims to improve visual and navigational ease, while expanded reporting features are anticipated. Opportunities for improved training, documentation, and integrations enhance platform usability and functionality.

What Are the Key Features?

Automation: Streamlines resource allocation and VM management.
Planning Tools: Supports capacity management and growth projections.
Right-Sizing: Optimizes VM configurations for efficient resource use.
Customizable Dashboards: Provides quick insights into environment health.
Reporting Capabilities: Offers valuable data for informed decision-making.
Cloud Integration: Ensures cost-effective resource utilization.

What Benefits Should Be Looked For?

Performance Enhancement: Improves application performance efficiency.
Cost Reduction: Minimizes operational expenses through optimizations.
Infrastructure Efficiency: Maximizes use of IT infrastructure resources.

In finance, IBM Turbonomic aids in maintaining platform efficiency during market fluctuations. Healthcare organizations leverage its capability for resource optimization during high-demand periods to enhance patient care support. Retailers use it for planning in peak seasons, ensuring resources align with fluctuating demand to maintain performance continuity.

IBM

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.

Buyer's Guide

DataRobot vs. IBM Turbonomic

July 2025

Free Report: DataRobot vs. IBM Turbonomic

Find out what your peers are saying about DataRobot vs. IBM Turbonomic and other solutions. Updated: July 2025.

DOWNLOAD NOW

865,384 professionals have used our research since 2012.

See our DataRobot vs. IBM Turbonomic report.

See our list of best AIOps vendors.

We monitor all AIOps reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.