

Find out in this report how the two AIOps solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
I have seen a return on investment, specifically with increased data science productivity by four times, time saved with deploying models, and homogeneous analysis models developed easily.
On average, we're saving about 10 to 15 hours per project.
They answer all my questions and share guidance on using DataRobot scripts if certain functionalities are not available in the UI.
Being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.
The customer support from DataRobot is proactive and responsive.
DataRobot's scalability is very strong and grows with my organization's needs.
DataRobot is very stable.
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python.
There is a lack of transparency in the models; sometimes it feels like a black box.
Another improvement that DataRobot needs is integrating the capability to modify the whole pipeline with Python.
The setup cost was minimal because it's cloud-hosted, eliminating the need for heavy on-premises infrastructure, allowing us to start using it immediately after purchase.
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.
DataRobot has positively impacted our organization in many ways. First, it has improved efficiency; tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours.
By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.
When business leaders ask for the fastest possible solution, DataRobot is our go-to platform.
| Product | Mindshare (%) |
|---|---|
| DataRobot | 1.1% |
| IBM Turbonomic | 1.1% |
| Other | 97.8% |

| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 41 |
| Midsize Enterprise | 57 |
| Large Enterprise | 147 |
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.
IBM Turbonomic enhances IT efficiency with automation, capacity planning, and reporting features, enabling organizations to optimize resource utilization and improve performance through advanced workload management and scenario analysis.
IBM Turbonomic equips organizations with robust capabilities for dynamic resource allocation and informed decision-making. Its planning module provides scenario analysis, right-sizing recommendations, and a customizable dashboard for tailored insights. Automation features improve workload placements and resource efficiency, while forecasting capabilities enhance performance. Simulation of environments helps in decision-making, leading to significant savings in cloud and hardware management. There is a need for a more intuitive interface, enhanced navigation, and improved customization in reporting with integration potential with third-party applications. Transition to the HTML5 interface and stronger training resources are among anticipated improvements.
What are the key features of IBM Turbonomic?IBMTurbonomic is implemented across industries such as cloud management and virtualization, helping organizations balance clusters, optimize virtual machine performance, and manage Azure configurations. In resource-monitored environments like VMware and XenServer, its features facilitate load balancing, VM rightsizing, and automation shutoffs. Industries can rely on its insights for cost-saving measures, ensuring efficient resource allocation for hybrid and cloud environments.
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