

DataRobot and Devo cater to different needs in AI and data analytics. Users generally find the feature set of Devo superior, while DataRobot's pricing and support receive higher satisfaction scores. Despite this, Devo's enriched features outweigh its higher price.
Features: DataRobot offers automated data preparation, predictive modeling, and deployment, which is comprehensive. Devo excels in real-time data analytics and visualization, scoring higher for its integration capabilities. Users highlight Devo's advanced analytics as more valuable, though both platforms have robust feature sets.
Room for Improvement: Users of DataRobot point out the need for enhanced customizability and more intuitive tools for non-technical users. Devo users suggest improvements in documentation and more streamlined onboarding processes. DataRobot's feedback emphasizes usability improvements, while Devo focuses on user experience enhancements.
Ease of Deployment and Customer Service: DataRobot is praised for its straightforward deployment process and responsive customer service. Devo's deployment is slightly more complex due to its advanced features, but it also has commendable support. DataRobot has an edge in ease of deployment, while both provide reliable customer service.
Pricing and ROI: DataRobot is rated favorably for its competitive pricing and clear ROI, making it attractive for budget-conscious buyers. Devo's pricing is higher, but users believe its feature set justifies the cost, providing substantial ROI. DataRobot's lower cost appeals to many, but Devo's advanced features deliver considerable value for the investment.
Previously we had five employees doing the entire workflow, and now we can do it with two employees because agents are being used to do the same which was previously being done by the employees.
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.
The DataRobot team was very helpful in answering the questions which the customer had.
Being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.
I rate the customer support a nine out of ten because of their timely technical guidance and responsiveness during the deployment and troubleshooting periods.
DataRobot is very scalable because the customer initially started with two licenses, and now they have around 20 licenses.
Devo is a unified SIEM solution designed to handle growing log volumes and enterprise-scale monitoring requirements.
It is stable and reliable for our security operations.
If DataRobot also adds those data transformation capabilities, then it will be an end-to-end tool and the customer will not have to procure many tools for doing the ingestion and transformation process.
There is a lack of transparency in the models; sometimes it feels like a black box.
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python.
UI improvements, a simplified dashboard, or an easier reporting workflow could further improve analyst productivity.
Integrations with other sandboxes could be improved to better interpret data using AI and machine learning models.
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.
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.
DataRobot has a very good agentic interface where one can actually spin up multiple agents at the click of a mouse and can have multi-agent protocol.
When they see a spike in a line chart for a failed login, which could be a true or false attempt, they can click that spike, and a table widget on the same active board instantly populates with raw logs of data for those specific failed logins.
When the analyst uses queries to search, it pulls the data quickly, in a second, which aids us greatly with the investigation.
| Product | Mindshare (%) |
|---|---|
| DataRobot | 1.5% |
| Devo | 1.6% |
| Other | 96.9% |

| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 4 |
| Large Enterprise | 12 |
DataRobot automates model building and deployment, simplifying MLOps with user-friendly interfaces. Its AutoML and feature engineering streamline model comparison, selection, and testing, enhancing efficiency and scalability.
DataRobot facilitates efficient integration with cloud systems and data sources, reducing manual workload, enhancing productivity, and empowering data-driven decision-making. Its strengths lie in automating complex modeling tasks and supporting multiple predictive models effectively. Users emphasize the need for better handling of large datasets, integration with orchestration tools, and more flexibility for custom code integration and advanced model tuning. They also seek improved support response times, transparent model processing, real-world documentation, and enhanced capabilities in generative AI and accuracy metrics.
What are the key features of DataRobot?DataRobot is adopted across industries like healthcare and education for creating and monitoring machine learning models. It accelerates development with GUI capabilities, aids data cleaning, and optimizes feature engineering and deployment. Organizations can predict behaviors, automate tasks, manage production models, and integrate into data science processes to improve data processing and maximize efficiency.
Devo offers powerful visual analytics, real-time data querying, and log integration capabilities within a cloud-native, multi-tenant architecture, supporting extended data retention ideal for long-term analysis and compliance.
Devo is recognized for its Activeboards, which facilitate visual analytics. High-speed search capabilities and real-time analytics enable efficient data manipulation and querying. Its multi-tenant architecture supports effective data segregation and customization tailored to distinct business needs, enhancing its value for handling complex log integrations. With extended data retention of 400 days and a cloud-native architecture, Devo is a robust platform for long-term analysis and compliance requirements. Though opportunities exist to improve browser stability on large searches, SOAR integrations, and its parser capabilities, Devo remains essential for incident response and security monitoring, offering centralized data storage and analysis.
What are Devo's most important features?Devo is extensively used in industries focused on incident response and digital forensics, centralizing data for security monitoring across hybrid environments. Organizations benefit from its ability to store and analyze aggregated logs, creating alerts and dashboards to enhance visibility for network and endpoint activities in multi-domain settings.
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