

New Relic and DataRobot compete in the tech products category. DataRobot appears more favored due to its robust features and perceived value.
Features: New Relic is known for its comprehensive performance monitoring, custom dashboards, and detailed transaction tracking. DataRobot excels with its advanced machine learning capabilities, ease of use, and strong predictive analytics. DataRobot's extensive feature set is more appealing to users seeking comprehensive data science solutions.
Room for Improvement: New Relic could improve in integration complexity, occasional performance lags, and user interface intuitiveness. DataRobot needs better documentation, more customizable reporting tools, and enhanced data import options. Each product has distinct areas requiring enhancement, with New Relic focusing on integrations and DataRobot on user resources.
Ease of Deployment and Customer Service: New Relic has mixed reviews about deployment, with some users finding it straightforward and others noting initial setup challenges. Customer service is generally well-received. DataRobot has a smoother deployment process, with strong customer support assisting throughout. DataRobot stands out for its ease of deployment and reliable support.
Pricing and ROI: New Relic users report varying setup costs but find the overall ROI satisfactory. DataRobot's setup cost is viewed as higher; however, users feel the ROI justifies the expense due to its robust feature set. DataRobot is seen as providing better value despite higher costs.
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.
There is return on investment because since we reduced the downtime, we can definitely save a lot of money within that period.
There is a definite return on investment for New Relic, as we would not have invested in building its infrastructure if there were no returns.
After implementing New Relic, we have decreased staffing requirements while saving time and money.
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 DataRobot team was very helpful in answering the questions which the customer had.
If I drop an email to them, they will respond quickly to my email.
Customer support from New Relic is very good, and we rarely need to create support tickets.
They are very polite and helped him out.
DataRobot is very scalable because the customer initially started with two licenses, and now they have around 20 licenses.
We currently use New Relic for tens of thousands of developers and hundreds of teams within our organization, and we have not encountered any scalability issues.
It is also suitable for cloud native architectures, SaaS, or software as a service, and for high volume data ingestion also.
Regarding New Relic's scalability, it excels at the enterprise level for cloud integrations that can utilize tags.
New Relic lags sometimes.
New Relic is stable based on my experience, as I have not seen any problems with the UI.
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.
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.
If they could improve the customer support by reducing their SLA within three to five days, if they could remediate everything, that will be so much helpful.
Using real-time data, if there are any malicious patterns or something happening, they can identify those.
Because of the pricing model, organizations have experienced uncontrolled costs and were not able to afford New Relic.
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.
Considering the features New Relic offers, the pricing or cost setup has not been a blocker for our budget.
My experience with pricing, setup cost, and licensing for synthetic monitoring is that minions used to cost a lot.
As we talk about pricing, it is not that much cheaper.
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 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.
DataRobot's one of the major features is model evaluation and model performance.
Using New Relic speeds up troubleshooting and resolution, giving us a clearer picture of where issues are, thus saving time and effort.
New Relic is very useful for teams that don't have much of a dedicated DevOps team but want to have observability for their platform, and it's an easy way to get started.
New Relic has positively impacted our organization by reducing errors, improving performance, and saving time.
| Product | Mindshare (%) |
|---|---|
| New Relic | 7.5% |
| DataRobot | 1.6% |
| Other | 90.9% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
| Company Size | Count |
|---|---|
| Small Business | 65 |
| Midsize Enterprise | 51 |
| Large Enterprise | 79 |
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.
New Relic offers real-time application monitoring and insight into performance bottlenecks. Its customizable dashboards and APM integration provide efficient operational support, while server performance alerts ensure quick issue detection.
New Relic provides comprehensive monitoring of application performance, tracking bottlenecks across databases and front-end components. Users employ it for server and infrastructure monitoring, as well as analyzing key metrics such as CPU and memory usage. The solution's ability to integrate with tools like PagerDuty enhances incident management capabilities. However, users have expressed a need for improvements in query language simplicity, more detailed historical insights, and better mobile app monitoring support.
What are New Relic's most important features?In industries like e-commerce and financial services, New Relic supports application performance monitoring to enhance user experience and system reliability. Organizations leverage its insights for optimizing performance, particularly in server operations and infrastructure management. Its ability to monitor API failures through synthetic monitoring is crucial for maintaining high service levels.
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