

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.
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.
One of the metrics that helped as a return on investment was the ability to detect issues faster and troubleshoot more quickly, which in turn helped to achieve a much better service level agreement with customers.
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.
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.
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.
New Relic's scalability is good based on my experience, and it can handle my organization's needs as they grow.
New Relic lags sometimes.
New Relic is stable based on my experience, as I have not seen any problems with the UI.
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.
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.
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 | Market Share (%) |
|---|---|
| New Relic | 9.0% |
| DataRobot | 1.0% |
| Other | 90.0% |


| Company Size | Count |
|---|---|
| Small Business | 65 |
| Midsize Enterprise | 50 |
| Large Enterprise | 71 |
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.
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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