

LogicMonitor and DataRobot are two prominent tools in the tech landscape, each catering to different monitoring and machine learning needs. LogicMonitor seems to have the upper hand in terms of pricing and customer support, while DataRobot stands out for its advanced features and robustness.
Features: LogicMonitor's valuable features include comprehensive infrastructure monitoring, scalability, and integration capabilities. DataRobot is praised for its automated machine learning, ease of use in building models, and predictive analytics.
Room for Improvement: LogicMonitor users suggest enhancements in reporting features, better customization options, and usability improvements. DataRobot users recommend improvements in deployment flexibility, increased transparency in algorithm selection, and technical advancements.
Ease of Deployment and Customer Service: LogicMonitor offers a quick and straightforward deployment process and receives favorable reviews for customer support. DataRobot’s deployment is perceived as more complex by users, but its customer service is also well-received.
Pricing and ROI: LogicMonitor is noted for its cost-effectiveness and delivers a higher ROI according to user reviews. DataRobot, although more expensive, is considered to provide substantial value through advanced features.
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.
The return is more of value and savings in preventing costly downtime, making the savings of about $60,000 which we would have lost without LogicMonitor, and in IT staff efficiency, we save approximately 15 hours a week.
Because of LogicMonitor, we have reduced our EC2 infrastructure significantly, which has helped us reduce costs by 20%.
Downtime on each network asset has been reduced, and there is now better visibility for the operations team to manage 24/7 support.
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.
Within one day, I received a script, and LogicMonitor was able to provide the firewall configuration in LogicMonitor on the same day I submitted the request.
Customer support is on point and very well trained.
We need to be able to reach them in real-time, and without those kinds of options available, we have to set up ad hoc calls, which could be improved.
DataRobot is very scalable because the customer initially started with two licenses, and now they have around 20 licenses.
They are not licensed, so you could deploy one collector or 1,000 collectors for the same cost.
LogicMonitor's scalability absolutely meets our organization's growth needs.
LogicMonitor is pretty good at scaling things when it comes to monitoring AWS infrastructure because I can see that it scales very well for us.
The platform is reliable, alerts are consistent, and once collectors and integrations are in place, monitoring runs smoothly with minimal disruption.
It is very stable. I have never seen LogicMonitor itself go down.
Since we implemented LogicMonitor and got it working in production, there has been no downtime, no reliability issues, and nothing major regarding flare-ups from LogicMonitor's perspective.
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.
I would also appreciate a stronger out-of-the-box AWS correlation, such as automatically grouping related issues across EC2, EBS, and ALBs in a way that reads as a single incident story.
For example, when we monitor a particular device with a temperature issue or high-temperature problem, sometimes I observe that in real time when I log into the device, the temperature shows something that does not accurately match what is displayed on the LogicMonitor platform.
I wish the user interface would be customizable to allow users to create personal context-specific workspaces to hide irrelevant data, rather than trying to have a one-size-fits-all interface.
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.
For small businesses that want to utilize LogicMonitor and are just starting out with limited customers, a pricing model targeted to this segment would be beneficial, perhaps at three or two dollars per device per month.
I experienced no issues with pricing, setup cost, and licensing; it was very transparent, and the licensing model is very clear and easy to understand.
The pricing model is subscription-based.
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.
The dynamic alerting and root cause analysis have helped us fix issues before they cause a full-blown outage or degrade performance for end users.
Our SLAs and SLOs were averaging about 10 to 15 failed SLAs and SLOs that were over the time allotted to get those resolved, and those are now down to about two to three per week.
When talking about the statistics, it has helped us reduce downtime to about 40 to 50% because without LogicMonitor, we used to know about the downtime only when the application was actually down.
| Product | Mindshare (%) |
|---|---|
| LogicMonitor | 5.5% |
| DataRobot | 1.6% |
| Other | 92.9% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 12 |
| Large Enterprise | 23 |
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.
LogicMonitor offers flexible IT monitoring with customizable dashboards and robust alerting capabilities. It integrates seamlessly with third-party apps like ServiceNow and provides a single-pane view for diverse IT environments, aiding in proactive issue resolution and enhancing operational efficiency.
LogicMonitor stands out with its capability to monitor diverse infrastructures including Cisco Voice systems, data centers, and virtual environments. Supporting servers, storage, networking devices, and applications, it provides seamless integration with cloud services like AWS and Azure. Users leverage its scalability and flexibility, benefiting from dynamic thresholds, anomaly detection, and detailed visualization. All these features contribute to improved management of IT assets and streamlined operations. Users suggest improvements in mapping, reporting, and automation for remediation, desiring more customizations and an expansive application performance monitoring toolset.
What are LogicMonitor's key features?LogicMonitor is widely implemented across industries, providing monitoring for infrastructure in sectors like telecommunications, cloud computing, and managed services. Managed service providers particularly value its ability to track client environments, deliver proactive alerts, and generate comprehensive reports, while its integration with cloud platforms like AWS and Azure offers users centralized management and visibility into IT assets worldwide.
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