We performed a comparison between Datadog and DataRobot based on real PeerSpot user reviews.
Find out what your peers are saying about Datadog, Dynatrace, New Relic and others in AIOps."The visibility that it provides is valuable. It is helping in being proactive around incident management. It is helping us to be able to get more visibility into our customers' applications so that we can assist them at the application layer. We also provide them the infrastructure from an AWS standpoint. We are able to make sure that our customers are aware of certain critical things around the analytical piece of either the network or the application. We're able to call customers before they even know about the issue. From there, we can start putting together some change management processes and help them a bit."
"Because of our client focus, it is easy for us to sell. This is because it is easy to use and easy to set up."
"We find they have a very helpful alert system."
"The flexibility to create notebooks and dashboards and fully customize them gives us a lot of power to track the exact services and endpoints we are working on."
"The many dozens of integrations that the solution brings out of the box are excellent."
"Straightforward to integrate and automate."
"Datadog has a lot of features to be able to drill down deep into the swath of logs that our platforms generate."
"Since we integrated Datadog, we have had increased confidence in the quality of our service, and we had an easier time increasing our delivery velocity."
"DataRobot can be easy to use."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"It could probably be a little bit of a better user experience."
"The error traceability is an area that can be improved."
"ECS could be improved by including more tutorials for beginners to reduce the barriers to entry."
"Datadog is so feature-rich that it is often hard to onboard new folks and tough to decide where to invest time."
"It could use some additional features when working with metrics like Grafana or like New Relic has. Datadog does not use library technologies like Dynatrace does. Datadog has machine learning too, but it does not have this option in all layers of monitoring like infrastructure service process in applications."
"We need a lot of modules since we collect all data logs from all operating systems."
"The on-premise version is very difficult to upgrade."
"We have recently had a number of issues with stability and delays on logging, monitoring, metric evaluation, and alerts."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
Earn 20 points
Datadog is ranked 1st in AIOps with 137 reviews while DataRobot is ranked 20th in AIOps. Datadog is rated 8.6, while DataRobot is rated 8.0. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of DataRobot writes "Easy to use, priced well, and can be customized". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Elastic Observability, whereas DataRobot is most compared with Amazon SageMaker, RapidMiner, Microsoft Azure Machine Learning Studio, Alteryx and SAS Predictive Analytics.
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