We performed a comparison between Amazon SageMaker and DataRobot based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"We were able to use the product to automate processes."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The deployment is very good, where you only need to press a few buttons."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"They are doing a good job of evolving."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"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."
"DataRobot can be easy to use."
"There are other better solutions for large data, such as Databricks."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"Lacking in some machine learning pipelines."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The documentation must be made clearer and more user-friendly."
"The solution is complex to use."
"SageMaker would be improved with the addition of reporting services."
"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
Amazon SageMaker is ranked 5th in AI Development Platforms with 19 reviews while DataRobot is ranked 13th in AI Development Platforms. Amazon SageMaker is rated 7.4, while DataRobot is rated 8.0. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of DataRobot writes "Easy to use, priced well, and can be customized". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Cloudera Data Science Workbench, whereas DataRobot is most compared with RapidMiner, Microsoft Azure Machine Learning Studio, Datadog, Alteryx and SAS Predictive Analytics. See our Amazon SageMaker vs. DataRobot report.
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We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
