We compared Databricks and Amazon SageMaker based on our user's reviews in several parameters.
Databricks offers seamless integration with various data sources, advanced analytics capabilities, and efficient customer service. Users appreciate the collaborative features and positive ROI. On the other hand, Amazon SageMaker is praised for its ease of use, comprehensive ML capabilities, and robust monitoring tools. Users find the pricing transparent and support team responsive.
Features: Databricks is known for its seamless integration with various data sources and platforms, collaborative capabilities, advanced analytics, and machine learning capabilities. On the other hand, Amazon SageMaker offers ease of use, comprehensive machine learning capabilities, seamless integration with other AWS services, customizable workflows, efficient model training and deployment, automated data labeling, and robust monitoring and troubleshooting tools.
Pricing and ROI: Databricks users have reported positive feedback on pricing, setup cost, and licensing. The setup cost is straightforward and hassle-free, while the license terms offer flexibility. Similarly, Amazon SageMaker users find the pricing reasonable, setup cost hassle-free, and licensing process clear and transparent., Users have reported positive outcomes and returns on investment with Databricks, appreciating its impact on efficiency, productivity, and data analysis capabilities. Similarly, Amazon SageMaker delivers positive ROI, providing value and benefits for businesses.
Room for Improvement: Databricks has room for improvement in aspects such as data visualization, monitoring and debugging tools, integration with external data sources and services, documentation and tutorials, and pricing flexibility. In comparison, users have identified areas for enhancement in Amazon SageMaker.
Deployment and customer support: Based on user reviews, there are varying durations required for deploying, setting up, and implementing a new tech solution on both Databricks and Amazon SageMaker. While some users mentioned spending three months on deployment and a week on setup for both products, it is important to evaluate the context to determine if these terms refer to the same period or should be considered separately., Customers have reported positive experiences with both Databricks and Amazon SageMaker customer service. Databricks is praised for its efficiency and proactive approach, while SageMaker is commended for its attentiveness and commitment to customer needs.
The summary above is based on 56 interviews we conducted recently with Databricks and Amazon SageMaker users. To access the review's full transcripts, download our report.
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"Allows you to create API endpoints."
"They are doing a good job of evolving."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."
"It is fast, it's scalable, and it does the job it needs to do."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"The integration with Python and the notebooks really helps."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"Lacking in some machine learning pipelines."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The solution requires a lot of data to train the model."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"Can be improved by including drag-and-drop features."
"A lot of people are required to manage this solution."
"It's not easy to use, and they need a better UI."
"There should be better integration with other platforms."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"There is room for improvement in visualization."
"CI/CD needs additional leverage and support."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Amazon SageMaker is rated 7.4, while Databricks is rated 8.2. 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 Databricks writes "A nice interface with good features for turning off clusters to save on computing". Amazon SageMaker is most compared with Azure OpenAI, Google Vertex AI, Domino Data Science Platform, Microsoft Azure Machine Learning Studio and Dataiku, whereas Databricks is most compared with Informatica PowerCenter, Dataiku, Dremio, Microsoft Azure Machine Learning Studio and Azure Stream Analytics. See our Amazon SageMaker vs. Databricks report.
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