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.
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"They are doing a good job of evolving."
"Allows you to create API endpoints."
"The few projects we have done have been promising."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"The processing capacity is tremendous in the database."
"Automation with Databricks is very easy when using the API."
"The main features of the solution are efficiency."
"The integration with Python and the notebooks really helps."
"The solution offers a free community version."
"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"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."
"Lacking in some machine learning pipelines."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"The solution requires a lot of data to train the model."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"The solution needs to be cheaper since it now charges per document for extraction."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"Anyone who doesn't know SQL may find the product difficult to work with."
"In the next release, I would like to see more optimization features."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"The product should provide more advanced features in future releases."
"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"I believe that this product could be improved by becoming more user-friendly."
"Pricing is one of the things that could be improved."
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 Data Science Studio, whereas Databricks is most compared with Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio, Dremio and Azure Stream Analytics. See our Amazon SageMaker vs. Databricks report.
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We monitor all Data Science 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.