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.
"We've had no problems with SageMaker's stability."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"We were able to use the product to automate processes."
"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."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The deployment is very good, where you only need to press a few buttons."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."
"Its lightweight and fast processing are valuable."
"The fast data loading process and data storage capabilities are great."
"Databricks has helped us have a good presence in data."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"Easy to use and requires minimal coding and customizations."
"The integration with Python and the notebooks really helps."
"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."
"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 pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"Lacking in some machine learning pipelines."
"SageMaker would be improved with the addition of reporting services."
"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."
"Databricks can improve by making the documentation better."
"Doesn't provide a lot of credits or trial options."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"The tool should improve its integration with other products."
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, Dataiku and DataRobot, 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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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.
