We performed a comparison between Amazon SageMaker and KNIME based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"We were able to use the product to automate processes."
"They are doing a good job of evolving."
"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 most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"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."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"This open-source product can compete with category leaders in ELT software."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It's a very powerful and simple tool to use."
"KNIME is easy to learn."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"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 documentation must be made clearer and more user-friendly."
"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."
"Lacking in some machine learning pipelines."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"The predefined workflows could use a bit of improvement."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"The documentation is lacking and it could be better."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too."
"The data visualization part is the area most in need of improvement."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Amazon SageMaker is rated 7.4, while KNIME 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 KNIME writes "A low-code platform that reduces data mining time by linking script". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Amazon Comprehend, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and SAS Analytics. See our Amazon SageMaker vs. KNIME 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.
