Amazon SageMaker vs Dataiku comparison

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Amazon Web Services (AWS) Logo
11,426 views|9,062 comparisons
84% willing to recommend
Dataiku Logo
9,109 views|7,135 comparisons
100% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Amazon SageMaker and Dataiku 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.
To learn more, read our detailed Amazon SageMaker vs. Dataiku Report (Updated: May 2024).
771,581 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides.""The product aggregates everything we need to build and deploy machine learning models in one place.""Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker.""The tool makes our ML model development a bit more efficient because everything is in one environment.""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 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 few projects we have done have been promising.""We've had no problems with SageMaker's stability."

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"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.""I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.""If many teams are collaborating and sharing Jupyter notebooks, it's very useful.""The most valuable feature is the set of visual data preparation tools.""The solution is quite stable.""Cloud-based process run helps in not keeping the systems on while processes are running.""The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.""Data Science Studio's data science model is very useful."

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Cons
"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 solution is complex to use.""The documentation must be made clearer and more user-friendly.""Lacking in some machine learning pipelines.""AI is a new area and AWS needs to have an internship training program available.""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.""There are other better solutions for large data, such as Databricks.""The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

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"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective.""In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.""The ability to have charts right from the explorer would be an improvement.""There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.""I think it would help if Data Science Studio added some more features and improved the data model.""I find that it is a little slow during use. It takes more time than I would expect for operations to complete.""Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).""Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

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Pricing and Cost Advice
  • "The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
  • "The support costs are 10% of the Amazon fees and it comes by default."
  • "SageMaker is worth the money for our use case."
  • "Databricks solution is less costly than Amazon SageMaker."
  • "I would rate the solution's price a ten out of ten since it is very high."
  • "There is no license required for the solution since you can use it on demand."
  • "I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."
  • "You don't pay for Sagemaker. You only pay for the compute instances in your storage."
  • More Amazon SageMaker Pricing and Cost Advice →

  • "The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
  • "Pricing is pretty steep. Dataiku is also not that cheap."
  • More Dataiku Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:We researched AWS SageMaker, but in the end, we chose Databricks Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
    Top Answer: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… more »
    Top Answer:Databricks and Dataiku are excellent Data Science platforms but have different strengths and weaknesses. Below is a comparison of the two products based on several parameters Cost It is… more »
    Top Answer:Hi, I am the founder of Actable AI so my answer may be biased. In terms of performance, it's Actable AI. Why? Because we leverage the best and latest open source technologies out there (AutoGluon… more »
    Top Answer:Dataiku is my choice as it's not bulky and the learning path for people like me (noobs in ML and data science) is not steep at all, so after a couple of pieces of training I feel very confident. Also… more »
    Ranking
    5th
    Views
    11,426
    Comparisons
    9,062
    Reviews
    11
    Average Words per Review
    536
    Rating
    7.2
    11th
    Views
    9,109
    Comparisons
    7,135
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Databricks logo
    Compared 35% of the time.
    KNIME logo
    Compared 13% of the time.
    Alteryx logo
    Compared 13% of the time.
    RapidMiner logo
    Compared 9% of the time.
    SAS Visual Analytics logo
    Compared 4% of the time.
    Also Known As
    AWS SageMaker, SageMaker
    Dataiku DSS
    Learn More
    Overview

    Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

    Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

    Sample Customers
    DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
    BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
    Top Industries
    REVIEWERS
    Computer Software Company22%
    Manufacturing Company11%
    Logistics Company11%
    Transportation Company11%
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Educational Organization13%
    Computer Software Company11%
    Manufacturing Company8%
    VISITORS READING REVIEWS
    Financial Services Firm18%
    Educational Organization14%
    Manufacturing Company8%
    Computer Software Company8%
    Company Size
    REVIEWERS
    Small Business15%
    Midsize Enterprise40%
    Large Enterprise45%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise18%
    Large Enterprise68%
    REVIEWERS
    Small Business57%
    Large Enterprise43%
    VISITORS READING REVIEWS
    Small Business12%
    Midsize Enterprise19%
    Large Enterprise68%
    Buyer's Guide
    Amazon SageMaker vs. Dataiku
    May 2024
    Find out what your peers are saying about Amazon SageMaker vs. Dataiku and other solutions. Updated: May 2024.
    771,581 professionals have used our research since 2012.

    Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Dataiku is ranked 11th in Data Science Platforms with 7 reviews. Amazon SageMaker is rated 7.4, while Dataiku 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 Dataiku writes "The model is very useful". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and DataRobot, whereas Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and SAS Visual Analytics. See our Amazon SageMaker vs. Dataiku report.

    See our list of best Data Science Platforms vendors.

    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.