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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    H2O.ai

    Score6.4 out of 10
    N/AAn open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.N/A

    IBM SPSS Statistics

    Score8 out of 10
    N/ASPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).

    $105

    per month per user

    Pricing
    H2O.aiIBM SPSS Statistics
    Editions & Modules
    No answers on this topic
    Base
    USD 3,830
    one-time fee per user
    Standard
    USD 8,440
    one-time fee per user
    Professional
    USD 16,900
    one-time fee per user
    Premium
    USD 25,200
    one-time fee per user
    Monthly subscription
    USD 105
    per month per user
    Annual subscription
    USD 1,188.00
    per year per user
    Offerings
    Pricing Offerings
    H2O.aiIBM SPSS Statistics
    Free Trial
    NoYes
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    H2O.aiIBM SPSS Statistics
    Considered Both Products
    H2O.ai
    No answer on this topic
    IBM
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    94%
    Would buy again
    49 Answers
    Delivers good value for the price
    No answers on this topic
    91%
    Delivers good value for the price
    42 Answers
    Happy with the feature set
    No answers on this topic
    98%
    Happy with the feature set
    51 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    97%
    Lived up to sales and marketing promises
    34 Answers
    Implementation went as expected
    No answers on this topic
    97%
    Implementation went as expected
    35 Answers
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    H2O.aiIBM SPSS Statistics
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    User Ratings
    H2O.aiIBM SPSS Statistics
    Likelihood to Recommend
    8.1
    (3 ratings)
    8.4
    (116 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    8.5
    (23 ratings)
    Usability
    -
    (0 ratings)
    8.0
    (15 ratings)
    Availability
    -
    (0 ratings)
    6.0
    (1 ratings)
    Performance
    -
    (0 ratings)
    6.0
    (1 ratings)
    Support Rating
    9.0
    (1 ratings)
    6.4
    (12 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.7
    (7 ratings)
    Configurability
    -
    (0 ratings)
    5.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    5.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    5.0
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    5.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    5.0
    (1 ratings)
    User Testimonials
    H2O.aiIBM SPSS Statistics
    Likelihood to Recommend
    H2O.ai
    Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
    Incentivized
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    IBM
    IBM SPSS Statistics is well suited for pretty much any data analytic scenario. It can handle extremely complex and large-scale datasets with ease. It especially shines if you have to do any kind of analyses that involve significance testing. Being able to do any number of significance tests (i.e., t-tests, chi-square, ANOVAs, etc.) right inside the tool is very valuable. The only scenario I would say it is less appropriate is if you need to work on a very small dataset and answer very simple questions, like frequencies or averages. In those cases, something like Excel could probably do the job just as easily.
    Incentivized
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    Pros
    H2O.ai
    • Excellent analytical and prediction tool
    • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
    • Readily available algorithms, easy to use in your analytical projects
    • Faster than Python scikit learn (in machine learning supervised learning area)
    • It can be accessed (run) from Python, not only JAVA etc.
    • Well documented and suitable for fast training or self studying
    • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
    • It can be used as open source
    Incentivized
    Read full review
    IBM
    • SPSS has been around for quite a while and has amassed a large suite of functionality. One of its longest-running features is the ability to automate SPSS via scripting, AKA "syntax." There is a very large community of practice on the internet who can help newbies to quickly scale up their automation abilities with SPSS. And SPSS allows users to save syntax scripting directly from GUI wizards and configuration windows, which can be a real life-saver if one is not an experienced coder.
    • Many statistics package users are doing scientific research with an eye to publish reproducible results. SPSS allows you to save datasets and syntax scripting in a common format, facilitating attempts by peer reviewers and other researchers to quickly and easily attempt to reproduce your results. It's very portable!
    • SPSS has both legacy and modern visualization suites baked into the base software, giving users an easily mountable learning curve when it comes to outputting charts and graphs. It's very easy to start with a canned look and feel of an exported chart, and then you can tweak a saved copy to change just about everything, from colors, legends, and axis scaling, to orientation, labels, and grid lines. And when you've got a chart or graph set up the way you like, you can export it as an image file, or create a template syntax to apply to new visualizations going forward.
    • SPSS makes it easy for even beginner-level users to create statistical coding fields to support multidimensional analysis, ensuring that you never need to destructively modify your dataset.
    • In closing, SPSS's long and successful tenure ensures that just about any question a new user may have about it can be answered with a modicum of Google-fu. There are even several fully-fledged tutorial websites out there for newbie perusal.
    Incentivized
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    Cons
    H2O.ai
    • Better documentation
    • Improve the Visual presentations including charting etc
    Incentivized
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    IBM
    • Cost is becoming prohibitive.
    • Availability of procedures in the base package seems to be dwindling.
    • The copy-and-paste function from output to Excel is not as easy as it once was (now I have to do a "paste special").
    • Text and date handling are terrible.
    • Need to include AI-based NLP for survey verbatims and other text-based fields. This is becoming increasingly important in the CX world, yet SPSS seems to be ignoring it.
    Incentivized
    Read full review
    Likelihood to Renew
    H2O.ai
    No answers on this topic
    IBM
    Both
    money and time are essential for success in terms of return on investment for any kind of research based project work. Using a Likert-scale questionnaire is very easy for data entry and analysis
    using IBM SPSS. With the help of IBM SPSS, I found very fast and reliable data
    entry and data analysis for my research. Output from SPSS is very easy to
    interpret for data analysis and findings
    Incentivized
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    Usability
    H2O.ai
    No answers on this topic
    IBM
    Probably because I have been using it for so long that I have used all of the modules, or at least almost all of the modules, and the way SPSS works is second nature to me, like fish to swimming.
    Incentivized
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    Reliability and Availability
    H2O.ai
    No answers on this topic
    IBM
    SPSS can tend to crash when I am trying to do a lot of data. This can slow me down when I need to do a lot of data
    Incentivized
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    Performance
    H2O.ai
    No answers on this topic
    IBM
    SPSS does the job, but it can be slow. I do have to plan a lot of time to get through a huge amount of data.
    Incentivized
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    Support Rating
    H2O.ai
    The overall experience I have with H2O is really awesome, even with its cost effectiveness.
    Incentivized
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    IBM
    I have not contacted IBM SPSS for support myself. However, our IT staff has for trying to get SPSS Text Analytics Module to work. The issue was never resolved, but I'm not sure if it was on the IT's end or on SPSS's end
    Read full review
    Implementation Rating
    H2O.ai
    No answers on this topic
    IBM
    Have a plan for managing the yearly upgrade cycle. Most users work in the desktop version, so there needs to be a mechanism for either pushing out new versions of the software or a key manager to deal with updated licensing keys. If you have a lot of users this needs to be planned for in advance.
    Incentivized
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    Alternatives Considered
    H2O.ai
    Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
    Incentivized
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    IBM
    If you have made it this far, you should have a very good idea of how SPSS stacks up the competition (data processing and analytics tools). Even the free ones, such as r Studio or Stata, are leaps and bounds ahead of SPSS. IBM is resting on a reputation developed nearly 30 years ago and has shown no desire to improve.
    Incentivized
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    Scalability
    H2O.ai
    No answers on this topic
    IBM
    I am neutral because I have not had to look into scalability since I am using as a student.
    Incentivized
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    Return on Investment
    H2O.ai
    • Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
    • Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
    • Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
    Incentivized
    Read full review
    IBM
    • I found SPSS easier to use than SAS as it's more intuitive to me.
    • The learning curve to use SPSS is less compared to SAS.
    • I used SAS, to a much lesser extent than SPSS. However, it seems that SAS may be more suitable for users who understand programming. With SPSS, users can perform many statistical tests without the need to know programming.
    Incentivized
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    ScreenShots

    IBM SPSS Statistics Screenshots

    Screenshot of SPSS Statistics Forecasting. This enables users to build time-series forecasts regardless of their skill level.Screenshot of SPSS Statistics Regression. These predict categorical outcomes and apply nonlinear regression procedures.Screenshot of IBM SPSS Statistics Neural Networks. These can discover complex relationships and improve predictive models.Screenshot of IBM SPSS Statistics Curated Help. These can interpret correlation output.Screenshot of IBM SPSS Statistics AI Output Assistant interprets statistical output in easy to consume language