TrustRadius: an HG Insights company

IBM Watson Studio on Cloud Pak for Data vs. Microsoft Azure

Save this comparison

Save this comparison

Add Product

Recommended Comparisons

    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM Watson Studio

    Score10 out of 10
    N/AIBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A

    Microsoft Azure

    Score8.5 out of 10
    N/AMicrosoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.

    $29

    per month

    Pricing
    IBM Watson StudioMicrosoft Azure
    Editions & Modules
    No answers on this topic
    Developer
    $29
    per month
    Standard
    $100
    per month
    Professional Direct
    $1000
    per month
    Basic
    Free
    per month
    Offerings
    Pricing Offerings
    IBM Watson StudioMicrosoft Azure
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
    More Pricing Information
    Community Pulse
    IBM Watson StudioMicrosoft Azure
    Considered Both Products
    IBM
    Chose IBM Watson Studio
    IBM offers a deep neural network training workflow, with a flow editor interface similar to the one used in Azure ML Studio. However, the custom build modeling in IBM has notebooks such as Jupiter to program models manually using popular frameworks like TensorFlow, …
    Incentivized
    Chose IBM Watson Studio
    AWS Sagemaker is new, and I personally think it's better than sliced bread. There's very little set up to do. Watson Studio needs to up its game against Sagemaker.
    Incentivized
    Chose IBM Watson Studio
    Watson Studio offers more capabilities and diversity in tools and services.
    Incentivized
    Chose IBM Watson Studio
    DSX is a good challenger for Databricks and co. It is Enterprise ready and well integrated.
    Incentivized
    Chose IBM Watson Studio
    I wanted an environment that can support multiple users without any restrictions. Also, R-Studio does not provide a collaborative environment for multiple users. The Auto feature selection in the SPSS modeler is a good node in DSx which helps make statistical decisions on …
    Incentivized
    Chose IBM Watson Studio
    The IBM Data Science Experience enables data scientists to collaborate through projects, to which they can add notebooks, data, data connections, and other users they want to collaborate with. In Jupyter notebooks they can use Python, R, or Scala, when needed with Apache Spark, …
    Incentivized
    Chose IBM Watson Studio
    Although we also use Azure ML services we prefer DSX because of SPSS integration.
    Incentivized
    Chose IBM Watson Studio
    DSX performed almost 2.5 times faster than Microsoft's free Jupyter Notebook service on their Azure platform.
    Incentivized
    Microsoft
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    5 Answers
    92%
    Would buy again
    34 Answers
    Delivers good value for the price
    No answers on this topic
    94%
    Delivers good value for the price
    31 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    5 Answers
    95%
    Happy with the feature set
    35 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    89%
    Lived up to sales and marketing promises
    24 Answers
    Implementation went as expected
    No answers on this topic
    94%
    Implementation went as expected
    34 Answers
    Features
    IBM Watson StudioMicrosoft Azure
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    8.1
    22 Ratings
    3% below category average
    Microsoft Azure
    -
    Ratings
    Connect to Multiple Data Sources8.022 Ratings00 Ratings
    Extend Existing Data Sources8.022 Ratings00 Ratings
    Automatic Data Format Detection10.021 Ratings00 Ratings
    MDM Integration6.414 Ratings00 Ratings
    Data Exploration
    Comparison of Data Exploration features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    10.0
    22 Ratings
    17% above category average
    Microsoft Azure
    -
    Ratings
    Visualization10.022 Ratings00 Ratings
    Interactive Data Analysis10.022 Ratings00 Ratings
    Data Preparation
    Comparison of Data Preparation features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    9.5
    22 Ratings
    15% above category average
    Microsoft Azure
    -
    Ratings
    Interactive Data Cleaning and Enrichment10.022 Ratings00 Ratings
    Data Transformations10.021 Ratings00 Ratings
    Data Encryption8.020 Ratings00 Ratings
    Built-in Processors10.021 Ratings00 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    9.5
    22 Ratings
    12% above category average
    Microsoft Azure
    -
    Ratings
    Multiple Model Development Languages and Tools10.021 Ratings00 Ratings
    Automated Machine Learning10.022 Ratings00 Ratings
    Single platform for multiple model development10.022 Ratings00 Ratings
    Self-Service Model Delivery8.020 Ratings00 Ratings
    Model Deployment
    Comparison of Model Deployment features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    8.0
    22 Ratings
    6% below category average
    Microsoft Azure
    -
    Ratings
    Flexible Model Publishing Options9.022 Ratings00 Ratings
    Security, Governance, and Cost Controls7.022 Ratings00 Ratings
    Infrastructure-as-a-Service (IaaS)
    Comparison of Infrastructure-as-a-Service (IaaS) features of IBM Watson Studio on Cloud Pak for Data and Microsoft Azure
    Feature
    IBM Watson Studio on Cloud Pak for Data
    -
    Ratings
    Microsoft Azure
    8.4
    27 Ratings
    2% above category average
    Service-level Agreement (SLA) uptime00 Ratings8.126 Ratings
    Dynamic scaling00 Ratings8.725 Ratings
    Elastic load balancing00 Ratings8.624 Ratings
    Pre-configured templates00 Ratings8.225 Ratings
    Monitoring tools00 Ratings8.326 Ratings
    Pre-defined machine images00 Ratings8.424 Ratings
    Operating system support00 Ratings8.926 Ratings
    Security controls00 Ratings8.626 Ratings
    Automation00 Ratings8.224 Ratings
    Best Alternatives
    IBM Watson StudioMicrosoft Azure
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    IBM Cloud Object Storage
    Score9 out of 10
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    IBM Cloud Bare Metal Servers
    Score8.7 out of 10
    Enterprises
    Posit
    Score10 out of 10
    SAP on IBM Cloud
    Score9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM Watson StudioMicrosoft Azure
    Likelihood to Recommend
    8.0
    (65 ratings)
    8.7
    (96 ratings)
    Likelihood to Renew
    8.2
    (1 ratings)
    10.0
    (17 ratings)
    Usability
    9.6
    (2 ratings)
    8.3
    (36 ratings)
    Availability
    8.2
    (1 ratings)
    6.8
    (2 ratings)
    Performance
    8.2
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    8.2
    (1 ratings)
    9.0
    (27 ratings)
    In-Person Training
    8.2
    (1 ratings)
    -
    (0 ratings)
    Online Training
    8.2
    (1 ratings)
    -
    (0 ratings)
    Implementation Rating
    7.3
    (1 ratings)
    8.0
    (2 ratings)
    Product Scalability
    8.2
    (1 ratings)
    -
    (0 ratings)
    Vendor post-sale
    7.3
    (1 ratings)
    -
    (0 ratings)
    Vendor pre-sale
    8.2
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    IBM Watson StudioMicrosoft Azure
    Likelihood to Recommend
    IBM
    It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
    Incentivized
    Read full review
    Microsoft
    Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.
    Read full review
    Pros
    IBM
    • Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
    • SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
    • Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
    • Estimator validation lets data scientists test and prove different models.
    Incentivized
    Read full review
    Microsoft
    • Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
    • You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
    • The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
    • The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.
    Incentivized
    Read full review
    Cons
    IBM
    • The cost is steep and so only companies with resources can afford it
    • It will be nice to have Chinese versions so that Chinese engineers can also use it easily
    • It takes a while to learn how to input different kinds of skin defects for detection
    Incentivized
    Read full review
    Microsoft
    • The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
    • The documentation needs to be improved, and some advanced configuration options require research and experimentation.
    • Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.
    Read full review
    Likelihood to Renew
    IBM
    because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
    Incentivized
    Read full review
    Microsoft
    Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.
    Read full review
    Usability
    IBM
    The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
    Incentivized
    Read full review
    Microsoft
    As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.
    Incentivized
    Read full review
    Reliability and Availability
    IBM
    From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
    Incentivized
    Read full review
    Microsoft
    It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators
    Incentivized
    Read full review
    Performance
    IBM
    Never had slow response even on our very busy network
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Support Rating
    IBM
    I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
    Incentivized
    Read full review
    Microsoft
    We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!
    Incentivized
    Read full review
    In-Person Training
    IBM
    The trainers on the job are very smart with solutions and very able in teaching
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Online Training
    IBM
    The Platform is very handy and suggests further steps according my previous interests
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Implementation Rating
    IBM
    It surprised us with unpredictable case of use and brand new points of view
    Incentivized
    Read full review
    Microsoft
    As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.
    Read full review
    Alternatives Considered
    IBM
    The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
    Incentivized
    Read full review
    Microsoft
    As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.
    Incentivized
    Read full review
    Scalability
    IBM
    It helped us in getting from 0 to DSX without getting lost
    Incentivized
    Read full review
    Microsoft
    No answers on this topic
    Return on Investment
    IBM
    • Could instantly show data driven insights to drive 20% incremental revenue over existing results
    • Still don't have a real use case for unstructured data like twitter feed
    • Some of the insights around user actions have driven new projects to automate mundane tasks
    Incentivized
    Read full review
    Microsoft
    • For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
    • DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
    • Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.
    Incentivized
    Read full review
    ScreenShots