Firebase vs. IBM Watson Studio on Cloud Pak for Data

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Firebase
Score 8.6 out of 10
N/A
Google offers the Firebase suite of application development tools, available free or at cost for higher degree of usages, priced flexibly accorded to features needed. The suite includes A/B testing and Crashlytics, Cloud Messaging (FCM) and in-app messaging, cloud storage and NoSQL storage (Cloud Firestore and Firestore Realtime Database), and other features supporting developers with flexible mobile application development.
$0.01
Per Verification
IBM Watson Studio
Score 8.8 out of 10
N/A
IBM 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
Pricing
FirebaseIBM Watson Studio on Cloud Pak for Data
Editions & Modules
Phone Authentication
$0.01
Per Verification
Stored Data
$0.18
Per GiB
No answers on this topic
Offerings
Pricing Offerings
FirebaseIBM Watson Studio
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
FirebaseIBM Watson Studio on Cloud Pak for Data
Top Pros
Top Cons
Features
FirebaseIBM Watson Studio on Cloud Pak for Data
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Firebase
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.1
23 Ratings
Connect to Multiple Data Sources00 Ratings8.123 Ratings
Extend Existing Data Sources00 Ratings8.323 Ratings
Automatic Data Format Detection00 Ratings8.222 Ratings
MDM Integration00 Ratings7.715 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Firebase
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.7
23 Ratings
Visualization00 Ratings9.023 Ratings
Interactive Data Analysis00 Ratings8.523 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Firebase
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.4
23 Ratings
Interactive Data Cleaning and Enrichment00 Ratings8.623 Ratings
Data Transformations00 Ratings8.422 Ratings
Data Encryption00 Ratings7.721 Ratings
Built-in Processors00 Ratings8.822 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Firebase
-
Ratings
IBM Watson Studio on Cloud Pak for Data
8.6
23 Ratings
Multiple Model Development Languages and Tools00 Ratings8.722 Ratings
Automated Machine Learning00 Ratings8.923 Ratings
Single platform for multiple model development00 Ratings8.723 Ratings
Self-Service Model Delivery00 Ratings8.021 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Firebase
-
Ratings
IBM Watson Studio on Cloud Pak for Data
7.5
23 Ratings
Flexible Model Publishing Options00 Ratings7.923 Ratings
Security, Governance, and Cost Controls00 Ratings7.223 Ratings
User Ratings
FirebaseIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
8.7
(27 ratings)
8.1
(66 ratings)
Likelihood to Renew
-
(0 ratings)
8.2
(1 ratings)
Usability
9.5
(2 ratings)
9.6
(2 ratings)
Availability
-
(0 ratings)
8.2
(2 ratings)
Performance
-
(0 ratings)
8.2
(2 ratings)
Support Rating
7.3
(12 ratings)
8.2
(2 ratings)
In-Person Training
-
(0 ratings)
8.2
(1 ratings)
Online Training
-
(0 ratings)
8.2
(1 ratings)
Implementation Rating
-
(0 ratings)
7.3
(2 ratings)
Product Scalability
-
(0 ratings)
8.2
(1 ratings)
Vendor post-sale
-
(0 ratings)
7.3
(1 ratings)
Vendor pre-sale
-
(0 ratings)
8.2
(1 ratings)
User Testimonials
FirebaseIBM Watson Studio on Cloud Pak for Data
Likelihood to Recommend
Google
Firebase should be your first choice if your platform is mobile first. Firebase's mobile platform support for client-side applications is second to none, and I cannot think of a comparable cross-platform toolkit. Firebase also integrates well with your server-side solution, meaning that you can plug Firebase into your existing app architecture with minimal effort.
Firebase lags behind on the desktop, however. Although macOS support is rapidly catching up, full Windows support is a glaring omission for most Firebase features. This means that if your platform targets Windows, you will need to implement the client functionality manually using Firebase's web APIs and wrappers, or look for another solution.
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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.
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Pros
Google
  • Analytics wise, retention is extremely important to our app, therefore we take advantage of the cohort analysis to see the impact of our middle funnel (retargeting, push, email) efforts affect the percent of users that come back into the app. Firebase allows us to easily segment these this data and look at a running average based on certain dates.
  • When it comes to any mobile app, a deep linking strategy is essential to any apps success. With Firebase's Dynamic Links, we are able to share dynamic links (recognize user device) that are able to redirect to in-app content. These deep links allow users to share other deep-linked content with friends, that also have link preview assets.
  • Firebase allows users to effectively track events, funnels, and MAUs. With this simple event tracking feature, users can put organize these events into funnels of their main user flows (e.g., checkout flows, onboarding flows, etc.), and subsequently be able to understand where the drop-off is in the funnel and then prioritize areas of the funnel to fix. Also, MAU is important to be able to tell if you are bringing in new users and what's the active volume for each platform (Android, iOS).
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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.
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Cons
Google
  • Attribution and specifically multi-touch attribution could be more robust such as Branch or Appsflyer but understand this isn't Firebases bread and butter.
  • More parameters. Firebase allows you to track tons of events (believe it's up to 50 or so) but the parameters of the events it only allows you to track 5 which is so messily and unbelievable. So you're able to get good high-level data but if you want to get granular with the events and actions are taken on your app to get real data insight you either have to go with a paid data analytics platform or bring on someone that's an expert in SQL to go through Big Query.
  • City-specific data instead of just country-specific data would have been a huge plus as well.
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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
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Likelihood to Renew
Google
No answers on this topic
IBM
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
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Usability
Google
It is simple to use overall, the console's main menu is divided into Develop, Quality, Analytics and Grow - which have further subdivisions by their set of features and tools. Develop and Quality are relevant for product and tech. Analytics is relevant for product, analytics and Grow is relevant for marketing. This makes the overall use very easy.
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IBM
The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
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Reliability and Availability
Google
No answers on this topic
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
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Performance
Google
No answers on this topic
IBM
Never had slow response even on our very busy network
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Support Rating
Google
Our analytics folks handled the majority of the communication when it came to customer service, but as far as I was aware, the support we got was pretty good. When we had an issue, we were able to reach out and get support in a timely fashion. Firebase was easy to reach and reasonably available to assist when needed.
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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
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In-Person Training
Google
No answers on this topic
IBM
The trainers on the job are very smart with solutions and very able in teaching
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Online Training
Google
No answers on this topic
IBM
The Platform is very handy and suggests further steps according my previous interests
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Implementation Rating
Google
No answers on this topic
IBM
It surprised us with unpredictable case of use and brand new points of view
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Alternatives Considered
Google
Before using Firebase, we exclusively used self hosted database services. Using Firebase has allowed us to reduce reliance on single points of failure and systems that are difficult to scale. Additionally, Firebase is much easier to set up and use than any sort of self hosted database. This simplicity has allowed us to try features that we might not have based on the amount of work they required in the past.
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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.
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Scalability
Google
No answers on this topic
IBM
It helped us in getting from 0 to DSX without getting lost
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Return on Investment
Google
  • Makes building real-time interfaces easy to do at scale with no backend involvement.
  • Very low pricing for small companies and green-fields projects.
  • Lack of support for more complicated queries needs to be managed by users and often forces strange architecture choices for data to enable it to be easily accessed.
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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
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