Software Alternatives, Accelerators & Startups

MongoDB Stitch VS Datatron

Compare MongoDB Stitch VS Datatron and see what are their differences

MongoDB Stitch logo MongoDB Stitch

The serverless platform from MongoDB

Datatron logo Datatron

Datatron automates the deployment, monitoring, governance, and validation of your machine learning models in scikit-learn, TensorFlow, Keras, Pytorch, R, H20 and SAS
  • MongoDB Stitch Landing page
    Landing page //
    2023-09-05

Get the full power and scalability of MongoDB – whether using your existing database or starting from scratch. Retain direct access to the database; safely expose existing data to new applications.

  • Datatron Landing page
    Landing page //
    2023-02-11

MongoDB Stitch videos

MongoDB Stitch, The Serverless Platform On Steroids! - Maxime Beugnet

More videos:

  • Review - Create a Data API in 10 Minutes with MongoDB Stitch
  • Review - Build a Slack App in 10 Minutes with MongoDB Stitch

Datatron videos

Harish Doddi demos Datatron @SFNewTech on 1 Mar 2017 #SFNT @getdatatron

More videos:

  • Review - Virtual Records Management from Datatron

Category Popularity

0-100% (relative to MongoDB Stitch and Datatron)
Developer Tools
100 100%
0% 0
Data Science And Machine Learning
App Development
100 100%
0% 0
Data Science Notebooks
0 0%
100% 100

User comments

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What are some alternatives?

When comparing MongoDB Stitch and Datatron, you can also consider the following products

Parse - Build applications faster with object and file storage, user authentication, push notifications, dashboard and more out of the box.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

AWS Amplify - JavaScript library for app development using cloud services

MCenter - Machine Learning Operationalization

AWS Mobile Services - Storage, Analytics, Push Notif. & SMS Delivery SDKs

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.