Need advice about which tool to choose?Ask the StackShare community!

Domino

16
23
+ 1
0
jFrog

94
71
+ 1
0
Add tool

Domino vs jFrog: What are the differences?

Domino: A PaaS for data science - easily run R, Python or Matlab code in the cloud with automatic version control for data, code, and results. Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall; jFrog: Universal Artifact Management. Host, manage and proxy artifacts using the best Docker Registry, Maven Repository, Gradle repository, NuGet repository, Ruby repository, Debian repository npm repository, Yum repository.

Domino and jFrog can be primarily classified as "Platform as a Service" tools.

Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More

What is Domino?

Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall.

What is jFrog?

Host, manage and proxy artifacts using the best Docker Registry, Maven Repository, Gradle repository, NuGet repository, Ruby repository, Debian repository npm repository, Yum repository.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Domino?
What companies use jFrog?
See which teams inside your own company are using Domino or jFrog.
Sign up for Private StackShareLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Domino?
What tools integrate with jFrog?
What are some alternatives to Domino and jFrog?
Biscuit
Biscuit is a simple key-value store for your infrastructure secrets. Biscuit is most useful to teams already using AWS and IAM to manage their infrastructure.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Heroku
Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling.
Google App Engine
Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
AWS Elastic Beanstalk
Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
See all alternatives