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AppFog vs PythonAnywhere

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AppFog
7
24
+ 1
14
PythonAnywhere
93
291
+ 1
64
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AppFog vs PythonAnywhere: What are the differences?

Developers describe AppFog as "Simple PaaS for Java, Python, Node, .Net, Ruby, PHP, MySQL, Mongo, and PostgreSQL". AppFog provides the infrastructure web developers need to build apps without worrying about IT tasks or having to wait days to get servers ready for writing code. AppFog’s web application technologies include PHP, NodeJS, Ruby, Python, Java, .NET, MySQL, and PostgreSQL. On the other hand, PythonAnywhere is detailed as "Micro PaaS for Python web apps. Develop and host Python from your browser". It's somewhat unique. A small PaaS that supports web apps (Python only) as well as scheduled jobs with shell access. It is an expensive way to tinker and run several small apps.

AppFog and PythonAnywhere can be primarily classified as "Platform as a Service" tools.

"The basic plan is free" is the primary reason why developers consider AppFog over the competitors, whereas "Web apps" was stated as the key factor in picking PythonAnywhere.

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Pros of AppFog
Pros of PythonAnywhere
  • 4
    Allocate multiple instances to one app for free
  • 4
    The basic plan is free
  • 3
    Pricing by memory size
  • 2
    Great for startups
  • 1
    10 Free instances
  • 15
    Web apps
  • 11
    Easy Setup
  • 8
    Great support
  • 8
    Shell access
  • 8
    Free plan
  • 7
    Super-easy to use
  • 5
    Libraries
  • 2
    Many things like Python are pre-installed

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Cons of AppFog
Cons of PythonAnywhere
    Be the first to leave a con
    • 1
      No root access
    • 1
      Really small community

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    What is AppFog?

    AppFog provides the infrastructure web developers need to build apps without worrying about IT tasks or having to wait days to get servers ready for writing code. AppFog’s web application technologies include PHP, NodeJS, Ruby, Python, Java, .NET, MySQL, and PostgreSQL.

    What is PythonAnywhere?

    It's somewhat unique. A small PaaS that supports web apps (Python only) as well as scheduled jobs with shell access. It is an expensive way to tinker and run several small apps.

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

    Jobs that mention AppFog and PythonAnywhere as a desired skillset
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    Staff iOS Software Engineer, Advanced Technologies Group
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    Staff Software Engineer, ML Training
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    PatternsInfraDream+12
    Software Engineer II (Backend), Data Encryption
    Postman
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    highlight.ioPatternsSchedule+5
    Senior Platform Engineer, (Distribution)
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    What companies use AppFog?
    What companies use PythonAnywhere?
    See which teams inside your own company are using AppFog or PythonAnywhere.
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    What tools integrate with AppFog?
    What tools integrate with PythonAnywhere?

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    What are some alternatives to AppFog and PythonAnywhere?
    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.
    Red Hat OpenShift
    OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.
    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.
    Apollo
    Build a universal GraphQL API on top of your existing REST APIs, so you can ship new application features fast without waiting on backend changes.
    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

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