Alternatives to Cloud Foundry logo

Alternatives to Cloud Foundry

Red Hat OpenShift, Docker, Kubernetes, OpenStack, and Terraform are the most popular alternatives and competitors to Cloud Foundry.
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What is Cloud Foundry and what are its top alternatives?

Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications.
Cloud Foundry is a tool in the Platform as a Service category of a tech stack.
Cloud Foundry is an open source tool with 586 GitHub stars and 505 GitHub forks. Here’s a link to Cloud Foundry's open source repository on GitHub

Top Alternatives to Cloud Foundry

  • Red Hat OpenShift

    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. ...

  • Docker

    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • OpenStack

    OpenStack

    OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...

  • Terraform

    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

  • Heroku

    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 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

    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. ...

Cloud Foundry alternatives & related posts

Red Hat OpenShift logo

Red Hat OpenShift

1.2K
1.2K
480
Red Hat's free Platform as a Service (PaaS) for hosting Java, PHP, Ruby, Python, Node.js, and Perl apps
1.2K
1.2K
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480
PROS OF RED HAT OPENSHIFT
  • 97
    Good free plan
  • 61
    Open Source
  • 45
    Easy setup
  • 41
    Nodejs support
  • 39
    Well documented
  • 31
    Custom domains
  • 27
    Mongodb support
  • 26
    Clean and simple architecture
  • 24
    PHP support
  • 20
    Customizable environments
  • 10
    Ability to run CRON jobs
  • 8
    Easier than Heroku for a WordPress blog
  • 6
    PostgreSQL support
  • 6
    Autoscaling
  • 6
    Easy deployment
  • 6
    Good balance between Heroku and AWS for flexibility
  • 5
    Free, Easy Setup, Lot of Gear or D.I.Y Gear
  • 4
    Shell access to gears
  • 3
    Great Support
  • 2
    Overly complicated and over engineered in majority of e
  • 2
    Golang support
  • 2
    Its free and offer custom domain usage
  • 1
    Meteor support
  • 1
    Easy setup and great customer support
  • 1
    High Security
  • 1
    No credit card needed
  • 1
    because it is easy to manage
  • 1
    Logging & Metrics
  • 1
    Autoscaling at a good price point
  • 1
    Great free plan with excellent support
  • 1
    This is the only free one among the three as of today
CONS OF RED HAT OPENSHIFT
  • 2
    Decisions are made for you, limiting your options
  • 2
    License cost
  • 1
    Behind, sometimes severely, the upstreams

related Red Hat OpenShift posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Michael Ionita

We use Kubernetes because we decided to migrate to a hosted cluster (not AWS) and still be able to scale our clusters up and down depending on load. By wrapping it with OpenShift we are now able to easily adapt to demand but also able to separate concerns into separate Pods depending on use-cases we have.

See more
Docker logo

Docker

115.8K
92.2K
3.8K
Enterprise Container Platform for High-Velocity Innovation.
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92.2K
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PROS OF DOCKER
  • 821
    Rapid integration and build up
  • 688
    Isolation
  • 517
    Open source
  • 505
    Testa­bil­i­ty and re­pro­ducibil­i­ty
  • 459
    Lightweight
  • 217
    Standardization
  • 182
    Scalable
  • 105
    Upgrading / down­grad­ing / ap­pli­ca­tion versions
  • 86
    Security
  • 84
    Private paas environments
  • 33
    Portability
  • 25
    Limit resource usage
  • 15
    I love the way docker has changed virtualization
  • 15
    Game changer
  • 12
    Fast
  • 11
    Concurrency
  • 7
    Docker's Compose tools
  • 4
    Fast and Portable
  • 4
    Easy setup
  • 4
    Because its fun
  • 3
    Makes shipping to production very simple
  • 2
    It's dope
  • 1
    Highly useful
  • 1
    MacOS support FAKE
  • 1
    Its cool
  • 1
    Docker hub for the FTW
  • 1
    Very easy to setup integrate and build
  • 1
    Package the environment with the application
  • 1
    Does a nice job hogging memory
  • 1
    Open source and highly configurable
  • 1
    Simplicity, isolation, resource effective
CONS OF DOCKER
  • 7
    New versions == broken features
  • 5
    Documentation not always in sync
  • 5
    Unreliable networking
  • 3
    Moves quickly
  • 2
    Not Secure

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.3M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
See more
Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 4.7M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Kubernetes logo

Kubernetes

39.2K
33.3K
628
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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PROS OF KUBERNETES
  • 159
    Leading docker container management solution
  • 124
    Simple and powerful
  • 101
    Open source
  • 75
    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
  • 18
    Replication controller
  • 9
    Permission managment
  • 7
    Simple
  • 7
    Supports autoscaling
  • 6
    Cheap
  • 4
    Self-healing
  • 4
    Reliable
  • 4
    No cloud platform lock-in
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Quick cloud setup
  • 3
    Promotes modern/good infrascture practice
  • 2
    Backed by Red Hat
  • 2
    Runs on azure
  • 2
    Cloud Agnostic
  • 2
    Custom and extensibility
  • 2
    Captain of Container Ship
  • 2
    A self healing environment with rich metadata
  • 1
    Golang
  • 1
    Easy setup
  • 1
    Everything of CaaS
  • 1
    Sfg
  • 1
    Expandable
  • 1
    Gke
CONS OF KUBERNETES
  • 13
    Poor workflow for development
  • 11
    Steep learning curve
  • 5
    Orchestrates only infrastructure
  • 2
    High resource requirements for on-prem clusters

related Kubernetes posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

See more
OpenStack logo

OpenStack

639
934
110
Open source software for building private and public clouds
639
934
+ 1
110
PROS OF OPENSTACK
  • 45
    Private cloud
  • 36
    Avoid vendor lock-in
  • 19
    Flexible in use
  • 5
    Industry leader
  • 3
    Supported by many companies in top500
  • 2
    Robust architecture
CONS OF OPENSTACK
    Be the first to leave a con

    related OpenStack posts

    Terraform logo

    Terraform

    11.4K
    8.4K
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    Describe your complete infrastructure as code and build resources across providers
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    PROS OF TERRAFORM
    • 109
      Infrastructure as code
    • 72
      Declarative syntax
    • 44
      Planning
    • 27
      Simple
    • 24
      Parallelism
    • 7
      Cloud agnostic
    • 6
      Well-documented
    • 6
      It's like coding your infrastructure in simple English
    • 4
      Automates infrastructure deployments
    • 4
      Immutable infrastructure
    • 4
      Platform agnostic
    • 3
      Extendable
    • 3
      Automation
    • 3
      Portability
    • 2
      Lightweight
    • 2
      Scales to hundreds of hosts
    CONS OF TERRAFORM
    • 1
      Doesn't have full support to GKE

    related Terraform posts

    Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

    Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

    Check Out My Architecture: CLICK ME

    Check out the GitHub repo attached

    See more
    Emanuel Evans
    Senior Architect at Rainforest QA · | 16 upvotes · 705.8K views

    We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

    We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

    Read the blog post to go more in depth.

    See more
    Heroku logo

    Heroku

    20.1K
    15.7K
    3.2K
    Build, deliver, monitor and scale web apps and APIs with a trail blazing developer experience.
    20.1K
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    PROS OF HEROKU
    • 704
      Easy deployment
    • 460
      Free for side projects
    • 374
      Huge time-saver
    • 348
      Simple scaling
    • 261
      Low devops skills required
    • 190
      Easy setup
    • 174
      Add-ons for almost everything
    • 154
      Beginner friendly
    • 150
      Better for startups
    • 133
      Low learning curve
    • 48
      Postgres hosting
    • 41
      Easy to add collaborators
    • 30
      Faster development
    • 24
      Awesome documentation
    • 19
      Focus on product, not deployment
    • 19
      Simple rollback
    • 15
      Natural companion for rails development
    • 15
      Easy integration
    • 12
      Great customer support
    • 8
      GitHub integration
    • 6
      No-ops
    • 6
      Painless & well documented
    • 4
      Free
    • 4
      I love that they make it free to launch a side project
    • 3
      Just works
    • 3
      Great UI
    • 2
      PostgreSQL forking and following
    • 2
      MySQL extension
    • 1
      Able to host stuff good like Discord Bot
    • 0
      Sec
    • 0
      Security
    CONS OF HEROKU
    • 23
      Super expensive
    • 6
      Not a whole lot of flexibility
    • 5
      No usable MySQL option
    • 5
      Storage
    • 4
      Low performance on free tier
    • 1
      24/7 support is $1,000 per month

    related Heroku posts

    Russel Werner
    Lead Engineer at StackShare · | 30 upvotes · 1.5M views

    StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

    Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

    #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

    See more
    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.3M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Google App Engine logo

    Google App Engine

    7.4K
    5.7K
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    Build web applications on the same scalable systems that power Google applications
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    PROS OF GOOGLE APP ENGINE
    • 144
      Easy to deploy
    • 108
      Auto scaling
    • 80
      Good free plan
    • 64
      Easy management
    • 58
      Scalability
    • 35
      Low cost
    • 33
      Comprehensive set of features
    • 29
      All services in one place
    • 23
      Simple scaling
    • 20
      Quick and reliable cloud servers
    • 5
      Granular Billing
    • 4
      Easy to develop and unit test
    • 3
      Monitoring gives comprehensive set of key indicators
    • 2
      Create APIs quickly with cloud endpoints
    • 2
      Really easy to quickly bring up a full stack
    • 1
      Mostly up
    • 1
      No Ops
    CONS OF GOOGLE APP ENGINE
      Be the first to leave a con

      related Google App Engine posts

      Nick Rockwell
      SVP, Engineering at Fastly · | 11 upvotes · 284.6K views

      So, the shift from Amazon EC2 to Google App Engine and generally #AWS to #GCP was a long decision and in the end, it's one that we've taken with eyes open and that we reserve the right to modify at any time. And to be clear, we continue to do a lot of stuff with AWS. But, by default, the content of the decision was, for our consumer-facing products, we're going to use GCP first. And if there's some reason why we don't think that's going to work out great, then we'll happily use AWS. In practice, that hasn't really happened. We've been able to meet almost 100% of our needs in GCP.

      So it's basically mostly Google Kubernetes Engine , we're mostly running stuff on Kubernetes right now.

      #AWStoGCPmigration #cloudmigration #migration

      See more
      Aliadoc Team

      In #Aliadoc, we're exploring the crowdfunding option to get traction before launch. We are building a SaaS platform for website design customization.

      For the Admin UI and website editor we use React and we're currently transitioning from a Create React App setup to a custom one because our needs have become more specific. We use CloudFlare as much as possible, it's a great service.

      For routing dynamic resources and proxy tasks to feed websites to the editor we leverage CloudFlare Workers for improved responsiveness. We use Firebase for our hosting needs and user authentication while also using several Cloud Functions for Firebase to interact with other services along with Google App Engine and Google Cloud Storage, but also the Real Time Database is on the radar for collaborative website editing.

      We generally hate configuration but honestly because of the stage of our project we lack resources for doing heavy sysops work. So we are basically just relying on Serverless technologies as much as we can to do all server side processing.

      Visual Studio Code definitively makes programming a much easier and enjoyable task, we just love it. We combine it with Bitbucket for our source code control needs.

      See more
      AWS Elastic Beanstalk logo

      AWS Elastic Beanstalk

      2K
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      Quickly deploy and manage applications in the AWS cloud.
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      + 1
      240
      PROS OF AWS ELASTIC BEANSTALK
      • 77
        Integrates with other aws services
      • 65
        Simple deployment
      • 44
        Fast
      • 28
        Painless
      • 16
        Free
      • 3
        Independend app container
      • 3
        Well-documented
      • 2
        Ability to be customized
      • 2
        Postgres hosting
      CONS OF AWS ELASTIC BEANSTALK
      • 2
        Charges appear automatically after exceeding free quota
      • 1
        Lots of moving parts and config
      • 0
        Slow deployments

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      Julien DeFrance
      Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

      See more

      We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

      We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

      In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

      Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

      See more