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Google Compute Engine vs Heroku: What are the differences?

Developers describe Google Compute Engine as "Run large-scale workloads on virtual machines hosted on Google's infrastructure". Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. Google Compute Engine offers scale, performance, and value that allows you to easily launch large compute clusters on Google's infrastructure. There are no upfront investments and you can run up to thousands of virtual CPUs on a system that has been designed from the ground up to be fast, and to offer strong consistency of performance. On the other hand, Heroku is detailed as "Build, deliver, monitor and scale web apps and APIs with a trail blazing developer experience". 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 Compute Engine and Heroku are primarily classified as "Cloud Hosting" and "Platform as a Service" tools respectively.

Some of the features offered by Google Compute Engine are:

  • High-performance virtual machines- Compute Engine’s Linux VMs are consistently performant, scalable, highly secure and reliable. Supported distros include Debian and CentOS. You can choose from micro-VMs to large instances.
  • Powered by Google’s global network- Create large compute clusters that benefit from strong and consistent cross-machine bandwidth. Connect to machines in other data centers and to other Google services using Google’s private global fiber network.
  • (Really) Pay for what you use- Google bills in minute-level increments (with a 10-minute minimum charge), so you don’t pay for unused computing time.

On the other hand, Heroku provides the following key features:

  • Agile deployment for Ruby, Node.js, Clojure, Java, Python, Go and Scala.
  • Run and scale any type of app.
  • Total visibility across your entire app.

"Backed by google", "Easy to scale" and "High-performance virtual machines" are the key factors why developers consider Google Compute Engine; whereas "Easy deployment", "Free for side projects" and "Huge time-saver" are the primary reasons why Heroku is favored.

StackShare, Heroku, and SendGrid are some of the popular companies that use Heroku, whereas Google Compute Engine is used by Snapchat, Harvest, and imgix. Heroku has a broader approval, being mentioned in 1496 company stacks & 937 developers stacks; compared to Google Compute Engine, which is listed in 587 company stacks and 414 developer stacks.

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What is Google Compute Engine?

Google Compute Engine is a service that provides virtual machines that run on Google infrastructure. Google Compute Engine offers scale, performance, and value that allows you to easily launch large compute clusters on Google's infrastructure. There are no upfront investments and you can run up to thousands of virtual CPUs on a system that has been designed from the ground up to be fast, and to offer strong consistency of performance.

What is 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.
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    What are some alternatives to Google Compute Engine and Heroku?
    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.
    DigitalOcean
    We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel.
    Google Cloud Platform
    It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    Microsoft Azure
    Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.
    See all alternatives
    Decisions about Google Compute Engine and Heroku
    Jerome Dalbert
    Jerome Dalbert
    Senior Backend Engineer at StackShare · | 7 upvotes · 21.8K views
    atGratify CommerceGratify Commerce
    Rails
    Rails
    Heroku
    Heroku
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    #PaaS

    When creating the web infrastructure for our start-up, I wanted to host our app on a PaaS to get started quickly.

    A very popular one for Rails is Heroku, which I love for free hobby side projects, but never used professionally. On the other hand, I was very familiar with the AWS ecosystem, and since I was going to use some of its services anyways, I thought: why not go all in on it?

    It turns out that Amazon offers a PaaS called AWS Elastic Beanstalk, which is basically like an “AWS Heroku”. It even comes with a similar command-line utility, called "eb”. While edge-case Rails problems are not as well documented as with Heroku, it was very satisfying to manage all our cloud services under the same AWS account. There are auto-scaling options for web and worker instances, which is a nice touch. Overall, it was reliable, and I would recommend it to anyone planning on heavily using AWS.

    See more
    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 14 upvotes · 173.1K views
    atCodeFactorCodeFactor
    Kubernetes
    Kubernetes
    CodeFactor.io
    CodeFactor.io
    Amazon EC2
    Amazon EC2
    Microsoft Azure
    Microsoft Azure
    Google Compute Engine
    Google Compute Engine
    Docker
    Docker
    AWS Lambda
    AWS Lambda
    Azure Functions
    Azure Functions
    Google Cloud Functions
    Google Cloud Functions
    #SAAS
    #IAAS
    #Containerization
    #Autoscale
    #Startup
    #Automation
    #Machinelearning
    #AI
    #Devops

    CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

    CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

    AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

    It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

    The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

    In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

    Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

    See more
    Russel Werner
    Russel Werner
    Lead Engineer at StackShare · | 21 upvotes · 509.1K views
    atStackShareStackShare
    React
    React
    Glamorous
    Glamorous
    Apollo
    Apollo
    Node.js
    Node.js
    Rails
    Rails
    Heroku
    Heroku
    GitHub
    GitHub
    Amazon S3
    Amazon S3
    Amazon CloudFront
    Amazon CloudFront
    Webpack
    Webpack
    CircleCI
    CircleCI
    Redis
    Redis
    #StackDecisionsLaunch
    #SSR
    #Microservices
    #FrontEndRepoSplit

    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
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Heroku
    Heroku
    Ruby
    Ruby
    Rails
    Rails
    Amazon RDS for PostgreSQL
    Amazon RDS for PostgreSQL
    MariaDB
    MariaDB
    Microsoft SQL Server
    Microsoft SQL Server
    Amazon RDS
    Amazon RDS
    AWS Lambda
    AWS Lambda
    Python
    Python
    Redis
    Redis
    Memcached
    Memcached
    AWS Elastic Load Balancing (ELB)
    AWS Elastic Load Balancing (ELB)
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Amazon ElastiCache
    Amazon ElastiCache

    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
    Heroku
    Heroku
    Red Hat OpenShift
    Red Hat OpenShift
    Docker
    Docker

    Heroku vs OpenShift. I've never decided which one is better. Heroku is easier to configure. Openshift provide a better machine for free. Heroku has many addons for free. I've chosen Heroku because of easy initial set-up. I had deployment based on git push. I also tried direct deployment of jar file. Currently Heroku runs my Docker image. Heroku has very good documentation like for beginners. So if you want to start with something, let's follow Heroku. On the other hand OpenShift seems like a PRO tool supported by @RedHat.

    See more
    Mohamed Labouardy
    Mohamed Labouardy
    Founder at Komiser · | 5 upvotes · 30.9K views
    atKomiserKomiser
    Google Compute Engine
    Google Compute Engine
    Amazon Web Services
    Amazon Web Services
    OVH
    OVH
    Microsoft Azure
    Microsoft Azure
    Go
    Go
    GitHub
    GitHub

    Google Compute Engine Amazon Web Services OVH Microsoft Azure Go GitHub

    Last week, we released a fresh new release of Komiser with support of multiple AWS accounts. Komiser support multiple AWS accounts through named profiles that are stored in the credentials files.

    You can now analyze and identify potential cost savings on unlimited AWS environments (Production, Staging, Sandbox, etc) on one single dashboard.

    Read the full story in the blog post.

    See more
    Mohamed Labouardy
    Mohamed Labouardy
    Founder at Komiser · | 5 upvotes · 38.1K views
    atKomiserKomiser
    Google Compute Engine
    Google Compute Engine
    Amazon Web Services
    Amazon Web Services
    Go
    Go
    Docker
    Docker
    Material Design for Angular
    Material Design for Angular
    Microsoft Azure
    Microsoft Azure
    GitHub
    GitHub

    Google Compute Engine Amazon Web Services Go Docker Material Design for Angular Microsoft Azure GitHub I’m super excited to annonce the release of Komiser:2.1.0 with beta support of Google Cloud Platform. You can now use one single open source tool to detect both AWS and GCP overspending.

    Komiser allows you to analyze and manage #cloud cost, usage, #security, and governance in one place. Hence, detecting potential vulnerabilities that could put your cloud environment at risk.

    It allows you also to control your usage and create visibility across all used services to achieve maximum cost-effectiveness and get a deep understanding of how you spend on the #AWS, #GCP and #Azure.

    See more
    Gunicorn
    Gunicorn
    uWSGI
    uWSGI
    Heroku
    Heroku
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk

    I use Gunicorn because does one thing - it’s a WSGI HTTP server - and it does it well. Deploy it quickly and easily, and let the rest of your stack do what the rest of your stack does well, wherever that may be.

    uWSGI “aims at developing a full stack for building hosting services” - if that’s a thing you need then ok, but I like the principle of doing one thing well, and I deploy to platforms like Heroku and AWS Elastic Beanstalk where the rest of the “hosting service” is provided and managed for me.

    See more
    Munkhtegsh Munkhbat
    Munkhtegsh Munkhbat
    Software Engineer Consultant at LoanSnap · | 9 upvotes · 46.5K views
    graphql-yoga
    graphql-yoga
    Prisma
    Prisma
    PostgreSQL
    PostgreSQL
    styled-components
    styled-components
    Heroku
    Heroku
    React
    React
    Apollo
    Apollo
    GraphQL
    GraphQL
    #Backend
    #Frontend

    In my last side project, I built a web posting application that has similar features as Facebook and hosted on Heroku. The user can register an account, create posts, upload images and share with others. I took an advantage of graphql-subscriptions to handle realtime notifications in the comments section. Currently, I'm at the last stage of styling and building layouts.

    For the #Backend I used graphql-yoga, Prisma, GraphQL with PostgreSQL database. For the #FrontEnd: React, styled-components with Apollo. The app is hosted on Heroku.

    See more
    Interest over time
    Reviews of Google Compute Engine and Heroku
    Review ofHerokuHeroku

    I use Heroku, for almost any project of mine. Their free plan is awesome for testing, solo developers or your startup and its almost impossible to not cover you somehow. Adding an add on is a simple command away and I find it easy to use it both on my Windows PC or my Linux laptop. Their documentation, covers almost everything. In particular I have used Heroku for Spring, Django and AngularJS. I even find it easier to run my project on my local dev with foreman start, than ./manage.py runserver (for my django projects). There is no place like Heroku for the developer!

    Review ofGoogle Compute EngineGoogle Compute Engine
    • I use Google Compute Engine instances as flexible, reproducible infrastructure that scale with my data science tasks.

    • Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.

    • Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome

    Review ofHerokuHeroku

    Can't beat the simplicity of deploying and managing apps, the pricing is a bit high, but you are paying for those streamlined tools. However, after several experiences of tracing issues back to Heroku's stack, not having visibility into what they are doing has prompted moving two applications off of it and on to other more transparent cloud solutions. Heroku is amazing for what it is, hosting for early stage products.

    Review ofHerokuHeroku

    I've been using Heroku for 3 years now, they have grown super fast and each time they're improving their services. What I really like the most is how easily you can show to your client the advances on you project, it would take you maximum 15 minutes to configure two environments (Staging/Production). It is simply essential and fantastic!

    Review ofHerokuHeroku

    I liked how easy this was to use and that I could create some proof of concepts without have to pay. The downside for NodeJS is remote debugging. Pretty much have to depend on logging where Azure allows remote debugging with Node Inspector.

    Review ofHerokuHeroku

    Using Heroku takes away all the pains associated with managing compute and backing services. It may require a little extra optimisation and tweaks, but these constraints often make your app better anyway.

    How developers use Google Compute Engine and Heroku
    Avatar of StackShare
    StackShare uses HerokuHeroku

    Not having to deal with servers is a huge win for us. There are certainly trade-offs (having to wait if the platform is down as opposed to being able to fix the issue), but we’re happy being on Heroku right now. Being able to focus 100% of our technical efforts on application code is immensely helpful.

    Two dynos seems to be the sweet spot for our application. We can handle traffic spikes and get pretty consistent performance otherwise.

    We have a total of four apps on Heroku: Legacy Leanstack, StackShare Prod, StackShare Staging, StackShare Dev. Protip: if you’re setting up multiple environments based on your prod environment, just run heroku fork app name. Super useful, it copies over your db, add-ons, and settings.

    We have a develop branch on GitHub that we push to dev to test out, then if everything is cool we push it to staging and eventually prod. Hotfixes of course go straight to staging and then prod usually.

    Avatar of Samuel Harrold
    Samuel Harrold uses Google Compute EngineGoogle Compute Engine
    • I use Google Compute Engine instances as flexible, reproducible infrastructure that scales with my data science tasks.
    • Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
    • Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
    Avatar of StackShare
    StackShare uses HerokuHeroku

    We keep the Metrics tab open while we load test, and hit refresh to see what’s going on: heroku metric

    I would expect the graphs to expand with some sort of detail, but that’s not the case. So these metrics aren’t very useful. The logs are far more useful, so we just keep the tail open while we test.

    Avatar of Tim Lucas
    Tim Lucas uses HerokuHeroku

    Heroku runs the web and background worker processes. Auto-deployments are triggered via GitHub commits and wait for the Buildkite test build to pass. Heroku pipelines with beta release phase execution (for automatically running database migrations) allowed for easy manual testing of big new releases. Web and worker logs are sent to Papertrail.

    Avatar of Jeff Flynn
    Jeff Flynn uses HerokuHeroku

    As much as I love AWS EC, I prefer Heroku for apps like this. Heroku has grown up around Rails and Ruby, massive set of add-ons that are usually one-click setup, and I once had to perform an emergency app scale-up a that I completed in seconds from my mobile phone whilst riding the Bangkok subway. Doesn't get much easier than that.

    Avatar of danlangford
    danlangford uses HerokuHeroku

    With its complimentary SSL (on *.herokuapp.com) we can test everything. Our dev branch is built and deployed out to Heroku. Testing happens out here. not production cause $20/mo is TOO much to pay for the ability to use my own SSL purchased elsewhere.

    Avatar of Casey Smith
    Casey Smith uses Google Compute EngineGoogle Compute Engine

    Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.

    Avatar of BitBank
    BitBank uses Google Compute EngineGoogle Compute Engine

    Compute engine is used to run our live forecaster and cron jobs

    Avatar of Jonathan Fries
    Jonathan Fries uses Google Compute EngineGoogle Compute Engine

    Ghost runs on a VM from Google Compute Engine.

    Avatar of Partners in School Innovation
    Partners in School Innovation uses Google Compute EngineGoogle Compute Engine

    Hosting our Bitnami PSQL instance

    How much does Google Compute Engine cost?
    How much does Heroku cost?
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