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Google AI Platform vs Heroku: What are the differences?

Developers describe Google AI Platform as "Create your AI applications once, then run them easily on both GCP and on-premises". Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. 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 AI Platform can be classified as a tool in the "Machine Learning as a Service" category, while Heroku is grouped under "Platform as a Service".

Some of the features offered by Google AI Platform are:

  • “No lock-in” flexibility
  • Supports Kubeflow
  • Supports TensorFlow

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.
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What is Google AI Platform?

Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.

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 AI Platform and Heroku?
      Azure Machine Learning
      Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
      Amazon Machine Learning
      This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
      Amazon SageMaker
      A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
      NanoNets
      Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
      Amazon Elastic Inference
      Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
      See all alternatives
      Decisions about Google AI Platform and Heroku
      Jerome Dalbert
      Jerome Dalbert
      Senior Backend Engineer at StackShare · | 7 upvotes · 17.9K views
      atGratify CommerceGratify Commerce
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Heroku
      Heroku
      Rails
      Rails
      #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
      Russel Werner
      Russel Werner
      Lead Engineer at StackShare · | 17 upvotes · 208.8K views
      atStackShareStackShare
      Redis
      Redis
      CircleCI
      CircleCI
      Webpack
      Webpack
      Amazon CloudFront
      Amazon CloudFront
      Amazon S3
      Amazon S3
      GitHub
      GitHub
      Heroku
      Heroku
      Rails
      Rails
      Node.js
      Node.js
      Apollo
      Apollo
      Glamorous
      Glamorous
      React
      React
      #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
      Amazon ElastiCache
      Amazon ElastiCache
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      AWS Elastic Load Balancing (ELB)
      AWS Elastic Load Balancing (ELB)
      Memcached
      Memcached
      Redis
      Redis
      Python
      Python
      AWS Lambda
      AWS Lambda
      Amazon RDS
      Amazon RDS
      Microsoft SQL Server
      Microsoft SQL Server
      MariaDB
      MariaDB
      Amazon RDS for PostgreSQL
      Amazon RDS for PostgreSQL
      Rails
      Rails
      Ruby
      Ruby
      Heroku
      Heroku
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk

      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
      Docker
      Docker
      OpenShift
      OpenShift
      Heroku
      Heroku

      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
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Heroku
      Heroku
      uWSGI
      uWSGI
      Gunicorn
      Gunicorn

      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 · 20.2K views
      GraphQL
      GraphQL
      Apollo
      Apollo
      React
      React
      Heroku
      Heroku
      styled-components
      styled-components
      PostgreSQL
      PostgreSQL
      Prisma
      Prisma
      graphql-yoga
      graphql-yoga
      #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 AI Platform 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 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 AI Platform 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 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.

      How much does Google AI Platform cost?
      How much does Heroku cost?
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