Alternatives to Microsoft Azure logo

Alternatives to Microsoft Azure

Amazon Web Services, Google Cloud Platform, DigitalOcean, OneDrive, and Hadoop are the most popular alternatives and competitors to Microsoft Azure.
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What is Microsoft Azure and what are its top alternatives?

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.
Microsoft Azure is a tool in the Cloud Hosting category of a tech stack.

Top Alternatives to Microsoft Azure

  • Amazon Web Services

    Amazon Web Services

    It provides on-demand cloud computing platforms to individuals, companies and governments. It offers reliable, scalable, and inexpensive cloud computing services. ...

  • Google Cloud Platform

    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. ...

  • DigitalOcean

    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. ...

  • OneDrive

    OneDrive

    Outlook.com is a free, personal email service from Microsoft. Keep your inbox clutter-free with powerful organizational tools, and collaborate easily with OneDrive and Office Online integration. ...

  • Hadoop

    Hadoop

    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. ...

  • Oracle

    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • Amazon EC2

    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. ...

  • Google Compute Engine

    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. ...

Microsoft Azure alternatives & related posts

Amazon Web Services logo

Amazon Web Services

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The world’s most comprehensive and broadly adopted cloud platform, offering over 165 fully featured services
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PROS OF AMAZON WEB SERVICES
  • 2
    Industry standard cloud vendor
  • 1
    <a href="https://hostandprotect.com/">best hosting</a>
  • 1
    Free tier
  • 1
    Scalable and high performance
CONS OF AMAZON WEB SERVICES
  • 0
    Amazing severless stack with lambda and api gateway

related Amazon Web Services posts

Mohamed Labouardy

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

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
Google Cloud Platform logo

Google Cloud Platform

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A suite of cloud computing services
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PROS OF GOOGLE CLOUD PLATFORM
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    1 year free trial credit USD300
  • 2
    Cheap
  • 2
    Good app Marketplace for Beginner and Advanced User
  • 2
    Premium tier IP address
  • 1
    Live chat support
CONS OF GOOGLE CLOUD PLATFORM
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    related Google Cloud Platform posts

    I am currently working on a long term mobile app project. Current stack: Frontend: Dart/Flutter Backend: Go, AWS Resources (AWS Lambda, Amazon DynamoDB, etc.) Since there are only two developers and we have limited time and resources, we are looking for a BAAS like Firebase or AWS Amplify to handle auth and push notifications for now. We are prioritizing developing speed so we can iterate quickly. The only problem is that AWS amplify support for flutter is in developer preview and has limited capabilities (We have tested it out in our app). Firebase is the more mature option. It has great support for flutter and has more than we need for auth, notifications, etc. My question is that, if we choose firebase, we would be stuck with using two different cloud providers. Is this bad, or is this even a problem? I am willing to change anything on the backend architecture wise, so any suggestions would be greatly appreciated as I am somewhat unfamiliar with Google Cloud Platform. Thank you.

    See more
    Sumit Singh Chauhan
    Data Scientist at Entropik · | 6 upvotes · 4.3K views

    I have started using AWS Batch for some long ML inference jobs. So far it's working well and giving a decent performance. Since it is fully managed, it saves a lot of extra work as well. But Batch takes a good amount of time to create a new cluster and then load the job based on the priority of the queue. Going forward would love to put effort into something which is fast to start and give more flexibility as well. What other tools you would suggest for long-running backend jobs which can scale well. I am not looking for something fully managed so ignore the options similar to batch in Google Cloud Platform or Microsoft Azure, Looking for open-source alternatives here. Do you think Kubernetes, RabbitMQ/Kafka will be a good fit or just overkill for my problem. Usually w we get 1000s of requests in parallel and each job might take 20-30 mins in a 2 vCPU system.

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    DigitalOcean logo

    DigitalOcean

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    Deploy an SSD cloud server in less than 55 seconds with a dedicated IP and root access.
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    PROS OF DIGITALOCEAN
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      Great value for money
    • 363
      Simple dashboard
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      Good pricing
    • 300
      Ssds
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      Nice ui
    • 192
      Easy configuration
    • 155
      Great documentation
    • 137
      Ssh access
    • 134
      Great community
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      Ubuntu
    • 13
      Docker
    • 12
      IPv6 support
    • 10
      Private networking
    • 7
      99.99% uptime SLA
    • 7
      Great tutorials
    • 7
      Simple API
    • 6
      55 Second Provisioning
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      One Click Applications
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      CoreOS
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      Dokku
    • 4
      Node.js
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      Debian
    • 4
      LAMP
    • 3
      Ghost
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      1Gb/sec Servers
    • 3
      Simple Control Panel
    • 3
      LEMP
    • 3
      Word Press
    • 2
      Runs CoreOS
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      Mean
    • 2
      Speed
    • 2
      GitLab
    • 2
      Django
    • 2
      Quick and no nonsense service
    • 2
      Good Tutorials
    • 2
      Ruby on Rails
    • 2
      Hex Core machines with dedicated ECC Ram and RAID SSD s
    • 1
      Spaces
    • 1
      My go to server provider
    • 1
      Ease and simplicity
    • 1
      Nice
    • 1
      Find it superfitting with my requirements (SSD, ssh.
    • 1
      Easy Setup
    • 1
      Transfer Globally
    • 1
      Drupal
    • 1
      FreeBSD Amp
    • 1
      Amazing Hardware
    • 1
      Magento
    • 1
      KVM Virtualization
    • 1
      ownCloud
    • 1
      RedMine
    • 1
      CentOS
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      Fedora
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      FreeBSD
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      Cheap
    • 1
      Static IP
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      It's the easiest to get started for small projects
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      Automatic Backup
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      Great support
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      Quick and easy to set up
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      Servers on demand - literally
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      Reliability
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      Variety of services
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      Managed Kubernetes
    CONS OF DIGITALOCEAN
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      Pricing
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      No live support chat

    related DigitalOcean posts

    Hello, I'm currently writing an e-commerce website with Laravel and Laravel Nova (as an admin panel). I want to start deploying the app and created a DigitalOcean account. After some searches about the deployment process, I saw that the setup via DigitalOcean (using Droplets) isn't very easy for beginners. Now I'm not sure how to deploy my app. I am in between Laravel Forge and DigitalOcean (?Apps Platform or Droplets?). I've read that Heroku and Laravel Vapor are a bit expensive. That's why I didn't consider them yet. I'd be happy to read your opinions on that topic!

    See more

    Hi, I'm a beginner at using MySQL, I currently deployed my crud app on Heroku using the ClearDB add-on. I didn't see that coming, but the increased value of the primary key instead of being 1 is set to 10, and I cannot find a way to change it. Now I`m considering switching and deploying the full app and MySql to DigitalOcean any advice on that? Will I get the same issue? Thanks in advance!

    See more
    OneDrive logo

    OneDrive

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    Save your files and photos to OneDrive and get them from any device, anywhere
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    PROS OF ONEDRIVE
      Be the first to leave a pro
      CONS OF ONEDRIVE
        Be the first to leave a con

        related OneDrive posts

        Hadoop logo

        Hadoop

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        Open-source software for reliable, scalable, distributed computing
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        PROS OF HADOOP
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          Great ecosystem
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          One stack to rule them all
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          Great load balancer
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          Amazon aws
        • 1
          Java syntax
        CONS OF HADOOP
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          related Hadoop posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1M views

          Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

          Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

          https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

          (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

          See more
          Shared insights
          on
          KafkaKafkaHadoopHadoop
          at

          The early data ingestion pipeline at Pinterest used Kafka as the central message transporter, with the app servers writing messages directly to Kafka, which then uploaded log files to S3.

          For databases, a custom Hadoop streamer pulled database data and wrote it to S3.

          Challenges cited for this infrastructure included high operational overhead, as well as potential data loss occurring when Kafka broker outages led to an overflow of in-memory message buffering.

          See more
          Oracle logo

          Oracle

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          An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
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          PROS OF ORACLE
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            Reliable
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            Enterprise
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            High Availability
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            Hard to maintain
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            Expensive
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            Maintainable
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            High complexity
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            Hard to use
          CONS OF ORACLE
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            Expensive

          related Oracle posts

          Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

          We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

          We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

          ASP.NET / Node.js / Laravel. ......?

          Please guide us

          See more
          Amazon EC2 logo

          Amazon EC2

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          Scalable, pay-as-you-go compute capacity in the cloud
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          PROS OF AMAZON EC2
          • 644
            Quick and reliable cloud servers
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            Scalability
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            Easy management
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            Low cost
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            Auto-scaling
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            Market leader
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            Backed by amazon
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            Reliable
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            Free tier
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            Easy management, scalability
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            Flexible
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            Easy to Start
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            Web-scale
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            Widely used
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            Elastic
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            Node.js API
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            Industry Standard
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            Lots of configuration options
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            GPU instances
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            Amazing for individuals
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            Extremely simple to use
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            All the Open Source CLI tools you could want.
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            Simpler to understand and learn
          CONS OF AMAZON EC2
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            Ui could use a lot of work
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            High learning curve when compared to PaaS
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            Extremely poor CPU performance

          related Amazon EC2 posts

          Ashish Singh
          Tech Lead, Big Data Platform at Pinterest · | 36 upvotes · 881.5K views

          To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

          Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

          We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

          Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

          Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

          #BigData #AWS #DataScience #DataEngineering

          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 Compute Engine logo

          Google Compute Engine

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          Run large-scale workloads on virtual machines hosted on Google's infrastructure.
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          PROS OF GOOGLE COMPUTE ENGINE
          • 88
            Backed by google
          • 80
            Easy to scale
          • 75
            High-performance virtual machines
          • 58
            Performance
          • 52
            Fast and easy provisioning
          • 15
            Load balancing
          • 12
            Compliance and security
          • 9
            Kubernetes
          • 8
            GitHub Integration
          • 7
            Consistency
          • 3
            Good documentation
          • 3
            One Click Setup Options
          • 3
            Free $300 credit (12 months)
          • 2
            Ease of Use and GitHub support
          • 2
            Great integration and product support
          • 2
            Escort
          • 1
            Integration with mobile notification services
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            Easy Snapshot and Backup feature
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            Low cost
          • 1
            Support many OS
          • 1
            Very Reliable
          • 1
            Nice UI
          CONS OF GOOGLE COMPUTE ENGINE
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            related Google Compute Engine posts

            Kestas Barzdaitis
            Entrepreneur & Engineer · | 16 upvotes · 452.4K views

            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
            Mohamed Labouardy

            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