Alternatives to Google Compute Engine logo

Alternatives to Google Compute Engine

Google App Engine, DigitalOcean, Google Cloud Platform, Amazon EC2, and Microsoft Azure are the most popular alternatives and competitors to Google Compute Engine.
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What is Google Compute Engine and what are its top alternatives?

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.
Google Compute Engine is a tool in the Cloud Hosting category of a tech stack.

Top Alternatives to Google Compute Engine

  • 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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • Microsoft Azure
    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. ...

  • 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. ...

  • Linode
    Linode

    Get a server running in minutes with your choice of Linux distro, resources, and node location. ...

  • Rackspace Cloud Servers
    Rackspace Cloud Servers

    Cloud Servers is based on OpenStack, the open and scalable operating system for building public and private clouds. With the open cloud, you get reliable cloud hosting, without locking your data into one proprietary platform. ...

Google Compute Engine alternatives & related posts

Google App Engine logo

Google App Engine

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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
  • 106
    Auto scaling
  • 80
    Good free plan
  • 62
    Easy management
  • 56
    Scalability
  • 35
    Low cost
  • 32
    Comprehensive set of features
  • 28
    All services in one place
  • 22
    Simple scaling
  • 19
    Quick and reliable cloud servers
  • 6
    Granular Billing
  • 5
    Easy to develop and unit test
  • 4
    Monitoring gives comprehensive set of key indicators
  • 3
    Create APIs quickly with cloud endpoints
  • 3
    Really easy to quickly bring up a full stack
  • 2
    No Ops
  • 2
    Mostly up
CONS OF GOOGLE APP ENGINE
  • 1
    It's a Google product - they don't like your political

related Google App Engine posts

Nick Rockwell
SVP, Engineering at Fastly · | 11 upvotes · 311.7K 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

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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.

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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
  • 559
    Great value for money
  • 364
    Simple dashboard
  • 362
    Good pricing
  • 300
    Ssds
  • 250
    Nice ui
  • 192
    Easy configuration
  • 155
    Great documentation
  • 138
    Ssh access
  • 135
    Great community
  • 24
    Ubuntu
  • 13
    Docker
  • 12
    IPv6 support
  • 10
    Private networking
  • 8
    99.99% uptime SLA
  • 7
    Simple API
  • 7
    Great tutorials
  • 6
    55 Second Provisioning
  • 5
    One Click Applications
  • 4
    Dokku
  • 4
    Node.js
  • 4
    Debian
  • 4
    CoreOS
  • 4
    LAMP
  • 3
    LEMP
  • 3
    Ghost
  • 3
    Simple Control Panel
  • 3
    Word Press
  • 3
    1Gb/sec Servers
  • 2
    Quick and no nonsense service
  • 2
    Speed
  • 2
    Mean
  • 2
    Hex Core machines with dedicated ECC Ram and RAID SSD s
  • 2
    Django
  • 2
    Runs CoreOS
  • 2
    Good Tutorials
  • 2
    GitLab
  • 2
    Ruby on Rails
  • 1
    CentOS
  • 1
    Spaces
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    KVM Virtualization
  • 1
    Amazing Hardware
  • 1
    Transfer Globally
  • 1
    Fedora
  • 1
    FreeBSD
  • 1
    Drupal
  • 1
    FreeBSD Amp
  • 1
    Magento
  • 1
    ownCloud
  • 1
    RedMine
  • 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
    Cheap
  • 1
    Static IP
  • 1
    It's the easiest to get started for small projects
  • 1
    Automatic Backup
  • 1
    Great support
  • 1
    Quick and easy to set up
  • 1
    Servers on demand - literally
  • 1
    Reliability
  • 0
    Variety of services
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    Managed Kubernetes
CONS OF DIGITALOCEAN
  • 3
    Pricing
  • 3
    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!

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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!

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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
    Be the first to leave a con

    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 · 22.3K 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.

    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
    • 643
      Quick and reliable cloud servers
    • 515
      Scalability
    • 391
      Easy management
    • 276
      Low cost
    • 269
      Auto-scaling
    • 88
      Market leader
    • 80
      Backed by amazon
    • 78
      Reliable
    • 66
      Free tier
    • 57
      Easy management, scalability
    • 12
      Flexible
    • 10
      Easy to Start
    • 9
      Web-scale
    • 8
      Widely used
    • 8
      Elastic
    • 7
      Node.js API
    • 4
      Industry Standard
    • 3
      Lots of configuration options
    • 2
      GPU instances
    • 1
      Amazing for individuals
    • 1
      Extremely simple to use
    • 1
      All the Open Source CLI tools you could want.
    • 1
      Simpler to understand and learn
    CONS OF AMAZON EC2
    • 13
      Ui could use a lot of work
    • 6
      High learning curve when compared to PaaS
    • 3
      Extremely poor CPU performance

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    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 37 upvotes · 1M 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

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    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 29 upvotes · 4.2M 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
    Microsoft Azure logo

    Microsoft Azure

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    Integrated cloud services and infrastructure to support computing, database, analytics, mobile, and web scenarios.
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    PROS OF MICROSOFT AZURE
    • 113
      Scales well and quite easy
    • 95
      Can use .Net or open source tools
    • 81
      Startup friendly
    • 73
      Startup plans via BizSpark
    • 62
      High performance
    • 38
      Wide choice of services
    • 32
      Lots of integrations
    • 32
      Low cost
    • 31
      Reliability
    • 19
      Twillio & Github are directly accessible
    • 13
      RESTful API
    • 10
      Startup support
    • 10
      PaaS
    • 10
      Enterprise Grade
    • 8
      DocumentDB
    • 7
      In person support
    • 6
      Virtual Machines
    • 6
      Free for students
    • 6
      Service Bus
    • 5
      Redis Cache
    • 5
      It rocks
    • 4
      CDN
    • 4
      SQL Databases
    • 4
      Infrastructure Services
    • 4
      Storage, Backup, and Recovery
    • 3
      BizSpark 60k Azure Benefit
    • 3
      Integration
    • 3
      IaaS
    • 3
      HDInsight
    • 3
      Scheduler
    • 3
      Built on Node.js
    • 3
      Big Data
    • 3
      Preview Portal
    • 2
      Backup
    • 2
      Open cloud
    • 2
      Web
    • 2
      SaaS
    • 2
      Big Compute
    • 2
      Mobile
    • 2
      Media
    • 2
      Dev-Test
    • 2
      Storage
    • 2
      StorSimple
    • 2
      Machine Learning
    • 2
      Stream Analytics
    • 2
      Data Factory
    • 2
      Event Hubs
    • 2
      Virtual Network
    • 2
      ExpressRoute
    • 2
      Traffic Manager
    • 2
      Media Services
    • 2
      BizTalk Services
    • 2
      Site Recovery
    • 2
      Active Directory
    • 2
      Multi-Factor Authentication
    • 2
      Visual Studio Online
    • 2
      Application Insights
    • 2
      Automation
    • 2
      Operational Insights
    • 2
      Key Vault
    • 2
      Infrastructure near your customers
    • 2
      Easy Deployment
    • 1
      Enterprise customer preferences
    • 1
      Documentation
    • 1
      Security
    • 1
      Best cloud platfrom
    • 1
      Easy and fast to start with
    • 1
      Remote Debugging
    CONS OF MICROSOFT AZURE
    • 6
      Confusing UI
    • 2
      Expensive plesk on Azure

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    Omar Mehilba
    Co-Founder and COO at Magalix · | 19 upvotes · 269.3K views

    We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!

    See more
    Kestas Barzdaitis
    Entrepreneur & Engineer · | 16 upvotes · 495.1K 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
    Kubernetes logo

    Kubernetes

    44.3K
    38.1K
    634
    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    PROS OF KUBERNETES
    • 161
      Leading docker container management solution
    • 126
      Simple and powerful
    • 102
      Open source
    • 75
      Backed by google
    • 56
      The right abstractions
    • 24
      Scale services
    • 19
      Replication controller
    • 9
      Permission managment
    • 7
      Simple
    • 7
      Supports autoscaling
    • 6
      Cheap
    • 4
      Self-healing
    • 4
      No cloud platform lock-in
    • 4
      Reliable
    • 3
      Open, powerful, stable
    • 3
      Scalable
    • 3
      Quick cloud setup
    • 3
      Promotes modern/good infrascture practice
    • 2
      Backed by Red Hat
    • 2
      Cloud Agnostic
    • 2
      Runs on azure
    • 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
    • 14
      Poor workflow for development
    • 12
      Steep learning curve
    • 6
      Orchestrates only infrastructure
    • 3
      High resource requirements for on-prem clusters

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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M 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...

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    Linode logo

    Linode

    702
    583
    420
    Deploy and Manage Linux Virtual Servers in the Linode Cloud.
    702
    583
    + 1
    420
    PROS OF LINODE
    • 100
      Extremely reliable
    • 70
      Good value
    • 59
      Great customer support
    • 58
      Easy to configure
    • 36
      Great documentation
    • 24
      Servers across the world
    • 18
      Managed/hosted DNS service
    • 15
      Simple ui
    • 11
      Network and CPU usage graphs
    • 7
      IPv6 support
    • 6
      Multiple IP address support
    • 3
      Ssh access
    • 3
      Good price, good cusomter sevice
    • 2
      IP address fail over support
    • 2
      SSH root access
    • 1
      Great performance compared to EC2 or DO
    • 1
      It runs apps with speed
    • 1
      Best customizable VPS
    • 1
      Latest kernels
    • 1
      Cheapest
    • 1
      Ssds
    CONS OF LINODE
    • 2
      No "floating IP" support

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    Kumar Gaurav
    DevOps Engineer at CoRover Private Limited · | 2 upvotes · 22.9K views
    Shared insights
    on
    Microsoft AzureMicrosoft AzureLinodeLinode

    What is the data transfer out cost (Bandwidth cost) on Linode compared to Microsoft Azure?

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    Rackspace Cloud Servers logo

    Rackspace Cloud Servers

    288
    206
    106
    Powerful Linux and Windows servers in minutes
    288
    206
    + 1
    106
    PROS OF RACKSPACE CLOUD SERVERS
    • 41
      Quick and reliable cloud servers
    • 29
      Great customer support
    • 20
      SSDs
    • 10
      Uses OpenStack
    • 2
      Windows server 2012 possible
    • 2
      Easy integration with Github
    • 1
      Ubuntu
    • 1
      Ssh access
    CONS OF RACKSPACE CLOUD SERVERS
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