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.

Google Compute Engine alternatives & related posts

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Nick Rockwell
Nick Rockwell
CTO at NY Times | 9 upvotes 39.3K views
atThe New York TimesThe New York Times
Amazon EC2
Amazon EC2
Google App Engine
Google App Engine
Google Kubernetes Engine
Google Kubernetes Engine
Kubernetes
Kubernetes
#AWS
#GCP
#AWStoGCPmigration
#Cloudmigration
#Migration

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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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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DigitalOcean
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Google Compute Engine

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Rajat Jain
Rajat Jain
Devops Engineer at Aurochssoftware | 1 upvotes 8.3K views
Amazon EC2
Amazon EC2
Amazon S3
Amazon S3
Bitbucket
Bitbucket
GitLab
GitLab
PyCharm
PyCharm
Ubuntu
Ubuntu
DigitalOcean
DigitalOcean
Docker
Docker
Git
Git

Building my skill set to become Devops Engineer-Tool chain: Amazon EC2, Amazon S3, Bitbucket, GitLab, PyCharm, Ubuntu, DigitalOcean, Docker, Git

IT engineer with more than 6 months of experience in startups with focus on DevOps, Cloud infrastructure & Testing (QA). I had set up CI process, monitoring and infrastructure on dev/test (lower) environments

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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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    Google Cloud Platform
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    Google Compute Engine

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    Jorge Cortell
    Jorge Cortell
    Founder & CEO at Kanteron Systems | 1 upvotes 6.2K views
    atKanteron SystemsKanteron Systems
    Google Cloud Platform
    Google Cloud Platform
    Microsoft Azure
    Microsoft Azure
    Amazon S3
    Amazon S3

    We use Google Cloud Platform, Microsoft Azure and Amazon S3 (amongst others) because our platform needs to be cloud-independent to give customers the freedom they need and deserve. But being in the healthcare enterprise space, we believe Azure is the top choice... today (it tends to change often).

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    Amazon EC2 logo

    Amazon EC2

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    Scalable, pay-as-you-go compute capacity in the cloud
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    Amazon EC2
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    Google Compute Engine

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    John-Daniel Trask
    John-Daniel Trask
    Co-founder & CEO at Raygun | 19 upvotes 78.5K views
    atRaygunRaygun
    Amazon S3
    Amazon S3
    Amazon RDS
    Amazon RDS
    nginx
    nginx
    Amazon EC2
    Amazon EC2
    AWS Elastic Load Balancing (ELB)
    AWS Elastic Load Balancing (ELB)
    #CloudHosting
    #WebServers
    #CloudStorage
    #LoadBalancerReverseProxy

    We chose AWS because, at the time, it was really the only cloud provider to choose from.

    We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.

    We鈥檝e utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).

    While we鈥檙e satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.

    #CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy

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    Ashish Singh
    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest | 17 upvotes 14.4K views
    Apache Hive
    Apache Hive
    Kubernetes
    Kubernetes
    Kafka
    Kafka
    Amazon S3
    Amazon S3
    Amazon EC2
    Amazon EC2
    Presto
    Presto
    #DataScience
    #DataEngineering
    #AWS
    #BigData

    To provide employees with the critical need of interactive querying, we鈥檝e worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest鈥檚 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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    Kestas Barzdaitis
    Kestas Barzdaitis
    Entrepreneur & Engineer | 14 upvotes 79.4K 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.

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    Omar Mehilba
    Omar Mehilba
    Co-Founder and COO at Magalix | 13 upvotes 55.8K views
    atMagalixMagalix
    Kubernetes
    Kubernetes
    Microsoft Azure
    Microsoft Azure
    Google Kubernetes Engine
    Google Kubernetes Engine
    Amazon EC2
    Amazon EC2
    Go
    Go
    Python
    Python
    #Autopilot

    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

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    Yshay Yaacobi
    Yshay Yaacobi
    Software Engineer | 27 upvotes 339.8K views
    atSolutoSoluto
    Docker Swarm
    Docker Swarm
    .NET
    .NET
    F#
    F#
    C#
    C#
    JavaScript
    JavaScript
    TypeScript
    TypeScript
    Go
    Go
    Visual Studio Code
    Visual Studio Code
    Kubernetes
    Kubernetes

    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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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber | 16 upvotes 833.5K views
    atUber TechnologiesUber Technologies
    Jaeger
    Jaeger
    Python
    Python
    Java
    Java
    Node.js
    Node.js
    Go
    Go
    C++
    C++
    Kubernetes
    Kubernetes
    JavaScript
    JavaScript
    OpenShift
    OpenShift
    C#
    C#
    Apache Spark
    Apache Spark

    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

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    Vultr logo

    Vultr

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    Deploy Cloud Servers, Bare Metal, and Storage worldwide
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    Paul Whittemore
    Paul Whittemore
    Developer and Owner at Appurist Software | 4 upvotes 25.4K views
    Vultr
    Vultr
    Amazon LightSail
    Amazon LightSail
    Windows
    Windows
    Windows Server
    Windows Server

    For those needing hosting on Windows or Windows Server too (and avoiding licensing hurdles), both Vultr and Amazon LightSail offer compelling choices, depending on how much compute power you need. Don't underestimate Amazon LightSail, especially for smaller or starting projects, but Vultr also offers an incremental $16 Windows option on top of their standard compute offerings.

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    Amazon LightSail logo

    Amazon LightSail

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    Simple Virtual Private Servers on AWS
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    Amazon LightSail
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    Google Compute Engine

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    Paul Whittemore
    Paul Whittemore
    Developer and Owner at Appurist Software | 4 upvotes 25.4K views
    Vultr
    Vultr
    Amazon LightSail
    Amazon LightSail
    Windows
    Windows
    Windows Server
    Windows Server

    For those needing hosting on Windows or Windows Server too (and avoiding licensing hurdles), both Vultr and Amazon LightSail offer compelling choices, depending on how much compute power you need. Don't underestimate Amazon LightSail, especially for smaller or starting projects, but Vultr also offers an incremental $16 Windows option on top of their standard compute offerings.

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