Alternatives to Amazon EC2 logo
Amazon LightSail, Amazon S3, Amazon EC2 Container Service, Beanstalk, and Microsoft Azure are the most popular alternatives and competitors to Amazon EC2.
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What is Amazon EC2?

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
Amazon EC2 is a tool in the Cloud Hosting category of a tech stack.

Amazon EC2 alternatives & related posts

Amazon LightSail logo

Amazon LightSail

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Simple Virtual Private Servers on AWS
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Paul Whittemore
Paul Whittemore
Developer and Owner at Appurist Software · | 4 upvotes · 6.1K views
Windows Server
Windows Server
Windows
Windows
Amazon LightSail
Amazon LightSail
Vultr
Vultr

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 S3 logo

Amazon S3

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Store and retrieve any amount of data, at any time, from anywhere on the web
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John-Daniel Trask
John-Daniel Trask
Co-founder & CEO at Raygun · | 19 upvotes · 40.9K views
atRaygunRaygun
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)
Amazon EC2
Amazon EC2
nginx
nginx
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
#LoadBalancerReverseProxy
#CloudStorage
#WebServers
#CloudHosting

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’ve 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’re 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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Julien DeFrance
Julien DeFrance
Full Stack Engineering Manager at ValiMail · | 16 upvotes · 174.6K views
atSmartZipSmartZip
Amazon DynamoDB
Amazon DynamoDB
Ruby
Ruby
Node.js
Node.js
AWS Lambda
AWS Lambda
New Relic
New Relic
Amazon Elasticsearch Service
Amazon Elasticsearch Service
Elasticsearch
Elasticsearch
Superset
Superset
Amazon Quicksight
Amazon Quicksight
Amazon Redshift
Amazon Redshift
Zapier
Zapier
Segment
Segment
Amazon CloudFront
Amazon CloudFront
Memcached
Memcached
Amazon ElastiCache
Amazon ElastiCache
Amazon RDS for Aurora
Amazon RDS for Aurora
MySQL
MySQL
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Docker
Docker
Capistrano
Capistrano
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Rails API
Rails API
Rails
Rails
Algolia
Algolia

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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related Amazon EC2 Container Service posts

Arik Fraimovich
Arik Fraimovich
Kubernetes
Kubernetes
Amazon EC2 Container Service
Amazon EC2 Container Service

We started using Amazon EC2 Container Service 3 years ago because it was the easiest containers orchestration tool to start with. At the time it was missing a lot of features compared to other tools, but it was still the fastest way to deploy a container on AWS. As with any AWS product, over time they caught up and improved it significantly. Today it probably one of the best tools in its category. It might not have all the feature Kubernetes has, but it also has less complexity. And it definitely has all the features a small company/team needs.

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Wesly Nouse
Wesly Nouse
Owner at Absolum · | 1 upvotes · 807 views
atAbsolumAbsolum
Google Kubernetes Engine
Google Kubernetes Engine
Amazon EC2 Container Service
Amazon EC2 Container Service
Kubernetes
Kubernetes

We use Kubernetes because it is the best and easiest way to orchestrate your klusters. Intergrated with Amazon EC2 Container Service or Google Kubernetes Engine it works wonderfully.

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related Microsoft Azure posts

Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer · | 12 upvotes · 30.7K views
atCodeFactorCodeFactor
Google Cloud Functions
Google Cloud Functions
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Docker
Docker
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
Amazon EC2
Amazon EC2
CodeFactor.io
CodeFactor.io
Kubernetes
Kubernetes
#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 · | 10 upvotes · 34.6K views
atMagalixMagalix
Python
Python
Go
Go
Amazon EC2
Amazon EC2
Google Kubernetes Engine
Google Kubernetes Engine
Microsoft Azure
Microsoft Azure
Kubernetes
Kubernetes
#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!

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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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Rajat Jain
Rajat Jain
Devops Engineer at Aurochssoftware · | 1 upvotes · 204 views
Git
Git
Docker
Docker
DigitalOcean
DigitalOcean
Ubuntu
Ubuntu
PyCharm
PyCharm
GitLab
GitLab
Bitbucket
Bitbucket
Amazon S3
Amazon S3
Amazon EC2
Amazon EC2

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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related Google Compute Engine posts

Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer · | 12 upvotes · 30.7K views
atCodeFactorCodeFactor
Google Cloud Functions
Google Cloud Functions
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Docker
Docker
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
Amazon EC2
Amazon EC2
CodeFactor.io
CodeFactor.io
Kubernetes
Kubernetes
#SAAS
#IAAS
#Containerization
#Autoscale
#Startup
#Automation
#Machinelearning
#AI
#Devops

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

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

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

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

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

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

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

See more
Marcel Kornegoor
Marcel Kornegoor
CTO at AT Computing · | 5 upvotes · 44.7K views
atAT ComputingAT Computing
Python
Python
Chef
Chef
Puppet Labs
Puppet Labs
Ansible
Ansible
Google Compute Engine
Google Compute Engine
Kubernetes
Kubernetes
Docker
Docker
GitHub
GitHub
VirtualBox
VirtualBox
Jenkins
Jenkins
Visual Studio Code
Visual Studio Code
Fedora
Fedora
Red Hat Enterprise Linux
Red Hat Enterprise Linux
Debian
Debian
CentOS
CentOS
Ubuntu
Ubuntu
Linux
Linux
#ATComputing

Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

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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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    Jorge Cortell
    Jorge Cortell
    Founder & CEO at Kanteron Systems · | 1 upvotes · 1.1K views
    atKanteron SystemsKanteron Systems
    Amazon S3
    Amazon S3
    Microsoft Azure
    Microsoft Azure
    Google Cloud Platform
    Google Cloud Platform

    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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    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 · 6.1K views
    Windows Server
    Windows Server
    Windows
    Windows
    Amazon LightSail
    Amazon LightSail
    Vultr
    Vultr

    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.

    See more
    Packet logo

    Packet

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    The Leading Bare Metal Cloud for Developers
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    fortrabbit logo

    fortrabbit

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      RamNode logo

      RamNode

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      HP Cloud Compute logo

      HP Cloud Compute

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        Cloudways  logo

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