Alternatives to Linode logo

Alternatives to Linode

DigitalOcean, Vultr, WebFaction, Heroku, and RamNode are the most popular alternatives and competitors to Linode.
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What is Linode and what are its top alternatives?

Get a server running in minutes with your choice of Linux distro, resources, and node location.
Linode is a tool in the Cloud Hosting category of a tech stack.

Linode alternatives & related posts

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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Linode

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Rajat Jain
Rajat Jain
Devops Engineer at Aurochssoftware | 1 upvotes 22.5K 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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Vultr logo

Vultr

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Deploy Cloud Servers, Bare Metal, and Storage worldwide
Vultr logo
Vultr
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Linode

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Paul Whittemore
Paul Whittemore
Developer and Owner at Appurist Software | 4 upvotes 58.8K 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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Heroku logo

Heroku

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Build, deliver, monitor and scale web apps and APIs with a trail blazing developer experience.
Heroku logo
Heroku
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Linode

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Russel Werner
Russel Werner
Lead Engineer at StackShare | 22 upvotes 518.8K views
atStackShareStackShare
React
React
Glamorous
Glamorous
Apollo
Apollo
Node.js
Node.js
Rails
Rails
Heroku
Heroku
GitHub
GitHub
Amazon S3
Amazon S3
Amazon CloudFront
Amazon CloudFront
Webpack
Webpack
CircleCI
CircleCI
Redis
Redis
#StackDecisionsLaunch
#SSR
#Microservices
#FrontEndRepoSplit

StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

#StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly | 20 upvotes 513.4K views
atChecklyHQChecklyHQ
Heroku
Heroku
Docker
Docker
GitHub
GitHub
Node.js
Node.js
hapi
hapi
Vue.js
Vue.js
AWS Lambda
AWS Lambda
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Knex.js
Knex.js
vuex
vuex

Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.

We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.

Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.

Enough biz talk, onto tech. The challenges were:

  • Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
  • Update API and back end services to handle and enforce plan limits.
  • Update the UI to kindly state plan limits are in effect on some part of the UI.
  • Update the pricing page to reflect all changes.
  • Keep the actual processing backend, storage and API's as untouched as possible.

In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.

  1. We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
  2. The Vue.js frontend reads these from the vuex store on login.
  3. Based on these values, the UI has simple v-if statements to either just show the feature or show a friendly "please upgrade" button.
  4. The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.

Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.

What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.

Hope this helps anyone building out their SaaS and is in a similar situation.

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RamNode logo

RamNode

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High Performance SSD and SSD-Cached VPSs
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RamNode
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Linode

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Ashish Singh
Ashish Singh
Tech Lead, Big Data Platform at Pinterest | 26 upvotes 157.3K 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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John-Daniel Trask
John-Daniel Trask
Co-founder & CEO at Raygun | 19 upvotes 131.1K 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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Microsoft Azure logo

Microsoft Azure

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Integrated cloud services and infrastructure to support computing, database, analytics, mobile, and web scenarios.
Microsoft Azure logo
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Omar Mehilba
Omar Mehilba
Co-Founder and COO at Magalix | 17 upvotes 107.9K 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!

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Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 175.6K 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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Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 175.6K 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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Marcel Kornegoor
Marcel Kornegoor
CTO at AT Computing | 5 upvotes 276.2K views
atAT ComputingAT Computing
Linux
Linux
Ubuntu
Ubuntu
CentOS
CentOS
Debian
Debian
Red Hat Enterprise Linux
Red Hat Enterprise Linux
Fedora
Fedora
Visual Studio Code
Visual Studio Code
Jenkins
Jenkins
VirtualBox
VirtualBox
GitHub
GitHub
Docker
Docker
Kubernetes
Kubernetes
Google Compute Engine
Google Compute Engine
Ansible
Ansible
Puppet Labs
Puppet Labs
Chef
Chef
Python
Python
#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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