Alternatives to HP Cloud Compute logo

Alternatives to HP Cloud Compute

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

Now backed by an industry-leading Service Level Agreement, HP Cloud Compute delivers reliable computing resources to handle your production workloads. Configure and scale your capacity in an open cloud environment. With HP Cloud Compute, you gain control over your workloads while only paying for the resources you actually use.
HP Cloud Compute is a tool in the Cloud Hosting category of a tech stack.

HP Cloud Compute alternatives & related posts

Amazon EC2 logo

Amazon EC2

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Scalable, pay-as-you-go compute capacity in the cloud
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Ashish Singh
Ashish Singh
Tech Lead, Big Data Platform at Pinterest | 20 upvotes 34.5K 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 84.2K 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.
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Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 88.3K 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 58.7K 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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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 9.5K 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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Kestas Barzdaitis
Kestas Barzdaitis
Entrepreneur & Engineer | 14 upvotes 88.3K 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.

See more
Marcel Kornegoor
Marcel Kornegoor
CTO at AT Computing | 5 upvotes 139K 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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Google Cloud Platform logo

Google Cloud Platform

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    Jorge Cortell
    Jorge Cortell
    Founder & CEO at Kanteron Systems | 1 upvotes 7K 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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    Vultr logo

    Vultr

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    Paul Whittemore
    Paul Whittemore
    Developer and Owner at Appurist Software | 4 upvotes 28.8K 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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    Amazon LightSail logo

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    Paul Whittemore
    Paul Whittemore
    Developer and Owner at Appurist Software | 4 upvotes 28.8K views
    Vultr
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    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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    Packet

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    The Leading Bare Metal Cloud for Developers
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    fortrabbit logo

    fortrabbit

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