What is Google Compute Engine and what are its top alternatives?
Top Alternatives to Google Compute 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. ...
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
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
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
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 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. ...
- Amazon Web Service
It is a comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. ...
Get a server running in minutes with your choice of Linux distro, resources, and node location. ...
Google Compute Engine alternatives & related posts
Google App Engine
- Easy to deploy144
- Auto scaling106
- Good free plan80
- Easy management62
- Low cost35
- Comprehensive set of features32
- All services in one place28
- Simple scaling22
- Quick and reliable cloud servers19
- Granular Billing6
- Easy to develop and unit test5
- Monitoring gives comprehensive set of key indicators4
- Really easy to quickly bring up a full stack3
- Create APIs quickly with cloud endpoints3
- No Ops2
- Mostly up2
related Google App Engine posts
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
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.
- Great value for money560
- Simple dashboard364
- Good pricing362
- Nice ui250
- Easy configuration191
- Great documentation156
- Ssh access138
- Great community135
- IPv6 support12
- Private networking10
- 99.99% uptime SLA8
- Simple API7
- Great tutorials7
- 55 Second Provisioning6
- One Click Applications5
- 1Gb/sec Servers3
- Word Press3
- Simple Control Panel3
- Runs CoreOS2
- Quick and no nonsense service2
- Good Tutorials2
- Ruby on Rails2
- Hex Core machines with dedicated ECC Ram and RAID SSD s2
- KVM Virtualization1
- Amazing Hardware1
- Transfer Globally1
- FreeBSD Amp1
- My go to server provider1
- Ease and simplicity1
- Find it superfitting with my requirements (SSD, ssh.1
- Easy Setup1
- Static IP1
- It's the easiest to get started for small projects1
- Automatic Backup1
- Great support1
- Quick and easy to set up1
- Servers on demand - literally1
- Variety of services0
- Managed Kubernetes0
- No live support chat3
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!
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!
- Good app Marketplace for Beginner and Advanced User5
- 1 year free trial credit USD3004
- Premium tier IP address3
- Live chat support3
related Google Cloud Platform posts
My days of using Firebase are over! I want to move to something scalable and possibly less cheap. In the past seven days I have done my research on what type of DB best fits my needs, and have chosen to go with the nonrelational DB; MongoDB. Although I understand it, I need help understanding how to set up the architecture. I have the client app (Flutter/ Dart) that would make HTTP requests to the web server (node/express), and from there the webserver would query data from MongoDB.
How should I go about hosting the web server and MongoDb; do they have to be hosted together (this is where a lot of my confusion is)? Based on the research I've done, it seems like the standard practice would be to host on a VM provided by services such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc. If there are better ways, such as possibly self-hosting (more responsibility), should I? Anyways, I just want to confirm with a community (you guys) to make sure I do this right, all input is highly appreciated.
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.
- Quick and reliable cloud servers647
- Easy management393
- Low cost277
- Market leader89
- Backed by amazon80
- Free tier67
- Easy management, scalability58
- Easy to Start10
- Widely used9
- Node.js API7
- Industry Standard5
- Lots of configuration options4
- GPU instances2
- Extremely simple to use1
- Amazing for individuals1
- All the Open Source CLI tools you could want.1
- Simpler to understand and learn1
- Ui could use a lot of work13
- High learning curve when compared to PaaS6
- Extremely poor CPU performance3
related Amazon EC2 posts
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
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.
- Scales well and quite easy114
- Can use .Net or open source tools96
- Startup friendly81
- Startup plans via BizSpark73
- High performance62
- Wide choice of services38
- Low cost32
- Lots of integrations32
- Twillio & Github are directly accessible19
- RESTful API13
- Enterprise Grade10
- Startup support10
- In person support7
- Free for students6
- Service Bus6
- Virtual Machines6
- Redis Cache5
- It rocks5
- Storage, Backup, and Recovery4
- Infrastructure Services4
- SQL Databases4
- Preview Portal3
- Built on Node.js3
- Big Data3
- BizSpark 60k Azure Benefit3
- Open cloud2
- Big Compute2
- Machine Learning2
- Stream Analytics2
- Data Factory2
- Event Hubs2
- Virtual Network2
- Traffic Manager2
- Media Services2
- BizTalk Services2
- Site Recovery2
- Active Directory2
- Multi-Factor Authentication2
- Visual Studio Online2
- Application Insights2
- Operational Insights2
- Key Vault2
- Infrastructure near your customers2
- Easy Deployment2
- Enterprise customer preferences1
- Best cloud platfrom1
- Easy and fast to start with1
- Remote Debugging1
- Confusing UI6
- Expensive plesk on Azure2
related Microsoft Azure posts
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!
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.
- Leading docker container management solution163
- Simple and powerful128
- Open source105
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling8
- No cloud platform lock-in5
- Open, powerful, stable4
- Quick cloud setup4
- Promotes modern/good infrascture practice4
- Backed by Red Hat3
- A self healing environment with rich metadata3
- Captain of Container Ship3
- Cloud Agnostic3
- Custom and extensibility3
- Runs on azure3
- Everything of CaaS2
- Easy setup2
- Steep learning curve15
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1
related Kubernetes posts
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:
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
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...
related Amazon Web Service posts
- Extremely reliable100
- Good value70
- Great customer support60
- Easy to configure58
- Great documentation37
- Servers across the world24
- Managed/hosted DNS service18
- Simple ui15
- Network and CPU usage graphs11
- IPv6 support7
- Multiple IP address support6
- Good price, good cusomter sevice3
- Ssh access3
- IP address fail over support2
- SSH root access2
- Great performance compared to EC2 or DO1
- It runs apps with speed1
- Best customizable VPS1
- Latest kernels1
- No "floating IP" support2
related Linode posts
What is the data transfer out cost (Bandwidth cost) on Linode compared to Microsoft Azure?