Google Cloud Platform vs Google Compute Engine: What are the differences?
Developers describe Google Cloud Platform as "A suite of cloud computing services". 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. On the other hand, Google Compute Engine is detailed as "Run large-scale workloads on virtual machines hosted on Google's infrastructure". 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 Cloud Platform and Google Compute Engine can be primarily classified as "Cloud Hosting" tools.
9GAG, Snapchat, and CircleCI are some of the popular companies that use Google Compute Engine, whereas Google Cloud Platform is used by Sentry, WePay, and BetterCloud. Google Compute Engine has a broader approval, being mentioned in 594 company stacks & 429 developers stacks; compared to Google Cloud Platform, which is listed in 61 company stacks and 24 developer stacks.
What is Google Cloud Platform?
What is Google Compute Engine?
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What are the cons of using Google Compute Engine?
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I use Google Compute Engine instances as flexible, reproducible infrastructure that scale with my data science tasks.
Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
- I use Google Compute Engine instances as flexible, reproducible infrastructure that scales with my data science tasks.
- Between Google Cloud and Amazon Web Services, I chose Google Cloud for its intuitive UI. SSH within the browser is very convenient.
- Related blog post with example usage: Running an IPython Notebook on Google Compute Engine from Chrome
Infrastructure for Google App Engine, Google Cloud Endpoints, Memcached, and Google Cloud SQL components, as well as Git repository and Jenkins CI server.