Microsoft Azure vs Google Cloud Platform: What are some of the major shortcomings of Google Cloud Platform?
Both Microsoft Azure and Google Cloud Platform (GCP) are major cloud computing services that offer a wide range of machine learning tools. However, both services are not created equally. They do not support the same type of ML models; they do not offer the same set of data processing tools; and they do not support the same kind of UI or design.
When it comes to the cloud computing services, GCP is generally considered to be the best choice for machine learning projects. However, both Azure and GCP are great options that you should definitely try out. Depending on your needs, one of these two services may be a better option than the other. Let's talk about each of these services in detail and see what makes them so different.
Here Are The Differences
1. Google Cloud Platform does not have a wide range of pre-built models.
One of the biggest differences between GCP and Azure is that GCP does not offer any pre-built ML models. This means that you will have to download each and every model from their website, or build it yourself from an existing library. In addition to this, you will also have to make sure that the libraries' versions are up to date by downloading them from their GitHub page or from other sources.
When it comes to Azure, it has a wide range of pre-built models that can be used for various machine learning tasks. It also provides users with an easy way to check which specific ML models are available for different tasks, along with the code that you will need to use for these tasks - so that you do not have to spend too much time figuring out which libraries and techniques should be used in your project.
2. GCP offers a unique service called BigQuery.
Besides these pre-built ML models, GCP has its own unique data processing tool called BigQuery. This data processing tool is similar to AWS's RDS (Redshift) service - but it does not provide the same level of flexibility and functionality as RDS does. It works for various data types and various projects, but it is not as flexible as AWS's own cluster-based model. However, it is great for enterprises and larger companies which need a standard and reliable data processing tool for their machine learning projects.
3. GCP offers a wide range of data processing tools, but lacks in flexibility.
Since GCP does not offer any pre-built ML models (except BigQuery), it has to make up for the lack of flexibility in its other data processing tools - namely Big Data and Dataflow Tools. These tools provide users with a wide range of common data processing functions for various projects, but they do not provide the same level of flexibility as AWS's services.
This means that you will have to spend more time figuring out how each of these functions works, especially if you are used to RDS or Redshift functions. However, these tools are great for smaller companies and startups which do not need any flexible data processing functionality for their projects.
4. Google Cloud Platform is compatible with virtually any Cloud service - except Azure.
When it comes to compatibility, both GCP and Azure offer similar compatibility features. Both of these services are compatible with virtually every other cloud computing service out there, which means that you can easily create interactive web maps using Google Maps Data API, or build conversational AI using Microsoft's language processing services.
However, this does not mean that both of these services are compatible with each other. Both GCP and Azure provide cross-platform services for their own cloud computing providers - but neither of them supports the other's platform. That means that you will have to spend time figuring out which of these platforms you need to use to build your project, and this can cause some problems later on.
5. GCP has a wide variety of tools for data storage and processing - but Azure provides a wider range of tools.
When it comes to data storage and processing, both GCP and Azure offer users with similar functions for their projects. Both of these services support a wide range of database systems and storage tools, which means that you will be able to use them for various data types and various projects.
However, Azure also offers a wider range of tools and functions for this purpose. If your project requires the use of Machine Learning models or data processing tools, then Azure is the better option between the two cloud computing providers. GCP may be excellent for small companies and startups that do not involve any kind of ML model or data processing in their projects.
6. Google Cloud Platform does not provide users with many design tools.
One of the biggest differences between GCP and Azure is that GCP does not offer any design tools for machine learning projects (except for some simple dashboard features). If you are looking for a service that can help you build an interactive dashboard for your machine learning projects, then Azure is the better option. It has a wide range of dashboard features which can be used to build any kind of interactive graphical user interface for your machine learning project.
7. GCP does not offer very high-level programming languages.
If you are an expert in some programming language, then Azure may be the better option between the two cloud computing providers. Azure offers developers with a wide range of different programming languages for building their projects, including R, Python, Javascript and many more - compared to GCP's very limited list of languages (which includes Python and Java).
Both of these services allow users to code using various programming languages - but only Azure has more options when it comes to this category.
8. Google Cloud Platform does not use data centers all over the world.
If you are an enterprise or a startup, then you will probably want to use a service that has a wide range of data centers all over the world - instead of a service that does not have any data centers. GCP is not as great as Azure when it comes to the number of data centers - only 7 locations across the world compared to Azure's 19 global locations. If your company will be using Cloud services in different locations, then Azure is the best choice.
9. Google Cloud Platform does not provide users with high-level language implementations for their projects/tasks.
If you are an expert in some programming language (like C++), then you should probably use Azure over GCP while building your machine learning project or AI task. GCP only provides its users with low-level programming functions for various tasks - which makes it challenging to develop complex projects or AI tasks using this service.
Both GCP and Azure provide users with some kind of programming language - but Azure's is more user-friendly for those programmers who are used to C++.
10. Google Cloud Platform does not have a very strong AI Community
If you are a startup or a small startup company, then you will probably want to use a service that has an AI community for your projects/tasks. GCP's AI community is not as strong as AWS's, but it is also not as bad as Azure's. The community is better than Azure's, but not as good as AWS's.
Overall, both of these services provide users with a very strong community for building their projects/tasks - but Azure has more resources for this. If you need suggestions on how to develop your AI tasks or machine learning models, then the Azure community may be the best option between the two cloud computing providers.
11. Google Cloud Platform does not have much documentation available for developers.
If you are an expert in some programming language (like C++), then you should probably use Azure over GCP while building your machine learning project or AI task. GCP only provides its users with documentation for some simple tasks, along with describing how to use the service. This is not enough for most experienced developers, especially if they are used to using other high-level languages.
Both GCP and Azure provide developers with some kind of documentation - but Azure's is more user-friendly for those programmers who are used to C++.
12. Google Cloud Platform has a faster data transfer speed than Azure.
If you need to build a project that runs fast enough, then GCP may be the better between the two cloud computing providers. The data transfer speed on GCP is faster than Azure, which means that your project will be able to process/transfer its data in less time than with Azure.
13. Google Cloud Platform does not offer as many free trials as Azure.
GCP offers its users with a smaller number of free trials than Azure, which can be an issue for those startups and small companies that only need a limited range of resources for their projects. This limits your ability to access GCP's resources - and it is the same with Azure, which also limits the number of free trials it offers to its users. This can be a limiting factor, especially if you want to test out services for your projects.
Both of these providers offer free trials - but Azure has more free trial options for its users.
14. GCP does not provide developers with any tools/features like AWS's Lambda or Kubernetes.
AWS is known for providing excellent tools and features for all of its users, which is why it is the best among the world's leading cloud computing providers. GCP does not have any of these tools/features, which makes it difficult for developers to find good solutions for their tasks.
On the other hand, Azure does have some tools/features that are similar to AWS's Lambda and Kubernetes - but Azure has a better number of them than GCP.
15. Google Cloud Platform does not provide users with many project management features.
If you want to build some kind of project management software - like some kind of design system for your products - then GCP may not be the best between the two cloud computing providers. GCP does not provide any project management functions for its users, which means that you have to find a different service for doing this.
On the other hand, Azure is a very strong company when it comes to providing tools and features for building projects/tasks. Azure has the same number (or maybe more) project management features than GCP - which is not surprising, since Azure was built off of Amazon Web Services.
16. Google Cloud Platform does not offer users with many support options like Azure does.
If you are building AI applications or projects with GCP, then you may want to use a service that is known for its customer support - especially if you are building a startup or small company. If you are building AI applications or projects using GCP, then your company may become too dependent on this cloud computing provider.
On the other hand, Azure's customer support is more than great for its users. Azure has more customer support options than GCP - which gives us some kind of freedom to use this service without worrying about it.
