Following the series of posts that we are publishing on our blog about Serverless architecture (serverless), we decided to bring a comparison of the main providers of this type of technology: Amazon Web Services (AWS), Google Cloud and Azure, from Microsoft.
The idea is to show why, despite promising practically the same things, some stand out in the market.
Check it out in detail below!
Key Differences Between the Serverless Architecture of AWS, Google Cloud and Microsoft Azure
AWS Lambda is better than others as the programming language is quite diverse and Lambda provides more versions and more types of supported languages than other serverless architecture providers.
Stateful function support
AWS Lambda does not support this, but it can access AWS storage services where Azure provides this capability, and Google Cloud does not have this element at this time.
Granular Identity and Access Management (IAM)
Identity and Access Management (IAM) policies can be attached to Lambda. While RBAC is supported in Subscription and Roles are inside Azure. Google Cloud has not publicly released anything related to this.
AWS uses S3 and DynamoDB for full stateless persistent storage, while in the Azure environment variables can be set so they can be used in functions.
Azure stores in blob storage.
Google Cloud provides Cloud Storage, Cloud Datastore, Cloud SQL for the same.
On AWS, deployment is done in zip format.
The zip is loaded into Lambda/S3. While on Azure Git, dropbox, visual studio, Kudu console, etc. can be used for deployment.
In Google Cloud CLI, Zip upload, Cloud Storage or Source and built-in web editor are used for this purpose.
Maximum number of functions
On AWS Serverless and Azure Serverless, there is no limit to a maximum number of roles, while on Google Cloud the limit is up to 1000 per project.
See the superiority of AWS, check out the applications it offers
Highlights below are the top AWS Serverless computing applications:
Web application and backend
Serverless and backend web applications can be built using AWS Lambda, Amazon API Gateway, Amazon S3 and Amazon DynamoDB and would help handle requests from web, mobile, IoT and chatbots.
Example: Mobile backend for social media app.
Many different variants of real-time data processing systems can be built in AWS Serverless Computing. You can use the following for data processing.
- AWS Lambda;
- Amazon Kinesis;
- Amazon S3;
- Amazon DynamoDB
- Creation of image thumbnails;
- Social media streaming data analysis;
- Discover how to build serverless applications on AWS;
- Explore serverless application development services.
→ Also read: AWS Serverless: The Benefits of Serverless Architecture.
How about, can we show you the differences between AWS, Google Cloud and Azure Serverless architectures? To continue learning about the topic, Download the Serverless Computing eBook now!