Microsoft Azure vs AWS vs GCP Comparison: Which Works Best for Serverless Architecture?

You’ve definitely heard about serverless computing or Functions-as-a-Service (FaaS) offerings from multiple cloud providers such as AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions. When evaluating Azure vs AWS vs GCP, which will work best for serverless architecture? While introduced not so long ago (with Amazon Web Services releasing Lambda in 2014, Microsoft launching Azure Functions in 2016, and Google Cloud Functions in 2018), these services are steadily growing in popularity. The reason for this is simple — they remove a ton of overheads and costs when running your workloads in the cloud computing industry.

According to a GitLab survey on AWS Lambda usage, 90% of software engineers use FaaS in their production environments. More importantly, serverless computing works great for developers and Ops engineers; AWS and Azure are used by large corporations, startups, and SMEs alike. Whether you compare AWS to its competitors or look at them as a unified cloud platform, the impact of serverless is undeniable.

Are you considering navigating the complexities of cloud migration? Choosing the right serverless architecture requires a deep understanding of your existing tech stack and future scaling needs. Contact SPsoft to consult with our cloud architects and find the perfect fit for your digital transformation journey!

Get in Touch

What Is Serverless Computing?

Serverless computing is the cloud computing resource allocation model where the cloud provider offers backend services on an as-used basis, rather than prepaid bandwidth or server capacities. In simple terms, you don’t pay for idling virtual servers. Instead, you upload the code that executes some function and configure various invocation scenarios (webhook, API calls, event triggers), so that you pay per invocation, not per instance.

All the IaaS and PaaS components of the cloud platform run under the hood, so there is no need to handle their scaling and configuration. This sounds like a great solution, allowing you to avoid hiring expensive DevOps engineers to manage your cloud solutions. Any developer can use AWS Lambda, Azure Functions, or Google Cloud Functions web interface to configure the service and run apps in FaaS, right?

Wrong.

What some software engineers and business executives fail to grasp is that FaaS is not a replacement for standard cloud computing services. It is a glue that allows building event-driven workflows, but it cannot replace standard cloud services — file storage, databases, Kubernetes clusters, monitoring and security features, etc. FaaS is not meant to be a replacement for common cloud services and is not built to run 24/7. AWS and Azure deliver high performance, but functions are executed in invocations, meaning that when they are needed — they run and then are immediately shut down to conserve resources.

Serverless Computing Use Case Scenarios

If serverless computing does not fit every use case, where does it fit in? It is a great choice for infrequent, stateless, asynchronous, and concurrent workloads with sporadic demand. This way, Azure tends to be a favorite for those already using Microsoft 365 or Microsoft Services to handle Windows Server logic.

If your app runs as a bundle of microservices, having an instance (or a single container) running a registration module 24/7 is wasteful — because new registrations do not happen non-stop. It is much more feasible to invoke the required function when a new user wants to register and pay for the time the service was actually needed, not 24/7.

Here are other notable examples of FaaS usage:

• Business logic. When your app uses a variety of cloud services and features, they can be invoked upon request, via webhooks, API calls, and other methods. This is especially useful for running microservice-based applications.
• CI/CD pipelines. When your software delivery process automatically provisions the required environments only when and where they are needed, you save considerably on the software development pipeline, as you don’t run idle servers.
• Stream data processing. Leveraging FaaS in complex corporate ecosystems with infinite message queues helps to streamline data processing at any scale.
• Batch jobs and scheduled tasks. Azure provides proper orchestration via Azure Logic Apps. Invoke infrastructure that enables intense parallel computation, network access, or IO, perform the scheduled tasks, and shut everything down to conserve resources.
• HTTP REST APIs for web apps. When your app needs to perform infrequent requests (the aforementioned user registrations and authentications, yeah.)
• Chatbots. These should always be operational and meet wildly fluctuating demand, so FaaS is the obvious choice.
• Mobile backends. Why run a mobile app backend non-stop, if you can execute all the required business logic once the users demand it? Build the required workloads on REST APIs and invoke them upon request, not run servers 24/7 just in case.
• IoT sensor input management. When an IoT sensor signals some disruption, the infrastructure required to handle the response is invoked, scaled to size, and then shut down. AWS is known for its robust IoT Core integration here.
• Multimedia processing. A set of functions required to transform the multimedia object into the needed format starts once a new file is uploaded.
• Database changes. Auditing the changes made to the database to ensure the quality standards are met.

In short, serverless computing is a perfect choice for irregular jobs with fluctuating resource consumption. But what about use cases when serverless doesn’t work?

When Should You Avoid Using Serverless Architecture?

Contrary to the previous point, serverless is not viable for stateful, long-term, resource-intensive jobs, like Big Data analytics or Azure Machine Learning model training. AWS is often more cost-effective for large-scale training using Elastic Compute Cloud (EC2) rather than Lambda.

The cases when you should avoid using serverless computing:

• Long-term tasks. Running your apps with FaaS might seem a great idea unless they grow in popularity rapidly. When the user numbers start to kick in, paying per invocation becomes very expensive.
• Resource-intensive jobs. Training a Machine Learning model is best done using conventional infrastructure, not FaaS.
• When monitoring is essential. With FaaS you have very limited observability, so monitoring of your operations is literally impossible. Every FaaS platform provides some basic monitoring capabilities, but they are quite rudimentary and non-configurable. You will need to use tools like Dashbird or X-ray to enable at least some visibility.
• When latency matters. FaaS means a cold start every time, so you need to account for it. The more complex the workflow, the bigger the code size, and the larger the functions — the longer it takes to invoke them. Python and Go launch the quickest, while C# and Java on Windows Server environments may take longer. If you need lightning-fast execution, you either need to keep your FaaS warm by invoking them at regular intervals — or avoid going for serverless architecture in the first place.
• Vendor lock-in. When you go for AWS Lambda, you have a very limited range of database options (namely, DynamoDB or Aurora) to work with. If you wish to use RDS or ElastiCache, you need to connect them via a VPC. Shifting to Microsoft Azure would require rebuilding functions from scratch. Thus, FaaS equals vendor lock-in.

To sum it all up, serverless architecture is not a “one size fits all” solution, by far. It works great as a glue for multiple aspects of your software delivery pipeline. More work can now be done with requests that bounce between various corners and components of your cloud ecosystem, but FaaS will never replace standard cloud instances in a wide range of cases.

Major Serverless Market Players — Azure vs AWS vs GCP

Let’s take a look at what the big three cloud vendors have to offer in terms of FaaS.

AWS Lambda

Now, there is a reason why we decided to talk about AWS Lambda in the first place. It is gradually becoming one of the pivotal scripting languages within the Amazon Web Services infrastructure. One peculiar reason behind the developers falling in love with it is that it lets you create unlimited scenarios to respond to a plethora of events happening all over your AWS infrastructure. The events triggered from the outside can also be managed with AWS Lambda’s help. Basically, every event that occurs invokes a Lambda function, which, in turn, calls a consecutive function, thus enabling and launching genuinely complex workflows.

AWS and Azure offer vast ecosystems, but AWS Lambda is particularly diverse in its compatibility with Node.js, Python, Java, C#, and Go. All these languages are capable of embedding code into other languages while supplying you with the wrappers to work with Lisp, Haskell, or produce legacy C++ that will run on Lambda.

The choice between AWS and Azure often comes down to the existing ecosystem. AWS also offers AWS Outposts for those needing hybrid cloud capabilities that compete with AWS’s own public cloud performance. Lambda stands out with AWS Step Functions, a tool that lets you glue stateful events with stateless Lambda.

Another advantage of Lambda is its web interface that is clean as a whistle, which simplifies the task of configuring any pipeline you might need. No more text files to write configurations in, as Lambda gives you several web forms that will let you harness the full potential of the Amazon cloud while writing no more than just a few lines of code. Nonetheless, bear in mind that testing the new functionality can be a tall mountain to climb. Get ready to configure the API gateway respectively and open specific firewalls for the new functions to be tested comprehensively.

Lambda empowers you to build its workflows in the “events source to destination” direction while configuring the response scenarios

User-friendliness is another virtue that AWS Lambda can boast of. If you’re a Lambda user, get used to getting a lot of explanations and warnings popping out on your screen to prevent you from hitting the underwater reefs of its functions. For instance, when creating a function that requires open-source code from an external library, you’ll get a corresponding message in your browser. Before Lambda, the programmers were expected to know this all themselves. Now that you have the information in advance, you save yourself a lot of time and effort. 

Yet, let’s acknowledge that Lambda is not the only serverless offering from AWS, as there are lots of products that might relieve you from the tedious task of managing your server. Elastic Beanstalk is one of them. With its help, you can upload the code, as it will take it to the webserver, and automatically handle load balancing and scaling. However, the feature that yet again makes Lambda stand out from the crowd is AWS Step Functions, a tool that lets you glue stateful events with stateless Lambda. 

Any business logic will find this feature crucial and incomparably advantageous. Imagine reloading the client’s info from the database every time the page is reloaded. How easy would you find this task if there was no AWS Step Functions feature? The ordinary Lambda would have you do it every time the customer reloads a page. The Step Functions tool gives you the bragging rights to do it just once and forget about it. Hence, it seems like AWS Lambda has quite something in store for those willing to make their business run smoothly and efficiently.

Google Cloud Functions

Google has been providing “serverless” capabilities long before FaaS became a thing. With the release of Google App Engine, Google enabled developers to concentrate on writing code instead of configuring and managing the environments to run it. With Google Cloud Pub/Sub, you don’t need to handle the message queuing, only to write the code for the event producer and the consumer. 

Google Firebase is a database with superpowers, which allows you to mix the database layer with JavaScript functionality while delivering the data to your clients. Firebase takes the idea of serverless to the extreme, handling all server-side business logic, including data storage and authentication. You can really run an app with a bit of client-side HTML/CSS, and Firebase. Firebase code is written in JS, so it will run locally on Node.js. You can handle any business logic this way because there are multiple Node libraries for doing exactly this. In addition, you will enjoy the benefits of working with isometric code, running the same for the client, the server — and now the database.

Finally, Google Cloud Functions provides event-driven computing, which blends various GCP products into a powerful and flexible mix. It allows embedding custom Node.JS code throughout the whole GCP ecosystem. However, while AWS and Azure services support a wide range of services, Google Cloud Functions currently focuses heavily on Node.js and pure JS for its serverless triggers. Support for other languages is announced. However, Google Cloud Functions has to work through the REST API to even interact with Google Docs. Additionally, your code has to be stateless and every request can run for a limited time only.

Microsoft Azure Functions

Microsoft Azure Functions is Microsoft’s answer to FaaS offerings from AWS and GCP. One of its biggest applications is integration with Office products, which are slowly but steadily moving from desktop apps to the cloud. This way, HTML and web interfaces are not the only way to the Azure cloud — much can be achieved using Excel spreadsheets and Word documents.

Azure Logic Apps provides you with the ability to create workflows using built-in connectors for Azure and third-party apps like Microsoft 365, Twitter, and Salesforce. Instead of worrying about the semantics and syntax of the code, you fill out the forms that enable you to link stateful code with stateless functions. If your businesses already using Microsoft products want to move from desktop to the cloud, Azure is the natural choice. 

Azure also offers Durable Functions, which allows creating and managing stateful functions. For database management, Azure SQL Database and Azure CosmosDB provide a central nexus for data. If you are already using Microsoft SQL Server, the Azure Hybrid Benefit allows you to pay less with Azure by repurposing existing licenses.

Comparing Serverless PaaS: AWS vs Azure vs GCP

While each of the big three FaaS offerings has a different set of features, they all have a set of characteristics that can be easily compared. These include pricing per invocation, programming languages supported, trigger types, concurrency, execution time length, and more. The table below showcases these parameters.

Regarding trigger types, Lambda and Azure work via APIs. With Lambda, you can use API Gateway, dynamic triggering from DynamoDB, or files-based triggering from Amazon S3. For Azure Functions, you have web API triggering, scheduled invocations, and event-based triggers via Azure Event Hub or Azure Storage. Besides, Azure excels in hybrid cloud environments through Azure ARC and Azure Stack, allowing for running Azure services on-premises.

For Google Cloud Functions, you have a wide range of trigger types, described in their documentation. The key benefit here is that you can integrate your FaaS with any Google Cloud service, and use Cloud Pub/Sub or API callbacks, for instance.

Concerns about Using FaaS in Real Life

While this all looks fine and dandy, the real question is whether it works this way in real life. Regarding this question, the answer is… not so much.

AWS vs Azure is a common debate. AWS Lambda runs best, being the oldest and most polished. Azure may feel like Microsoft at its most traditional — sometimes, they freeze or have a lower number of runtimes. However, for those invested in Microsoft products, the integration with Microsoft tools like Azure SQL makes the Azure virtual environment much more cohesive. AWS and Azure provide different levels of cost management tools, but Azure is easier for companies that have already invested in Microsoft infrastructure.

Google Functions present yet another level of challenges, due to the limited application scenarios. Besides, at times they experience deployment and scaling issues, which are quite hard to debug and resolve.

Conclusions: Which One to Choose?

As you can see, all three cloud service providers offer robust serverless computing. AWS and Azure are comparable in power, but the choice between AWS and Azure depends on existing dependencies. If you use Windows Server and SQL Server on-premises, Azure is the best cloud provider. If you want the market leader with the most polished FaaS, AWS is an option.

We hope you now learn the differences between Azure and its competitors. Serverless architecture is a “glue” for your hybrid cloud solutions, and choosing a cloud provider should be based on which “glue” fits your private cloud or public cloud strategy best.

Are you ready to optimize your cloud spend? Effective cost management in the cloud starts with the right architecture. Talk to SPsoft about how we can help you implement serverless solutions that reduce overhead while maximizing performance across AWS and Azure!

Contact Us

FAQ