AWS Lambda vs. Azure Functions: battle of the serverless titans

Serverless computing brings unlimited scalability and agility to various types of businesses, from startups to large corporate bodies. This is why Amazon and Microsoft have pulled in resources to develop two of the world's most extensive serverless cloud services, AWS Lambda and Azure Functions.

According to Global Market Insights, the serverless architecture market raked in a whopping $9 billion in 2022 and is expected to grow at over 25% CAGR from 2023-2032.

AWS Lambda, which spearheaded the Function as a Service (FaaS) application model in 2014, and Azure Functions, Microsoft's equivalent in the Azure cloud, are two major players in this field and will probably dominate for a very long time to come. While the basic idea behind these offerings is the same, it's important to understand the significant differences between these two services to help you determine which is best for your project.

This article will explore the advantages, disadvantages, and differences between these serverless services.

What is AWS Lambda?

AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), which allows developers to run code in response to certain events, such as Amazon S3 data changes, updates to a DynamoDB table, or an HTTP request. According to the book Serverless Applications with Node.js: Using AWS Lambda and Claudia.js, written by Slobodan Stojanovic and Aleksandar Simovic, “Lambda is AWS’s serverless computing container that runs your function when an event trigger occurs. It gets scaled automatically if many events trigger the function at the same time.”

It means that AWS Lambda lets you run your functions without a server, automatically activating them when a specific event happens. It can also automatically handle running your function multiple times if many such events occur simultaneously.

What are the core features of AWS Lambda?

Let’s closely examine some of the features of AWS Lambda.

Automatic scaling

AWS Lambda can automatically scale your functions in response to incoming traffic. It can run your function in parallel and process each trigger individually, allowing it to handle thousands of events simultaneously. It eliminates the need for manual intervention to allocate resources based on the load, making it highly scalable and responsive.

Stateless and stateful processing

By default, Lambda functions are stateless, meaning each function execution is independent and does not retain any state information between runs. However, for scenarios where stateful processing is required, AWS Lambda can be configured with AWS Lambda Layers and Amazon EFS to retain state information between function executions.

Integrated security model

AWS Lambda provides robust security mechanisms to safeguard your functions. It runs your code in an isolated environment with its own resources. You can also configure your Lambda functions to access resources within a Virtual Private Cloud (VPC) for enhanced network isolation and security. Also, AWS Lambda integrates with AWS Identity and Access Management (IAM) to control access to your functions.

Multiple language support

AWS Lambda supports multiple programming languages, including Node.js, PowerShell, Python, Ruby, Java, Go, and .NET Core, allowing developers to use their preferred coding language. Developers are also provided with a Runtime API that allows them to use additional programming languages.

Built-in fault tolerance

AWS Lambda has built-in fault tolerance features that maintain compute capacity and infrastructure reliability. It monitors function health and automatically replaces unhealthy instances. Logging and monitoring are provided through integration with Amazon CloudWatch, allowing you to track function executions and troubleshoot issues. AWS Lambda also supports automatic retries for failed function executions, enhancing the reliability of your applications.

Resource specification

When creating a Lambda function, you can specify the amount of memory allocated to it. AWS Lambda then allocates proportional CPU power, network bandwidth, and disk I/O. It allows you to optimize your functions based on their resource requirements, ensuring efficient utilization of resources.

Easy integration

AWS Lambda can be easily integrated with other AWS services, enabling you to build comprehensive, event-driven applications. For example, you can use Lambda to process events from Amazon S3, Amazon DynamoDB Streams, Amazon Kinesis, and more. This seamless integration allows you to leverage the entire AWS ecosystem to enhance your serverless applications.

Deployment

Deploying code to AWS Lambda is straightforward. You package your code and any dependencies into a deployment package and upload it to Lambda. You can manage and deploy your Lambda functions using the AWS Management Console, AWS Command Line Interface (CLI), AWS SDKs, or AWS CloudFormation. This flexibility simplifies the deployment process and accelerates development cycles.

Pay-as-you-go pricing

The pay-as-you-go (PAYG) payment model works by charging users based on how much of the services they use. This pricing model is particularly popular in the SaaS startup industry and large enterprises like AWS Lambda. The Benchmarks report by OpenView also mentions a shift in pricing models, with a 30% increase in startups offering a usage-based pricing model.

With AWS Lambda, you are charged based on the number of requests and the duration of your code execution. You don’t pay for idle time, making it a cost-effective solution for varying workloads. AWS also offers a free tier for Lambda, allowing you to execute numerous requests and compute time each month at no cost.

Advantages of using AWS Lambda

Here are some of the advantages of using AWS Lambda:

Serverless approach

If you have ever managed a physical server for your business, you most certainly are already acquainted with the high cost of setting up and maintaining a server. Let's not forget the extra hardware, software licensing, and IT staff costs. This is a problem that serverless computing aims to solve.

Lambda functions are automatically provisioned and scaled based on demand, so you don't have to worry about managing servers or provisioning capacity.

Continuous scaling

Lambda manages the scaling of your functions precisely by running event-initiated code in parallel and processing each event individually, allowing for seamless adaptation to varying loads.

Develop and deploy new features quickly

Features can be developed and deployed faster, as you can write and upload your code as a ZIP file or container image. This can help you quickly respond to changing market conditions and meet your customers' needs.

Never get stuck with AWS Lambda’s extensive developer support

Lambda supports developers through various tools and services like the AWS Serverless Application Repository, AWS Serverless Application Model, and integrations with various integrated development environments (IDEs) such as AWS Cloud9, AWS Toolkit for Visual Studio, and AWS Toolkits for Azure DevOps. This support empowers developers to take full advantage of the breadth and depth of AWS when building serverless applications.

Disadvantages of using AWS Lambda

Here are some of the disadvantages of using AWS Lambda:

Cold starts

When a Lambda function is invoked for the first time, it takes time for AWS to spin up a container and load the function code. The result? Latency issues, especially for functions that are invoked infrequently.

Computational constraints

Lambda has limits on its memory and CPU usage, making it unsuitable for workloads that require a lot of processing power.

Complex orchestration

It can be difficult to orchestrate Lambda functions to implement complex workflows. This is because Lambda functions are stateless and ephemeral.

Concurrent execution limits

AWS Lambda has a limit on the number of concurrent executions that can be running at the same time. This can be a limitation for workloads that experience sudden spikes in traffic.

Real-world use cases

AWS Lamba is used and trusted by large enterprises worldwide. Below are some of the companies that use AWS Lambda:

• Netflix: Netflix stands as one of the leading online streaming services globally, supplying close to 7 billion hours of video content to approximately 50 million users across 60 nations every quarter. Netflix uses Lambda to process media files in over 50 formats. It allows them to deliver content to their customers in the best possible format for their devices. Netflix also uses AWS Lamba to “build rule-based self-managing infrastructure and replace inefficient processes to reduce the rate of errors and save valuable time.” See case study.
• Square Enix: Square Enix uses Lambda to run image processing for its Massively Multiplayer Online Role-Playing Game (MMORPG). This allows them to handle spikes in traffic and reduce infrastructure costs. See case study.
• The Seattle Times: Established in 1896, The Seattle Times is a family-owned news media that caters to the Pacific Northwest region. The Seattle Times uses Lambda to resize images for viewing on different devices. This improves the user experience for their website visitors. See case study.

What is Azure Function?

Often seen as a strong competitor of AWS Lambda, Azure Functions, offered by Microsoft, is a serverless compute platform that allows you to run code without provisioning or managing servers. You can use Functions to build web APIs, process database changes, send emails, and more. Functions execute your code when an event occurs and shut down the compute infrastructure. Also, like AWS Lambda, you only pay for the resources that you use. One thing to note in Azure is it has three pricing plans: Consumption, Premium, and App service. The Consumption plan is the only one with “serverless” capabilities.

What are the core features of Azure Functions?

Let’s examine the main features of Azure Functions.

Azure Durable Functions

Durable Functions is an extension of Azure Functions that lets you write stateful functions in a serverless environment. It allows for the definition of workflows, where you can chain functions together in a sequence, wait for external input, or set timers.

Azure bindings and triggers

In Azure, triggers are what cause a function to run, and bindings are declarations that connect the function to another resource. For example, a timer trigger might run a function at a set schedule, and a blob binding might read or write data to Azure Blob Storage.

Consumption & custom plans

In the Consumption Plan, resources are added dynamically to handle the load, and you only pay for the time your functions are running. It’s an ideal plan for sporadic, unpredictable workloads as it offers cost savings and eliminates the need for resource pre-allocation. Azure Functions also supports App Service Plan and Premium Plan, which offer more advanced features like VNET integration, enhanced scaling options, and better performance. These are suitable for more demanding, consistent workloads.

What dependencies do you prefer?

Azure Functions supports NuGet and NPM, so you can use your favorite libraries and frameworks in your functions.

Welcomes fans of CI/CD

Azure Functions supports continuous integration and continuous delivery (CI/CD), so you can easily set up automated deployments and testing. For example, a developer can use Azure Functions with GitHub Actions to automate the deployment of their applications to Azure.

Don’t worry. Your application is secure!

Azure Functions provides multiple levels of security, including support for OAuth providers like Azure Active Directory, Facebook, Google, and Twitter. It also integrates with Azure Key Vault for secure storage and management of secrets, keys, and certificates.

Do you like Opensource?

The open-source nature of the Azure Functions runtime makes it possible for developers to contribute to the development of Functions and customize it to meet their specific needs. This is important because it gives developers more control over their serverless applications.

Monitoring and diagnostics

Azure Functions integrates with Azure Monitor and Application Insights, providing detailed insights into the function's operation, allowing developers to trace function execution, monitor performance, and troubleshoot errors.

AI integrations

Azure Functions can easily integrate with Azure Cognitive Services, allowing developers to incorporate AI capabilities such as vision, speech, and language understanding into their applications, enhancing functionality and user experience.

Advantages of using Azure Functions

What are the advantages of using Azure functions?

Easy to use

Azure functions are quite easy to use. You don’t even have to have experience with cloud computing to get things up and running. You can use various languages and frameworks to develop your functions and deploy them in just a few clicks.

Integration with other Azure services

Azure Functions can be easily integrated with other Azure services, such as Azure Storage, Azure SQL Database, and Azure Cosmos DB. It gives you a lot of flexibility in how you build your applications.

DevOps is always welcome

Azure Functions supports integration with various CI/CD tools, allowing for automated testing and deployment of your functions.

Scalability

Azure Functions can scale automatically to meet the demands of your application. It means you don't have to worry about overloading or slowing your application.

Disadvantages of using Azure Functions

What are the disadvantages of using Azure Functions?

Again, the serverless cold start problem

Azure Functions are serverless, meaning they are not always running. When a function is triggered, it must start up before executing. It can lead to a cold start time, which is the time it takes for the function to start up and execute the first request. Cold start times can be especially long for functions that use many resources.

Limited resources

Like AWS Lambda, Azure Functions is also limited in terms of resources, such as CPU, memory, and storage. IT can be a problem for functions requiring complex or resource-intensive tasks.

Timeout limits

There are maximum execution time limits for functions and long-running tasks may be terminated if they exceed these limits.

VNET integration limitations

Integrating Azure Functions with Virtual Networks (VNET) can be complex and may have limitations, impacting network isolation and security configurations.

Real-world use cases

Leading enterprises use Azure Functions for very important business processes. Let’s take a look at some of Azure Functions real-word use cases:

• Netflix: Netflix uses Azure Functions to process and analyze streaming data to detect and prevent fraud. See report.
• McKinsey: McKinsey utilized the Microsoft Azure IoT hub to build and introduce a digitally empowered resident application. This innovation is unprecedented in offering health-monitoring solutions and presents a new business capability concentrated on digital advancement and transformation. See report.
• NASA: National Aeronautics and Space Administration (NASA) is a United States government agency responsible for the nation's civilian space program and for aeronautics and aerospace research. NASA uses Azure Functions to process and analyze satellite data to track weather patterns and monitor climate change. See report.

AWS Lambda vs. Azure Functions: key differences and comparison

Let's take a look at the comparison of these services:

Scalability

Both AWS Lambda and Azure Functions automatically scale based on incoming traffic and can handle millions of requests per second. Although both platforms offer automatic scaling, their processes for achieving this are quite different.

AWS Lambda uses a concurrency model to manage the number of simultaneous executions. For instance, with a concurrency limit of 100, Lambda ensures that no more than 100 instances of your function execute simultaneously. It can also be referred to as “sharding” in AWS.

Furthermore, you can configure maximum concurrency for a function, and Lambda maintains that concurrency level by creating and destroying execution environments as needed. The granular scaling capabilities of each function are scaled independently, allowing different functions within the same application to scale based on their individual demands.

Meanwhile, Azure Functions employ a "Scale Controller" method that monitors the rate of incoming events and adjusts the number of function instances accordingly. For example, if a sudden spike in requests occurs, it will add more instances to handle the load efficiently.

Azure also uses the Azure App Service for infrastructure management and scale within a defined limit.

Reviews generally say that AWS Lambda has better auto-scalability capabilities than Azure Functions, with Lambda having a 9.0 rating (296 responses) against Azure Functions with an 8.5 rating (12 responses) on G2. If there’s anything to trust for your choice, its certainly customer reviews.

Configurability

AWS Lambda provides a high level of configurability, allowing you to fine-tune various aspects of your functions. For example, you can set resource allocation (CPU, memory) on a per-function basis, gaining control over performance and cost. You can also define environment variables to store configuration settings, making it easy to adapt your function's behavior without modifying code.

Azure Functions also offer configurability but with a slightly different approach. Azure Functions uses a consumption-based pricing model, which automatically scales resources based on demand. While this can simplify cost management, it may offer less control over resource allocation compared to AWS Lambda.

Both AWS Lambda and Azure Functions allow you to configure triggers, timeouts, and logging settings, but the specific implementation details differ. AWS Lambda, for instance, provides VPC (Virtual Private Cloud) integration, which allows you to configure network-specific settings for your functions. Azure Functions offers similar capabilities with its VNet integration. However, the way you configure these network-related settings may vary, depending on your familiarity with each cloud provider's ecosystem.

Cold starts

At this point, both services should have cold starts as a nickname (lol), since this is a pretty common occurrence between the two. However, they are different from each other.

Cold start times in AWS Lambda are shorter than those of Azure Functions. For instance, Azure Functions cold start times could last for several seconds and often require a cold start after a 20-minute inactivity time frame, which is in huge contrast to Lambda, with a cold start time of usually not more than 2 seconds.

HTTP integration

HTTP integration in AWS Lambda and Azure Functions allows these serverless platforms to receive and process HTTP requests directly. However, a key difference lies in how they handle these integrations.

AWS Lambda often requires setting up an API Gateway (formerly with Amazon API Gateway, now with Elastic Load Balancer) to manage HTTP traffic, adding additional configuration layer and cost.

In contrast, Azure Functions natively supports HTTP triggers out-of-the-box, simplifying the process by directly handling HTTP requests without the need for a separate service, making it a more streamlined choice for HTTP integration. A clear winner here is Azure Functions.

Code reusability

AWS Lambda provides code reusability through its Lambda Layers feature. With Lambda Layers, you can package and share common code, libraries, or dependencies across multiple Lambda functions. For instance, if you have a set of utility functions that are used by several Lambda functions within your AWS ecosystem, you can create a Lambda Layer containing these utilities.

Azure Functions offer code reusability through a different mechanism called Azure Functions Proxies. Proxies allow you to define routing and composition rules for your functions. This can be particularly useful for creating a single entry point that aggregates multiple functions, encapsulating code that might be reused in different contexts. For instance, you could create a proxy that routes requests to different Azure Functions, thus encapsulating code logic for authentication, logging, or error handling.

Workflows

AWS Lambda is known for its flexibility in integrating with various AWS services, such as AWS Step Functions, to create intricate and serverless workflows. For example, you can set up a Lambda function to automatically resize and compress images uploaded to an S3 bucket, then trigger another function to update a DynamoDB database with the metadata.

Azure Functions excels at simplifying workflow orchestration through Azure Logic Apps. While you can certainly create workflows directly within Azure Functions, Logic Apps provide a visual, low-code approach for designing and managing complex workflows. For instance, you can use Logic Apps to create a workflow that monitors an Azure Blob Storage container for new files, processes them with an Azure Function, and sends notifications via email or Slack.

Hosting

In AWS Lambda, hosting is abstracted entirely from the user. You simply upload your code as a function, and AWS provides the necessary resources to run it. Lambda allows you to define the amount of memory and execution time for your functions, and you are billed based on the number of invocations and the total execution time.

Azure Functions provides more flexibility in hosting options. You can choose from the Consumption, Premium, or Dedicated (App Service) Plan. Keep in mind that only the Consumption plan comes with the Severless functionality.

Pricing

Since both platforms are Serverless, they both run on a pay-per-usage model. Their main components for cost calculations are the pay-per-call and pay-per-use GB/secs. The two main factors for calculation include: Invocation and Execution. Let’s look at each service pricing:

AWS Lamba pricing

The price per invocation is $0.20 for the first million invocations per month, and for the first 6 billion GB-seconds/month, you pay $0.0000166667 per GB-second. Lambda also offers a free tier that includes 1 million free monthly requests and 400,000 GB-seconds of compute time/month.

This free tier is available indefinitely and does not automatically expire after a 12-month AWS Free Tier term.

Pricing also varies based on the memory size you choose for your function, ranging from $0.00001667 for 128MB to $0.00130001 for 3008MB per GB-second. See the pricing page for more details.

Azure Functions

Here, you pay $0.20 per million executions in a month, which is similar to what AWS Lambda offers. You also get a free grant of 1 million requests for the first 400,000 GB-seconds per month on the Consumption plan.

Azure has set a baseline memory allocation of 128 MB for its functions, so even functions that utilize less memory will be charged based on this minimum limit​. See the pricing page for more details.

Conclusion

This article explores the features, pros, cons, and comparison between two of the largest serverless giants in the tech world. Anime fans like myself can even compare the two competitors to Naruto and Sasuke, each with unique strengths and styles. Although AWS Lambda seems to be leading in this battle, who knows what Microsoft Azure has cooking up in the coming years? If you're reading this article's conclusion, congratulations!! I hope this was an exciting read for you.

Deciding which service to use ultimately boils down to what you are working on and which service you think is best for you.

References & resources

• Slobodan Stojanovic and Aleksandar Simovic, “Serverless Applications with Node.js: Using AWS Lambda and Claudia.js,” 2019.
• John Chapin and Mike Roberts, “Programming AWS Lambda: Build and Deploy Serverless Applications with Java,” 2020.
• Rahul Sawhney, “Beginning Azure Functions: Building Scalable and Serverless Apps” 2019.
• “Benefits of Lambda” - AWS Docs.
• “Azure Functions Overview” - Microsoft Learn.