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Case studiesServerless 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.
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.
Let’s closely examine some of the features of AWS Lambda.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Here are some of the advantages of using AWS Lambda:
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.
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.
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.
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.
Here are some of the disadvantages of using AWS Lambda:
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.
Lambda has limits on its memory and CPU usage, making it unsuitable for workloads that require a lot of processing power.
It can be difficult to orchestrate Lambda functions to implement complex workflows. This is because Lambda functions are stateless and ephemeral.
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.
AWS Lamba is used and trusted by large enterprises worldwide. Below are some of the companies that use AWS Lambda:
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.
Let’s examine the main features of Azure 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.
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.
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.
Azure Functions supports NuGet and NPM, so you can use your favorite libraries and frameworks in your functions.
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.
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.
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.
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.
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.
What are the advantages of using Azure functions?
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.
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.
Azure Functions supports integration with various CI/CD tools, allowing for automated testing and deployment of your functions.
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.
What are the disadvantages of using Azure Functions?
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.
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.
There are maximum execution time limits for functions and long-running tasks may be terminated if they exceed these limits.
Integrating Azure Functions with Virtual Networks (VNET) can be complex and may have limitations, impacting network isolation and security configurations.
Leading enterprises use Azure Functions for very important business processes. Let’s take a look at some of Azure Functions real-word use cases:
Let's take a look at the comparison of these services:
| Features | vs AWS Lambda | vs Azure Functions |
|---|---|---|
Supported languages | Python, Java, Node.js, Go, Ruby, C# | JavaScript, Java, Python, C#, F#, PHP, TypeScript |
Code sharing | No | Yes |
Local debugging | Local only | Local and remote |
IDE support | Yes | Yes |
Runtime environment | Done with Amazon Linux | Done with Azure Stack |
Triggers | Event-driven (e.g., S3 uploads, HTTP requests) | Event-driven (e.g., Azure Storage, Event Grid) |
Monitoring & logging | AWS CloudWatch | Azure Monitor and Application Insights |
Dev tools | AWS CLI, AWS SDKs, AWS SAM (Serverless Application Model) | Azure Active Directory (Azure AD), OAuth, Key Vault |
Concurrency control | Can be configured with reserved concurrency | Concurrency control through "Scale Controller" |
Billing | Pay per request and execution time | Pay per request and execution time |
Deployment methods | AWS Management Console, AWS CLI, SDKs, Infrastructure as Code | Azure Portal, Azure CLI, Azure DevOps, IaC (ARM template) |
Hybrid Deployment | AWS Outposts and Wavelength for edge computing | Azure Stack for hybrid scenarios |
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.
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.
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 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.
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.
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.
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.
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:
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.
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.
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.