A classic server usually features a single server that needs to be maintained in-house and has a variety of responsibilities within its codebase. This one server can be responsible for several different functions.
What is serverless architecture?
In a serverless architecture, functions are used to trigger an event. For example, Google has Firebase cloud functions to trigger a serverless event-driven architecture, whereas AWS has Lambda functions for the same task. We already saw the comparison between AWS and GCP.
Here in this article, we will learn about Firebase cloud functions and AWS Lambda. Also, we will look at the difference between Firebase Cloud functions and AWS Lambda.
Serverless architecture is essentially a microservice architecture, meaning you build your infrastructure from different components in the cloud and send them to managed servers.
What is Firebase Cloud Function?
Firebase Cloud functions allow you to execute backend code automatically in response to events triggered or caused by HTTPS requests and Firebase features. The code is stored in a managed cloud environment, and we do not need to manage and scale servers.
For example, when an image is uploaded to Firebase Storage, a function can be triggered to build a thumbnail, or when a node in the Realtime Database is destroyed, a function can be fired to clear some user data.
When you create and deploy a function, Google’s servers instantly begin to handle it. The function can be invoked with an HTTP request, or the server will listen to events, and the function will be triggered if any event occurs.
Google responds to changes in load by swiftly scaling the number of virtual server instances (up and down) required to run your function. Each function runs in its environment, with its configurations.
Advantages of using Firebase Cloud Functions
- No maintenance required: Firebase automatically manages computational resources to fit your users’ consumption patterns. So you never have to bother about passwords, server settings, providing new servers, or decommissioning old ones. So technically, there is no extensive maintenance required.
- Auto Scale: When upscale and downscaling a service is one of the most challenging and essential tasks. However, with Firebase cloud functions, developers do not have to worry about growing their backend services. The Firebase cloud function has automatic scaling, which implies that the server capacity is dynamically scaled based on the application’s needs.
- Pay as you go: Firebase cloud functions’ pricing policy is user-friendly and easy to understand. Moreover, it works on a pay-as-you-go pricing model. Which means you only pay for what you use.
What is AWS Lambda Function?
AWS Lambda is a serverless compute service that allows you to run code without providing or managing a server. AWS Lambda allows you to operate your application or backend service with no administration.
Upload your code to Lambda, and it will execute it. AWS Lambda function can be referred to as a code that we run in AWS Lambda. We can write Lambda functions in Java, Python, C#, Go, PowerShell, Ruby, and Node.js.
You can also think of a function as a container containing information such as the name and description of the function and resource requirements, and entry points. Just like Firebase functions, AWS lambda functions also need to be triggered.
Functions can be triggered by uploading a document in S3, updating a table in DynamoDB, or HTTP request.
Advantages of using AWS Lambda functions
- Easy to scale: AWS Lambda easily scales the infrastructure without additional configuration. It decreases the amount of operating work required. It automatically downscales and upscales as per the requirements.
- Integration: You can easily integrate AWS Lambda with other AWS services such as CloudWatch, DynamoDB, CodeCommit, S3, API gateway, etc.
- No upfront fees & pay as you go pricing: There is no need to make an initial investment. You pay for the memory used by the lambda function, and there is no cost based on the number of queries, making it cost-effective.
- Highly secure & fault-tolerant: AWS lambda is a highly secure service. All roles and security policies are defined using AWS IAM. Also, It provides fault tolerance for both the services that run the code and the function. Therefore, you do not need to worry about the application going down.
Firebase Cloud Function vs AWS Lambda
The main difference between Firebase Cloud Function vs AWS Lambda is pricing. Both Google Cloud and AWS lambda functions work on a pay-as-you-go pricing model. For example, after 1 million free requests in AWS, you are charged $0.20 per one million requests. After 2 million free requests, you are charged $0.40 per one million invocations for Google cloud functions.
| Provider | Free Monthly Duration (GB-seconds) | Free Monthly Requests | Cost of Each Additional 1 Million Requests | Cost of Each Additional 1 GB-second | Duration is Rounded to the Nearest |
| Firebase Cloud Function | 400,000 | 1 Million | $0.20 | $0.000016 | 1ms |
| AWS Lambda | 400,000 | 2 Million | $0.40 | $0.0000125 | 100ms |
Both firebase cloud functions and AWS Lambda are pretty similar. They are designed to trigger an event-based architecture with auto-scaling capabilities. Here are some of the major differences between them:
Number of functions
With AWS Lambda, you get an unlimited number of functions, but in Google cloud functions, you have a limited number of functions which is 1,000 functions per project.
Supported languages
AWS supports more languages than Google cloud functions. For example, you can use Python, Java, Go, C#, Node.js, and Ruby with AWS, whereas Google cloud functions support C#, F#, Go, Java, Node.js, Python, Ruby, Visual Basic.
Even if your favorite language is not supported, you can create a custom runtime environment using custom deployment packages or AWS Lambda Layers for Lambda and a custom docker image for GCP.
Free requests
With AWS, you get 1 million lambda requests for free, and with Google cloud functions, you get 2 million requests for free.
Concurrency
Instances can process only one request at a time. So while processing a request, if another request is received, a second instance is launched to handle the new request. So concurrency is the number of concurrent functions that can run simultaneously. AWS has a soft limit on concurrency, 1000 per region, whereas there is no limit in firebase functions.
Execution timeout
Most of the time, the function is processed in seconds, but it may take extra time depending on the workload. For example, AWS has a timeout of 15 minutes, and GCP has 9 minutes.
Final words
Both Google Cloud functions and AWS lambda functions have some strong points compared to others. You can choose any one of them, which will empower your serverless architecture.
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Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.