AWS Lambda vs Google Cloud Functions

AWS Lambda is a computer service that lets you run code without provisioning or managing servers. It executes your code only when needed and scales automatically. It's provided by Amazon as a part of the Amazon Web Services. It was introduced in November 2014. AWS Lambda supports securely running native Linux executables via calling out from a supported runtime such as Node.js.

On AWS Lambda, the code you run  is called a “Lambda function.” After creating the Lambda function, it's always ready to run as soon as it's triggered. Each function includes your code as well as some associated configuration information such as the function name and resource requirements.

Lambda functions are “stateless,” with no affinity to the underlying infrastructure, so, Lambda can rapidly launch as many copies of the function as needed to scale to the rate of incoming events.

Google Cloud Functions is a serverless execution environment for building and connecting cloud services. It’s a suite of cloud computing services that run on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. With Cloud Functions, you write simple, single-purpose functions that are attached to events emitted from your cloud infrastructure and services. Your function is triggered when an event being watched is fired. Your code executes in a fully managed environment.

AWS Lambda vs Google Cloud Functions

• When using AWS Lambda, you are responsible only for your code. AWS Lambda manages the computer fleet that offers a balance of memory, CPU, network, and other resources.

• Cloud Functions provides a connective layer of logic that lets you write code to connect and extend cloud services.

• AWS Lambda is an ideal computing platform for many application scenarios.

• Google Cloud Functions is a lightweight computer solution for developers to create single-purpose, stand-alone functions that respond to Cloud events without the need to manage a server or runtime environment.

• In Google Cloud, there is no need to provision an infrastructure or worry about managing any servers.

• In AWS Lambda, there are tools for authoring code, such as the AWS Lambda console, Eclipse IDE, and Visual Studio IDE.

• Cloud Functions removes the work of managing servers, configuring software, updating frameworks, and patching operating systems.

• AWS Lambda runtime provides some of the libraries and you can upload any additional libraries that you use.

• Cloud Functions can be written using JavaScript (Node.js), Python, or Go runtimes on Google Cloud Platform. AWS Lambda functions can be written using Node.js, Java, C#, Python, Go, or Powershell.

• On-demand nature of Cloud Functions also makes it a perfect candidate for lightweight APIs and webhooks.

• Amazon was the first cloud provider to offer a full serverless platform with Lambda.

• Google is also one of six members which have sole access to a pair of 100Gb/s x 100 wavelengths optical transmission strands between Oregon and Japan.

• The max. execution time of AWS Lambda is 300 seconds (5 minutes). And Google Cloud offers 540 seconds (9 minutes).

• AWS Lambda is completely and transparently handled by the system, which means that you don’t know how many instances or machines your functions are running on at a given time. You can monitor the usage of your Lambda functions anytime, but your visibility of the underlying architecture is limited.

• Google Cloud Functions explicitly creates a set of instances in the cloud. In this way, you can always check the number of machines created and monitor the load of your cluster.

• As far as dependency management and deployment, the only real weakness of AWS Lambda is its Deployment Packages. You can use external dependencies only by including them within your zipped source code.

• Google Cloud Function has fewer steps than AWS Lambda but AWS Lambda can deploy and update the function faster than Google Cloud Function can do.

• Memory setting form of AWS Lambda is a slider which provides more options to the users to select their desired size between 128 MB to 3008 MB. On Google Cloud Function, memory setting form is just a dropdown selector which limits the available options to 128 MB, 256 MB, 512 MB, 1 GB, and 2 GB.

• Google Cloud Functions allows you to define a simple package.json file to declare and version your npm dependencies.

• There are a lot of steps on AWS Lambda while Google Cloud Function has only one step to deploy the function. It is because AWS Lambda provides many options which the users can configure their function.

• The Lambda free tier includes 1m free requests per month and 400,000 GB-seconds of compute time per month. After this, it’s $0.20 per 1m requests, and $0.00001667 for every GB-second used.

• Google cloud functions pricing is based on the number of requests to your functions and computes resources consumption, rounded to the nearest 100 milliseconds, and of course, only while your code is running. The free tier includes 400,000 GB-seconds, and 200,000 GHz-seconds of compute time.

 

AWS Lambda and Google Cloud Functions are not directly compatible with each other. Both AWS Lambda and Google Cloud Functions support the event-driven approach. This means that you can trigger a function whenever something interesting happens within the cloud environment. They also support a simple HTTP approach.